refactor(ws): rename agent_ids to scout_ids in device_hello frame

WsDeviceHello.agent_ids → scout_ids in Pydantic schema,
device_ws.py handler, and all test fixtures (test_device_ws,
test_ws_unified, test_memory_middleware). Also fixes stale
CloudAgentConfig reference in gmail.py docstring.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

.env.example

@@ -21,6 +21,8 @@ OPENAI_API_KEY=
 ANTHROPIC_API_KEY=
 GOOGLE_API_KEY=
 CEREBRAS_API_KEY=
+GROQ_API_KEY=
+DEEPSEEK_API_KEY=
 
 # Default model used by any agent that does not have a specific override below.
 LLM_MODEL=gpt-5-mini
@@ -50,9 +52,32 @@ LLM_MODEL_UNIFIED_PROCESSOR=
 # Cloud-processor — fetches and processes data from cloud connectors.
 LLM_MODEL_CLOUD_PROCESSOR=
 
+# Brief-agent — produces home and project text briefs.
+# A small model (e.g. gpt-4o-mini) is sufficient.
+# LLM_MODEL_BRIEF_AGENT=
+
+# Task-brief-agent — per-task deep research (Stage 1 executive assistant).
+# Needs tool-use + reasoning; a capable model recommended (e.g. gpt-4o, gemini-2.5-flash).
+# LLM_MODEL_TASK_BRIEF_AGENT=
+
 # Setup-agent — guided journey to build an AgentConfig via WebSocket chat.
 LLM_MODEL_SETUP_AGENT=
 
+# Memory-extractor — Mem0-style extract/decide pipeline (Phase 2).
+# Defaults to gpt-4o-mini when empty (fast + cheap, temperature=0).
+LLM_MODEL_MEMORY_EXTRACTOR=
+
+# Memory-miner — proactive pattern mining from episodic history (Phase 5, Power+ only).
+# Defaults to gpt-4o-mini when empty.
+LLM_MODEL_MEMORY_MINER=
+
+# Memory-auditor — weekly contradiction scan + relation label canonicalization (Phase 7).
+# Defaults to LLM_MODEL when empty (a reasoning-capable model is recommended).
+LLM_MODEL_MEMORY_AUDITOR=
+
+# Scheduler — set to false to disable memory cron jobs (automatically false in tests).
+SCHEDULER_ENABLED=true
+
 # ── Stripe (leave empty to stub billing) ──────────────────────────────────────
 STRIPE_SECRET_KEY=
 STRIPE_WEBHOOK_SECRET=

.gitignore

@@ -28,6 +28,9 @@ tests/fixtures/private*/
 
 # OS
 .DS_Store
+
+# Smoke scripts (dev-only, not for CI)
+scripts/smoke_*.py
 Thumbs.db
 
 # Claude Code

README.md

@@ -0,0 +1,5 @@
+## DEV
+Run in DEV with command:
+```
+uvicorn app.main:app --host 0.0.0.0 --port 8000 --reload --log-config logging.conf
+```

alembic/versions/005_associative_pgvector.py

@@ -0,0 +1,54 @@
+"""Phase 1 — confirm pgvector activation on memory_associative.
+
+Migration 004 created the embedding column as vector(1536) and added the
+IVFFlat index.  This migration is the Phase-1 checkpoint:
+  1. Ensures the pgvector extension is enabled (idempotent).
+  2. Ensures the canonical Phase-1 IVFFlat index exists under the name
+     memory_associative_embedding_idx (creates it only if absent).
+
+Revision ID: 005
+Revises: 9a1f2d0b6c7e
+Create Date: 2026-04-15
+"""
+
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+from alembic import op
+
+revision: str = "005"
+down_revision: Union[str, None] = "e04100e88ace"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Ensure pgvector extension is enabled (also done in 004, idempotent).
+    op.execute("CREATE EXTENSION IF NOT EXISTS vector;")
+
+    # Ensure the canonical Phase-1 IVFFlat index exists.
+    # 004 may have created ix_memory_associative_embedding; this adds the
+    # Phase-1 name memory_associative_embedding_idx if it is missing.
+    op.execute(
+        """
+        DO $$
+        BEGIN
+            IF NOT EXISTS (
+                SELECT 1
+                FROM   pg_indexes
+                WHERE  tablename  = 'memory_associative'
+                  AND  indexname  = 'memory_associative_embedding_idx'
+            ) THEN
+                CREATE INDEX memory_associative_embedding_idx
+                ON memory_associative
+                USING ivfflat (embedding vector_cosine_ops)
+                WITH  (lists = 100);
+            END IF;
+        END $$;
+        """
+    )
+
+
+def downgrade() -> None:
+    op.execute("DROP INDEX IF EXISTS memory_associative_embedding_idx;")

alembic/versions/006_memory_relations.py

@@ -0,0 +1,74 @@
+"""Add memory_relations table (Phase 3 — relational tier).
+
+Revision ID: 006
+Revises: 1f5975a4f3f4
+Create Date: 2026-04-16
+"""
+
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects import postgresql
+
+revision: str = "006"
+down_revision: Union[str, None] = "1f5975a4f3f4"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        "memory_relations",
+        sa.Column("id", postgresql.UUID(as_uuid=False), primary_key=True),
+        sa.Column(
+            "user_id",
+            postgresql.UUID(as_uuid=False),
+            sa.ForeignKey("users.id", ondelete="CASCADE"),
+            nullable=False,
+        ),
+        sa.Column("subject_label", sa.String(128), nullable=False),
+        sa.Column("subject_type", sa.String(32), nullable=False),
+        sa.Column("predicate", sa.String(64), nullable=False),
+        sa.Column("object_label", sa.String(128), nullable=False),
+        sa.Column("object_type", sa.String(32), nullable=False),
+        sa.Column("confidence", sa.Float, nullable=False, server_default="0.7"),
+        sa.Column(
+            "source_episode_id",
+            postgresql.UUID(as_uuid=False),
+            sa.ForeignKey("memory_episodic.id", ondelete="SET NULL"),
+            nullable=True,
+        ),
+        sa.Column("notes_encrypted", sa.LargeBinary, nullable=True),
+        sa.Column(
+            "created_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column(
+            "updated_at",
+            sa.DateTime(timezone=True),
+            nullable=False,
+            server_default=sa.func.now(),
+        ),
+        sa.Column("last_confirmed_at", sa.DateTime(timezone=True), nullable=True),
+    )
+    op.create_index(
+        "memory_relations_user_subject_idx",
+        "memory_relations",
+        ["user_id", "subject_label"],
+    )
+    op.create_index(
+        "memory_relations_user_predicate_idx",
+        "memory_relations",
+        ["user_id", "predicate"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("memory_relations_user_predicate_idx", "memory_relations")
+    op.drop_index("memory_relations_user_subject_idx", "memory_relations")
+    op.drop_table("memory_relations")

alembic/versions/007_rename_agents_to_scouts.py

@@ -0,0 +1,41 @@
+"""Rename agents to scouts.
+
+Revision ID: 007
+Revises: d6e3f4a5b6c7
+Create Date: 2026-05-15
+
+Renames the entire agents subsystem identifiers to scouts.
+Pre-1.0 — no data preservation concerns beyond ALTER TABLE rename.
+"""
+
+from typing import Sequence, Union
+
+from alembic import op
+
+
+revision: str = "007"
+down_revision: Union[str, None] = "d6e3f4a5b6c7"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    # Tables
+    op.rename_table("local_agent_configs", "local_scout_configs")
+    op.rename_table("cloud_agent_configs", "cloud_scout_configs")
+    op.rename_table("agent_run_logs", "scout_run_logs")
+
+    # Columns
+    op.alter_column("local_scout_configs", "agent_config", new_column_name="scout_config")
+    op.alter_column("scout_run_logs", "agent_id", new_column_name="scout_id")
+    op.alter_column("scout_run_logs", "agent_type", new_column_name="scout_type")
+
+
+def downgrade() -> None:
+    op.alter_column("scout_run_logs", "scout_type", new_column_name="agent_type")
+    op.alter_column("scout_run_logs", "scout_id", new_column_name="agent_id")
+    op.alter_column("local_scout_configs", "scout_config", new_column_name="agent_config")
+
+    op.rename_table("scout_run_logs", "agent_run_logs")
+    op.rename_table("cloud_scout_configs", "cloud_agent_configs")
+    op.rename_table("local_scout_configs", "local_agent_configs")

alembic/versions/1f5975a4f3f4_add_extraction_queue.py

@@ -0,0 +1,38 @@
+"""add extraction_queue
+
+Revision ID: 1f5975a4f3f4
+Revises: 005
+Create Date: 2026-04-16 17:26:25.790870
+
+"""
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+from alembic import op
+import sqlalchemy as sa
+
+
+# revision identifiers, used by Alembic.
+revision: str = '1f5975a4f3f4'
+down_revision: Union[str, None] = '005'
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.create_table(
+        'extraction_queue',
+        sa.Column('id', sa.Uuid(as_uuid=False), nullable=False),
+        sa.Column('user_id', sa.Uuid(as_uuid=False), nullable=False),
+        sa.Column('episode_id', sa.Uuid(as_uuid=False), nullable=True),
+        sa.Column('created_at', sa.DateTime(timezone=True), server_default=sa.text('now()'), nullable=False),
+        sa.ForeignKeyConstraint(['user_id'], ['users.id'], ondelete='CASCADE'),
+        sa.PrimaryKeyConstraint('id'),
+    )
+    op.create_index(op.f('ix_extraction_queue_user_id'), 'extraction_queue', ['user_id'], unique=False)
+
+
+def downgrade() -> None:
+    op.drop_index(op.f('ix_extraction_queue_user_id'), table_name='extraction_queue')
+    op.drop_table('extraction_queue')

alembic/versions/d6e3f4a5b6c7_folder_index_tables.py

@@ -0,0 +1,46 @@
+"""Add token tracking columns for folder integration.
+
+Revision ID: d6e3f4a5b6c7
+Revises: 006
+Create Date: 2026-05-11 00:00:00.000000
+
+"""
+from __future__ import annotations
+
+from typing import Sequence, Union
+
+import sqlalchemy as sa
+from alembic import op
+from sqlalchemy.dialects.postgresql import UUID
+
+# revision identifiers, used by Alembic.
+revision: str = "d6e3f4a5b6c7"
+down_revision: Union[str, None] = "006"
+branch_labels: Union[str, Sequence[str], None] = None
+depends_on: Union[str, Sequence[str], None] = None
+
+
+def upgrade() -> None:
+    op.add_column(
+        "agent_run_logs",
+        sa.Column("tokens_used", sa.Integer(), nullable=False, server_default="0"),
+    )
+    op.create_table(
+        "monthly_token_usage",
+        sa.Column("user_id", UUID(as_uuid=False), sa.ForeignKey("users.id", ondelete="CASCADE"), nullable=False),
+        sa.Column("year_month", sa.String(7), nullable=False),
+        sa.Column("feature", sa.String(64), nullable=False),
+        sa.Column("tokens_used", sa.Integer(), nullable=False, server_default="0"),
+        sa.PrimaryKeyConstraint("user_id", "year_month", "feature"),
+    )
+    op.create_index(
+        "ix_monthly_token_usage_user_month",
+        "monthly_token_usage",
+        ["user_id", "year_month"],
+    )
+
+
+def downgrade() -> None:
+    op.drop_index("ix_monthly_token_usage_user_month", table_name="monthly_token_usage")
+    op.drop_table("monthly_token_usage")
+    op.drop_column("agent_run_logs", "tokens_used")

app/agents/client_agent.py

@@ -0,0 +1,52 @@
+"""Client agent — read-only tools for the clients table."""
+
+from __future__ import annotations
+
+import json
+from typing import Any
+
+from langchain_core.tools import tool
+
+from app.core.ws_context import execute_on_client
+
+
+@tool
+async def list_clients(search: str = "", limit: int = 20) -> str:
+    """List clients, optionally filtered by a name/email substring search.
+
+    search: optional substring to match against client name or email.
+    limit: max rows to return (default 20).
+    """
+    filters: dict[str, Any] = {"limit": limit}
+    if search:
+        filters["search"] = search
+
+    result = await execute_on_client(action="select", table="clients", filters=filters)
+    rows = result.get("rows", [])
+    if not rows:
+        return "No clients found."
+    lines = [
+        f"- {r.get('name', '?')} (id: {r.get('id')}, email: {r.get('email', '')}, "
+        f"company: {r.get('company', '')})"
+        for r in rows
+    ]
+    return f"Found {len(rows)} client(s):\n" + "\n".join(lines)
+
+
+@tool
+async def get_client(id: str) -> str:
+    """Get full details for one client by UUID.
+
+    id: the client's UUID.
+    """
+    if not id:
+        return "Client id is required."
+
+    result = await execute_on_client(action="get", table="clients", data={"id": id})
+    row = result.get("row") or result.get("rows", [None])[0] if result else None
+    if not row:
+        return f"Client '{id}' not found."
+    return f"Client details:\n{json.dumps(row, ensure_ascii=False, indent=2)}"
+
+
+CLIENT_TOOLS: list[Any] = [list_clients, get_client]

app/agents/folder_agent.py

@@ -0,0 +1,168 @@
+"""Scoped file-read and search tools for the project folder feature."""
+from __future__ import annotations
+
+from langchain_core.tools import tool
+
+from app.core.folder_indexer import _extract_docx_text, _extract_pdf_text
+from app.core.ws_context import execute_on_client
+
+# Cap returned slice size to keep tool output under control.
+_MAX_RETURN_CHARS = 50_000
+_MAX_SEARCH_MATCHES = 20
+
+
+def _is_unsafe_path(rel: str) -> bool:
+    if not rel:
+        return True
+    norm = rel.replace("\\", "/")
+    if norm.startswith("/"):
+        return True
+    # Windows drive letter
+    if len(rel) >= 2 and rel[1] == ":":
+        return True
+    parts = norm.split("/")
+    return ".." in parts
+
+
+async def _fetch_file(project_id: str, relative_path: str, offset: int, length: int) -> dict:
+    """Return the raw Electron tool_result dict for a file read."""
+    return await execute_on_client(
+        action="read_project_folder_file",
+        data={
+            "projectId": project_id,
+            "relativePath": relative_path,
+            "offset": offset,
+            "length": length,
+        },
+    )
+
+
+def _decode(result: dict) -> tuple[str, str, int]:
+    """Decode a tool_result into (text, kind, total_size). For pdf/docx,
+    extracts text from base64. For images, returns a placeholder string.
+    For text, content is already a sliced utf-8 string.
+    """
+    kind = result.get("kind", "text")
+    content = result.get("content", "") or ""
+    total = int(result.get("totalSize", 0) or 0)
+    if kind == "image":
+        return ("[Image file — cannot be navigated as text. See manifest summary.]", kind, total)
+    if kind == "pdf":
+        return (_extract_pdf_text(content), kind, total)
+    if kind == "docx":
+        return (_extract_docx_text(content), kind, total)
+    return (content, kind, total)
+
+
+@tool
+async def read_project_folder_file(
+    project_id: str,
+    relative_path: str,
+    offset: int = 0,
+    length: int = _MAX_RETURN_CHARS,
+) -> str:
+    """Read a slice of a file inside the project's linked folder.
+
+    Args:
+        project_id: project ID.
+        relative_path: path relative to the linked folder root.
+        offset: char offset to start reading from (0 = beginning).
+        length: max chars to return. Default 50000. Use smaller values to save tokens.
+
+    Returns text content slice with a header showing position. Header tells you
+    when more content is available; call again with the suggested next offset.
+
+    For PDF / DOCX files the backend extracts text first, then applies offset/length
+    on the extracted text. For images returns a placeholder; navigate with the
+    manifest summary instead.
+    """
+    if _is_unsafe_path(relative_path):
+        return "Access denied"
+
+    result = await _fetch_file(project_id, relative_path, offset, length)
+    text, kind, total_size = _decode(result)
+
+    if not text and kind in ("missing", "error"):
+        return f"File not found or unreadable: {relative_path}"
+
+    if kind in ("pdf", "docx"):
+        # Backend extracted full text — apply offset/length on chars.
+        sliced = text[offset:offset + length]
+        slice_end = min(offset + length, len(text))
+        header = (
+            f"[file={relative_path} kind={kind} offset={offset} end={slice_end} "
+            f"totalChars={len(text)}]"
+        )
+        if slice_end < len(text):
+            header += f"\n[More content available — call again with offset={slice_end}.]"
+        return header + "\n" + sliced
+
+    if kind == "text":
+        slice_end = offset + len(text)
+        header = (
+            f"[file={relative_path} kind=text offset={offset} end={slice_end} "
+            f"totalBytes={total_size}]"
+        )
+        if slice_end < total_size:
+            header += f"\n[More content available — call again with offset={slice_end}.]"
+        return header + "\n" + text
+
+    # image or unknown
+    return text
+
+
+@tool
+async def search_project_folder_file(
+    project_id: str,
+    relative_path: str,
+    query: str,
+    context_lines: int = 3,
+) -> str:
+    """Search a project folder file for a query string (case-insensitive substring).
+
+    Args:
+        project_id: project ID.
+        relative_path: path relative to the linked folder root.
+        query: text to search for.
+        context_lines: number of lines of context around each match (default 3).
+
+    Returns matching line ranges with surrounding context and 1-based line numbers.
+    Capped at 20 matches; if more exist the header shows the total.
+
+    Works on text, code, markdown, PDF (extracted), and DOCX (extracted).
+    Images and binary files are not searchable.
+    """
+    if _is_unsafe_path(relative_path):
+        return "Access denied"
+    if not query:
+        return "Empty query."
+
+    # For text we still need full file; pass length=very large.
+    result = await _fetch_file(project_id, relative_path, offset=0, length=10_000_000)
+    text, kind, _ = _decode(result)
+
+    if not text and kind in ("missing", "error"):
+        return f"File not found or unreadable: {relative_path}"
+    if kind == "image":
+        return "Cannot search inside images."
+
+    lines = text.splitlines()
+    q = query.lower()
+    matches = [i for i, line in enumerate(lines) if q in line.lower()]
+    if not matches:
+        return f"No matches for '{query}' in {relative_path}."
+
+    shown = matches[:_MAX_SEARCH_MATCHES]
+    snippets: list[str] = []
+    for i in shown:
+        start = max(0, i - context_lines)
+        end = min(len(lines), i + context_lines + 1)
+        block = "\n".join(f"{n + 1:5d}: {lines[n]}" for n in range(start, end))
+        snippets.append(block)
+
+    header = f"[file={relative_path} matches={len(matches)} showing={len(shown)} query='{query}']"
+    body = "\n---\n".join(snippets)
+    return header + "\n" + body
+
+
+FOLDER_TOOLS = [read_project_folder_file, search_project_folder_file]

app/agents/note_agent.py

@@ -1,13 +1,14 @@
-"""Note agent — Markdown note management (list, get, create, update, delete)."""
+"""Note agent — Markdown note management (list, get, create, update, propose edit)."""
 
 from __future__ import annotations
 
+import asyncio
 import re
 from typing import Any
 
 from langchain_core.tools import tool
 
-from app.core.llm import embed
+from app.core.note_summarizer import generate_note_summary
 from app.core.ws_context import execute_on_client
 
 _UUID_RE = re.compile(
@@ -19,9 +20,21 @@ def _is_uuid(value: str) -> bool:
     return bool(_UUID_RE.match(value))
 
 
+def _fmt_summary(row: dict) -> str:
+    summary = (row.get("aiSummary") or row.get("ai_summary") or "").strip()
+    if summary:
+        return f" — {summary}"
+    snippet = (row.get("content") or "")[:120].replace("\n", " ").strip()
+    return f" — {snippet}" if snippet else ""
+
+
 @tool
 async def list_notes(project_id: str = "") -> str:
-    """List notes, optionally scoped to a project by project_id."""
+    """List notes with AI summaries, optionally scoped to a project by project_id.
+
+    Returns id, title, and ai_summary for each note so you can decide which
+    note to read in full with get_note before creating or updating.
+    """
     normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
     result = await execute_on_client(
         action="select",
@@ -31,7 +44,7 @@ async def list_notes(project_id: str = "") -> str:
     rows = result.get("rows", [])
     if not rows:
         return "No notes found."
-    lines = [f"- {r['title']} (id: {r['id']})" for r in rows]
+    lines = [f"  - [{r['id']}] {r['title']}{_fmt_summary(r)}" for r in rows]
     return f"Found {len(rows)} note(s):\n" + "\n".join(lines)
 
 
@@ -66,14 +79,10 @@ async def create_note(
         },
     )
     row = result["row"]
-    # Index the note content in the vector store.
+    note_id: str = row["id"]
-    vector = await embed(content)
+    # Generate summary asynchronously — fire-and-forget.
-    await execute_on_client(
+    asyncio.create_task(_refresh_summary(note_id, title, content))
-        action="vector_upsert",
+    return f"Note created: '{row['title']}' (id: {note_id})."
-        data={"id": row["id"], "projectId": row.get("projectId"), "content": content},
-        vector=vector,
-    )
-    return f"Note created: '{row['title']}' (id: {row['id']})."
 
 
 @tool
@@ -82,7 +91,8 @@ async def update_note(
     title: str = "",
     content: str = "",
 ) -> str:
-    """Update an existing note. Only pass fields that should change.
+    """Update an existing note directly (no approval required).
+    Use propose_note_edit instead when human review is needed.
     note_id: UUID of the note (required)
     If you need to preserve existing content, call get_note first.
     """
@@ -97,17 +107,63 @@ async def update_note(
         data={"id": note_id, "updates": updates},
     )
     row = result["row"]
-    # Re-index if content changed.
     if content:
-        vector = await embed(content)
+        new_title = title or row.get("title", "")
-        await execute_on_client(
+        asyncio.create_task(_refresh_summary(note_id, new_title, content))
-            action="vector_upsert",
-            data={"id": note_id, "projectId": row.get("projectId"), "content": content},
-            vector=vector,
-        )
     return f"Note updated: '{row['title']}' (id: {row['id']})."
 
 
+@tool
+async def propose_note_edit(
+    note_id: str,
+    edit_type: str,
+    proposed_content: str,
+    reasoning: str = "",
+    anchor_before: str = "",
+    anchor_text: str = "",
+    agent_id: str = "",
+    run_id: str = "",
+) -> str:
+    """Propose an AI edit to an existing note, pending human approval.
+
+    Use this instead of update_note when review_required is true.
+    The user will see the proposal highlighted before it is merged.
+
+    note_id: UUID of the target note (required)
+    edit_type: 'append' | 'insert' | 'replace'
+      - append: adds proposed_content at the end of the note
+      - insert: inserts proposed_content immediately after anchor_before text
+      - replace: replaces the first occurrence of anchor_text with proposed_content
+    proposed_content: the new Markdown text to add or substitute (required)
+    reasoning: brief explanation shown to the user (recommended)
+    anchor_before: for 'insert' — the text snippet that precedes the insertion point
+    anchor_text: for 'replace' — the exact text to be replaced
+    agent_id: agent identifier (for traceability)
+    run_id: run identifier (for traceability)
+    """
+    if edit_type not in ("append", "insert", "replace"):
+        return f"Invalid edit_type '{edit_type}'. Use 'append', 'insert', or 'replace'."
+
+    result = await execute_on_client(
+        action="propose_note_edit",
+        data={
+            "noteId": note_id,
+            "type": edit_type,
+            "proposedContent": proposed_content,
+            "reasoning": reasoning or None,
+            "anchorBefore": anchor_before or None,
+            "anchorText": anchor_text or None,
+            "agentId": agent_id or None,
+            "runId": run_id or None,
+        },
+    )
+    edit_id = result.get("id", "?")
+    return (
+        f"Edit proposal created (id: {edit_id}) for note {note_id}. "
+        f"Status: pending user approval."
+    )
+
+
 @tool
 async def delete_note(note_id: str) -> str:
     """Delete a note permanently by its UUID."""
@@ -115,10 +171,36 @@ async def delete_note(note_id: str) -> str:
     return f"Note {note_id} deleted."
 
 
+async def _refresh_summary(note_id: str, title: str, content: str) -> None:
+    """Generate and persist the AI summary for a note.  Fire-and-forget."""
+    try:
+        summary = await generate_note_summary(title, content)
+        if summary:
+            await execute_on_client(
+                action="update",
+                table="notes",
+                data={
+                    "id": note_id,
+                    "updates": {
+                        "aiSummary": summary,
+                        "aiSummaryUpdatedAt": int(__import__("time").time() * 1000),
+                    },
+                },
+            )
+    except Exception:
+        pass  # fire-and-forget; errors logged by generate_note_summary
+
+
 NOTE_TOOLS: list[Any] = [
     list_notes,
     get_note,
     create_note,
     update_note,
+    propose_note_edit,
     delete_note,
 ]
+
+NOTE_READ_TOOLS: list[Any] = [
+    list_notes,
+    get_note,
+]

app/agents/project_agent.py

@@ -125,3 +125,9 @@ PROJECT_TOOLS: list[Any] = [
     update_project,
     delete_project,
 ]
+
+PROJECT_READ_TOOLS: list[Any] = [
+    list_projects,
+    list_all_projects,
+    get_project,
+]

app/agents/relations_agent.py

@@ -0,0 +1,63 @@
+"""Relations agent — read-only tool wrapping MemoryMiddleware.query_relations."""
+
+from __future__ import annotations
+
+from typing import Any
+
+from langchain_core.tools import tool
+
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import async_session
+
+# Injected at tool-factory time by _brief_research_tools(); not a module-level global.
+# Each tool closure captures the user_id bound at factory time.
+
+
+def make_query_relations_tool(user_id: str, trace_id: str | None = None) -> Any:
+    """Return a query_relations tool bound to *user_id*."""
+
+    @tool
+    async def query_relations(
+        subject_label: str = "",
+        predicate: str = "",
+        object_label: str = "",
+        limit: int = 10,
+    ) -> str:
+        """Query the relational memory graph for entity relationships.
+
+        Returns rows where subject ↔ predicate ↔ object match the given filters.
+        All parameters are optional — omit to retrieve all relations up to limit.
+
+        subject_label: entity label on the left side (e.g. a client name, "Acme Corp").
+        predicate: relationship type (e.g. "mentioned_in", "works_at", "related_to").
+        object_label: entity label on the right side (e.g. a project name, "Website Redesign").
+        limit: max rows to return (default 10).
+        """
+        import logging
+        logger = logging.getLogger(__name__)
+        logger.info(
+            "relations_agent: query_relations trace=%s user=%s subject=%r predicate=%r object=%r",
+            trace_id or "-", user_id, subject_label, predicate, object_label,
+        )
+
+        async with async_session() as db:
+            memory = MemoryMiddleware(db)
+            rows = await memory.query_relations(
+                user_id=user_id,
+                subject=subject_label or None,
+                predicate=predicate or None,
+                object_=object_label or None,
+                limit=limit,
+            )
+
+        if not rows:
+            return "No relational memory entries found for the given filters."
+
+        lines = [
+            f"- {r.subject_label} —[{r.predicate}]→ {r.object_label}"
+            + (f" (confidence: {r.confidence:.2f})" if r.confidence is not None else "")
+            for r in rows
+        ]
+        return f"Found {len(rows)} relation(s):\n" + "\n".join(lines)
+
+    return query_relations

app/agents/task_agent.py

@@ -26,32 +26,141 @@ def _is_uuid(value: str) -> bool:
 async def list_tasks(
     project_id: str = "",
     status: str = "",
+    priority: str = "",
+    assignee: str = "",
     search: str = "",
     order_by: str = "",
+    order_dir: str = "",
+    due_date_from: int = -1,
+    due_date_to: int = -1,
+    created_at_from: int = -1,
+    created_at_to: int = -1,
+    completed_at_from: int = -1,
+    completed_at_to: int = -1,
+    is_ai_suggested: int = -1,
+    limit: int = 50,
+    offset: int = 0,
 ) -> str:
-    """List tasks, optionally filtered by project_id, status (todo|in_progress|done),
+    """List tasks with optional filters. Returns up to `limit` results (default 50).
-    a search string, or an order_by field name (dueDate|priority|createdAt)."""
+
+    project_id: UUID of the project to scope results to.
+    status: filter by status — todo | in_progress | done.
+    priority: filter by priority — high | medium | low.
+    assignee: substring to match against assignee names. OMIT unless the user explicitly
+              names a person or refers to themselves ("my tasks", "assigned to me", "mine").
+              Do NOT default to the current user.
+    search: substring search across title and description.
+    order_by: sort field — dueDate | priority | createdAt | completedAt.
+    order_dir: asc (default) | desc.
+    due_date_from / due_date_to: ms epoch range for dueDate. Use -1 to omit.
+    created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
+    completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
+    is_ai_suggested: 0 or 1 to filter by AI-suggested flag; -1 = any.
+    limit: max rows to return (default 50). Use with offset to paginate.
+    offset: skip first N rows (default 0).
+
+    Tip — combine *_from and *_to for a closed range; pass only one for open-ended.
+    Tip — prefer count_tasks for "how many" questions to avoid listing rows.
+    Tip — for natural-language windows ("today", "tomorrow", "this week", "last month", etc.)
+    take due_date_from / due_date_to verbatim from the DATE CONTEXT block in the system prompt;
+    do not compute boundaries from the current UTC instant.
+    """
     normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
-    result = await execute_on_client(
+    filters: dict[str, Any] = {
-        action="select",
+        "projectId": normalized_project_id or None,
-        table="tasks",
+        "status": status or None,
-        filters={
+        "priority": priority or None,
-            "projectId": normalized_project_id or None,
+        "search": search or None,
-            "status": status or None,
+        "orderBy": order_by or None,
-            "search": search or None,
+        "orderDir": order_dir or None,
-            "orderBy": order_by or None,
+        "limit": limit,
-        },
+        "offset": offset,
-    )
+    }
+    if assignee:
+        filters["assignee"] = assignee
+    if due_date_from != -1:
+        filters["dueDateFrom"] = due_date_from
+    if due_date_to != -1:
+        filters["dueDateTo"] = due_date_to
+    if created_at_from != -1:
+        filters["createdAtFrom"] = created_at_from
+    if created_at_to != -1:
+        filters["createdAtTo"] = created_at_to
+    if completed_at_from != -1:
+        filters["completedAtFrom"] = completed_at_from
+    if completed_at_to != -1:
+        filters["completedAtTo"] = completed_at_to
+    if is_ai_suggested != -1:
+        filters["isAiSuggested"] = is_ai_suggested
+
+    result = await execute_on_client(action="select", table="tasks", filters=filters)
     rows = result.get("rows", [])
     if not rows:
         return "No tasks found matching the given filters."
     lines = [
-        f"- {r['title']} (status: {r['status']}, priority: {r['priority']}, id: {r['id']})"
+        f"- {r['title']} (status: {r['status']}, priority: {r['priority']}, "
+        f"dueDate: {r.get('dueDate')}, completedAt: {r.get('completedAt')}, "
+        f"projectId: {r.get('projectId')}, id: {r['id']})"
         for r in rows
     ]
     return f"Found {len(rows)} task(s):\n" + "\n".join(lines)
 
 
+@tool
+async def count_tasks(
+    project_id: str = "",
+    status: str = "",
+    priority: str = "",
+    assignee: str = "",
+    search: str = "",
+    due_date_from: int = -1,
+    due_date_to: int = -1,
+    created_at_from: int = -1,
+    created_at_to: int = -1,
+    completed_at_from: int = -1,
+    completed_at_to: int = -1,
+    is_ai_suggested: int = -1,
+) -> str:
+    """Count tasks matching the given filters without returning rows.
+
+    Use this instead of list_tasks for "how many" questions — it is much cheaper.
+    Same filter parameters as list_tasks (no limit/offset/order_by needed).
+    assignee: OMIT unless the user explicitly names a person or refers to themselves
+              ("my tasks"). Do NOT default to the current user.
+    due_date_from / due_date_to: ms epoch range for dueDate. Use -1 to omit.
+    created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
+    completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
+    Tip — for natural-language windows take due_date_from / due_date_to from the DATE CONTEXT block;
+    do not compute boundaries from the current UTC instant.
+    """
+    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
+    filters: dict[str, Any] = {
+        "projectId": normalized_project_id or None,
+        "status": status or None,
+        "priority": priority or None,
+        "search": search or None,
+    }
+    if assignee:
+        filters["assignee"] = assignee
+    if due_date_from != -1:
+        filters["dueDateFrom"] = due_date_from
+    if due_date_to != -1:
+        filters["dueDateTo"] = due_date_to
+    if created_at_from != -1:
+        filters["createdAtFrom"] = created_at_from
+    if created_at_to != -1:
+        filters["createdAtTo"] = created_at_to
+    if completed_at_from != -1:
+        filters["completedAtFrom"] = completed_at_from
+    if completed_at_to != -1:
+        filters["completedAtTo"] = completed_at_to
+    if is_ai_suggested != -1:
+        filters["isAiSuggested"] = is_ai_suggested
+
+    result = await execute_on_client(action="count", table="tasks", filters=filters)
+    return f"Task count: {result.get('count', 0)}"
+
+
 @tool
 async def create_task(
     title: str,
@@ -72,6 +181,8 @@ async def create_task(
     due_date: Unix timestamp in milliseconds; 0 means no due date
     project_id: optional UUID of the parent project
     is_ai_suggested: 1 if proactively suggested, 0 if user-requested
+
+    completedAt is set automatically when status is 'done'.
     """
     result = await execute_on_client(
         action="insert",
@@ -90,7 +201,7 @@ async def create_task(
     row = result["row"]
     return (
         f"Task created: '{row['title']}' "
-        f"(id: {row['id']}, status: {row['status']}, priority: {row['priority']})"
+        f"(id: {row['id']}, status: {row['status']}, priority: {row['priority']}, projectId: {row.get('projectId')})"
     )
 
 
@@ -108,6 +219,10 @@ async def update_task(
     """Update fields on an existing task. Only pass fields you want to change.
     task_id: the task's UUID (required)
     due_date: -1 means unchanged; 0 clears the due date; any positive value sets it
+
+    completedAt is managed automatically:
+      - setting status to 'done' records the current timestamp
+      - changing status away from 'done' clears completedAt
     """
     updates: dict[str, Any] = {}
     if title:
@@ -130,7 +245,7 @@ async def update_task(
         data={"id": task_id, "updates": updates},
     )
     row = result["row"]
-    return f"Task updated: '{row['title']}' (id: {row['id']}, status: {row['status']})"
+    return f"Task updated: '{row['title']}' (id: {row['id']}, status: {row['status']}, projectId: {row.get('projectId')})"
 
 
 @tool
@@ -141,21 +256,36 @@ async def delete_task(task_id: str) -> str:
 
 
 @tool
-async def list_tasks_due_today() -> str:
+async def list_tasks_due_today(user_timezone: str = "UTC", include_done: bool = False) -> str:
-    """List all tasks whose due date falls on today's date."""
+    """List all tasks whose due date falls on today's date.
-    now = datetime.now(tz=timezone.utc)
+
-    start_ms = int(datetime(now.year, now.month, now.day, tzinfo=timezone.utc).timestamp() * 1000)
+    user_timezone: IANA timezone name (e.g. 'Europe/Rome', 'America/New_York').
-    end_ms = start_ms + 86_400_000 - 1  # last ms of today
+    Always pass the user's timezone so 'today' is computed in their local time.
+    include_done: set True to also include already-completed tasks due today (default False).
+    """
+    try:
+        from zoneinfo import ZoneInfo
+        tz = ZoneInfo(user_timezone or "UTC")
+    except Exception:
+        tz = timezone.utc
+    now_local = datetime.now(tz=tz)
+    start_dt = datetime(now_local.year, now_local.month, now_local.day, tzinfo=tz)
+    start_ms = int(start_dt.timestamp() * 1000)
+    end_ms = start_ms + 86_400_000 - 1
+    filters: dict[str, Any] = {"dueDateFrom": start_ms, "dueDateTo": end_ms}
+    if not include_done:
+        filters["status"] = "todo"
     result = await execute_on_client(
         action="select",
         table="tasks",
-        filters={"dueDateFrom": start_ms, "dueDateTo": end_ms},
+        filters=filters,
     )
     rows = result.get("rows", [])
     if not rows:
         return "No tasks are due today."
     lines = [
-        f"- {r['title']} (priority: {r['priority']}, status: {r['status']}, id: {r['id']})"
+        f"- {r['title']} (priority: {r['priority']}, status: {r['status']}, "
+        f"projectId: {r.get('projectId')}, id: {r['id']})"
         for r in rows
     ]
     return f"Tasks due today ({len(rows)}):\n" + "\n".join(lines)
@@ -193,7 +323,6 @@ async def add_task_comment(task_id: str, author: str, content: str) -> str:
     )
     row = result.get("row", {})
     row_author = row.get("author", author)
-    # Electron payloads can vary (taskId vs task_id). Fall back to input task_id.
     row_task_id = row.get("taskId") or row.get("task_id") or task_id
     row_comment_id = row.get("id", "unknown")
     return f"Comment added by {row_author} on task {row_task_id} (comment id: {row_comment_id})."
@@ -211,6 +340,7 @@ async def delete_task_comment(comment_id: str) -> str:
 
 TASK_TOOLS: list[Any] = [
     list_tasks,
+    count_tasks,
     create_task,
     update_task,
     delete_task,
@@ -219,3 +349,10 @@ TASK_TOOLS: list[Any] = [
     add_task_comment,
     delete_task_comment,
 ]
+
+TASK_READ_TOOLS: list[Any] = [
+    list_tasks,
+    count_tasks,
+    list_tasks_due_today,
+    list_task_comments,
+]

app/agents/timeline_agent.py

@@ -3,6 +3,7 @@
 from __future__ import annotations
 
 import re
+from datetime import datetime, timezone
 from typing import Any
 
 from langchain_core.tools import tool
@@ -19,19 +20,128 @@ def _is_uuid(value: str) -> bool:
 
 
 @tool
-async def list_timelines(project_id: str = "") -> str:
+async def list_timelines(
-    """List timelines. Provide project_id to scope to a specific project."""
+    project_id: str = "",
+    type: str = "",
+    is_completed: int = -1,
+    is_ai_suggested: int = -1,
+    order_by: str = "",
+    order_dir: str = "",
+    date_from: int = -1,
+    date_to: int = -1,
+    created_at_from: int = -1,
+    created_at_to: int = -1,
+    completed_at_from: int = -1,
+    completed_at_to: int = -1,
+    limit: int = 50,
+    offset: int = 0,
+) -> str:
+    """List timeline events (milestones, checkpoints, activities) with optional filters.
+
+    project_id: UUID to scope results to a specific project.
+    type: filter by event type — milestone | checkpoint | activity.
+    is_completed: 0 = incomplete only, 1 = completed only, -1 = any (default).
+    is_ai_suggested: 0 or 1 to filter by AI-suggested flag; -1 = any.
+    order_by: sort field — date (default) | createdAt | completedAt.
+    order_dir: asc (default) | desc.
+    date_from / date_to: ms epoch range for the event date. Use -1 to omit.
+    created_at_from / created_at_to: ms epoch range for createdAt. Use -1 to omit.
+    completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
+    limit: max rows to return (default 50). Use with offset to paginate.
+    offset: skip first N rows (default 0).
+
+    Tip — combine *_from and *_to for a closed range; pass only one for open-ended.
+    Tip — prefer count_timelines for "how many" questions to avoid listing rows.
+    Tip — for natural-language windows ("today", "this week", "last month", etc.)
+    take date_from / date_to verbatim from the DATE CONTEXT block in the system prompt;
+    do not compute boundaries from the current UTC instant.
+    """
     normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
-    result = await execute_on_client(
+    filters: dict[str, Any] = {
-        action="select",
+        "projectId": normalized_project_id or None,
-        table="timelines",
+        "orderBy": order_by or None,
-        filters={"projectId": normalized_project_id or None},
+        "orderDir": order_dir or None,
-    )
+        "limit": limit,
+        "offset": offset,
+    }
+    if type:
+        filters["type"] = type
+    if is_completed != -1:
+        filters["isCompleted"] = is_completed
+    if is_ai_suggested != -1:
+        filters["isAiSuggested"] = is_ai_suggested
+    if date_from != -1:
+        filters["dateFrom"] = date_from
+    if date_to != -1:
+        filters["dateTo"] = date_to
+    if created_at_from != -1:
+        filters["createdAtFrom"] = created_at_from
+    if created_at_to != -1:
+        filters["createdAtTo"] = created_at_to
+    if completed_at_from != -1:
+        filters["completedAtFrom"] = completed_at_from
+    if completed_at_to != -1:
+        filters["completedAtTo"] = completed_at_to
+
+    result = await execute_on_client(action="select", table="timelines", filters=filters)
     rows = result.get("rows", [])
     if not rows:
-        return "No timelines found."
+        return "No timeline events found."
-    lines = [f"- {r['title']} (date: {r['date']}, id: {r['id']})" for r in rows]
+    lines = [
-    return f"Found {len(rows)} timeline(s):\n" + "\n".join(lines)
+        f"- {r['title']} (date: {r['date']}, type: {r.get('type')}, "
+        f"completed: {bool(r.get('isCompleted'))}, completedAt: {r.get('completedAt')}, "
+        f"projectId: {r.get('projectId')}, id: {r['id']})"
+        for r in rows
+    ]
+    return f"Found {len(rows)} timeline event(s):\n" + "\n".join(lines)
+
+
+@tool
+async def count_timelines(
+    project_id: str = "",
+    type: str = "",
+    is_completed: int = -1,
+    is_ai_suggested: int = -1,
+    date_from: int = -1,
+    date_to: int = -1,
+    created_at_from: int = -1,
+    created_at_to: int = -1,
+    completed_at_from: int = -1,
+    completed_at_to: int = -1,
+) -> str:
+    """Count timeline events matching the given filters without returning rows.
+
+    Use this instead of list_timelines for "how many" questions — it is much cheaper.
+    Same filter parameters as list_timelines (no limit/offset/order_by needed).
+
+    date_from / date_to: ms epoch range for the event date. Use -1 to omit.
+    completed_at_from / completed_at_to: ms epoch range for completedAt. Use -1 to omit.
+    Tip — for natural-language windows take date_from / date_to from the DATE CONTEXT block;
+    do not compute boundaries from the current UTC instant.
+    """
+    normalized_project_id = project_id if (project_id and _is_uuid(project_id)) else ""
+    filters: dict[str, Any] = {"projectId": normalized_project_id or None}
+    if type:
+        filters["type"] = type
+    if is_completed != -1:
+        filters["isCompleted"] = is_completed
+    if is_ai_suggested != -1:
+        filters["isAiSuggested"] = is_ai_suggested
+    if date_from != -1:
+        filters["dateFrom"] = date_from
+    if date_to != -1:
+        filters["dateTo"] = date_to
+    if created_at_from != -1:
+        filters["createdAtFrom"] = created_at_from
+    if created_at_to != -1:
+        filters["createdAtTo"] = created_at_to
+    if completed_at_from != -1:
+        filters["completedAtFrom"] = completed_at_from
+    if completed_at_to != -1:
+        filters["completedAtTo"] = completed_at_to
+
+    result = await execute_on_client(action="count", table="timelines", filters=filters)
+    return f"Timeline event count: {result.get('count', 0)}"
 
 
 @tool
@@ -39,13 +149,19 @@ async def create_timeline(
     project_id: str,
     title: str,
     date: int,
+    type: str = "milestone",
+    is_completed: int = 0,
     is_ai_suggested: int = 0,
 ) -> str:
-    """Create a project timeline (milestone).
+    """Create a project timeline event.
     project_id: REQUIRED UUID of the parent project
-    title: descriptive name for the milestone
+    title: descriptive name for the event
-    date: Unix timestamp in milliseconds
+    date: Unix timestamp in milliseconds for the event date
+    type: milestone (default) | checkpoint | activity
+    is_completed: 1 if already completed, 0 if not (default 0)
     is_ai_suggested: 1 if proactively suggested, 0 if user-requested
+
+    completedAt is set automatically when is_completed is 1.
     """
     result = await execute_on_client(
         action="insert",
@@ -54,11 +170,13 @@ async def create_timeline(
             "projectId": project_id,
             "title": title,
             "date": date,
+            "type": type,
+            "isCompleted": is_completed,
             "isAiSuggested": is_ai_suggested,
         },
     )
     row = result["row"]
-    return f"Timeline created: '{row['title']}' (id: {row['id']}, date: {row['date']})"
+    return f"Timeline event created: '{row['title']}' (id: {row['id']}, date: {row['date']}, type: {row.get('type')})"
 
 
 @tool
@@ -66,35 +184,87 @@ async def update_timeline(
     timeline_id: str,
     title: str = "",
     date: int = -1,
+    is_completed: int = -1,
 ) -> str:
-    """Update a timeline. Only pass fields that should change.
+    """Update a timeline event. Only pass fields that should change.
-    timeline_id: UUID of the timeline (required)
+    timeline_id: UUID of the event (required)
     date: -1 means unchanged; any other value sets the new date (ms timestamp)
+    is_completed: 0 = mark incomplete, 1 = mark complete, -1 = unchanged
+
+    completedAt is managed automatically:
+      - setting is_completed to 1 records the current timestamp
+      - setting is_completed to 0 clears completedAt
     """
     updates: dict[str, Any] = {}
     if title:
         updates["title"] = title
     if date != -1:
         updates["date"] = date
+    if is_completed != -1:
+        updates["isCompleted"] = is_completed
     result = await execute_on_client(
         action="update",
         table="timelines",
         data={"id": timeline_id, "updates": updates},
     )
     row = result["row"]
-    return f"Timeline updated: '{row['title']}' (id: {row['id']})"
+    return f"Timeline event updated: '{row['title']}' (id: {row['id']})"
 
 
 @tool
 async def delete_timeline(timeline_id: str) -> str:
-    """Delete a timeline permanently by its UUID."""
+    """Delete a timeline event permanently by its UUID."""
     await execute_on_client(action="delete", table="timelines", data={"id": timeline_id})
-    return f"Timeline {timeline_id} deleted."
+    return f"Timeline event {timeline_id} deleted."
+
+
+@tool
+async def list_timelines_today(user_timezone: str = "UTC", include_completed: bool = True) -> str:
+    """List all timeline events whose date falls on today.
+
+    user_timezone: IANA timezone name (e.g. 'Europe/Rome', 'America/New_York').
+    Always pass the user's timezone so 'today' is computed in their local time.
+    include_completed: set False to exclude already-completed events (default True).
+    """
+    try:
+        from zoneinfo import ZoneInfo
+        tz = ZoneInfo(user_timezone or "UTC")
+    except Exception:
+        tz = timezone.utc
+    now_local = datetime.now(tz=tz)
+    start_dt = datetime(now_local.year, now_local.month, now_local.day, tzinfo=tz)
+    start_ms = int(start_dt.timestamp() * 1000)
+    end_ms = start_ms + 86_400_000 - 1
+    filters: dict[str, Any] = {"dateFrom": start_ms, "dateTo": end_ms}
+    if not include_completed:
+        filters["isCompleted"] = 0
+    result = await execute_on_client(
+        action="select",
+        table="timelines",
+        filters=filters,
+    )
+    rows = result.get("rows", [])
+    if not rows:
+        return "No timeline events today."
+    lines = [
+        f"- {r['title']} (date: {r['date']}, type: {r.get('type')}, "
+        f"completed: {bool(r.get('isCompleted'))}, projectId: {r.get('projectId')}, id: {r['id']})"
+        for r in rows
+    ]
+    return f"Timeline events today ({len(rows)}):\n" + "\n".join(lines)
 
 
 TIMELINE_TOOLS: list[Any] = [
     list_timelines,
+    count_timelines,
+    list_timelines_today,
     create_timeline,
     update_timeline,
     delete_timeline,
 ]
+
+TIMELINE_READ_TOOLS: list[Any] = [
+    list_timelines,
+    count_timelines,
+    list_timelines_today,
+]

app/api/routes/billing.py

@@ -9,7 +9,7 @@ from __future__ import annotations
 
 from typing import Any
 
-from fastapi import APIRouter, Depends, Header, Request, status
+from fastapi import APIRouter, Depends, Header, HTTPException, Request, status
 from pydantic import BaseModel
 from sqlalchemy.ext.asyncio import AsyncSession
 
@@ -96,3 +96,37 @@ async def list_invoices(
     """
     invoices = await stripe_service.list_invoices(current_user.id, db)
     return invoices
+
+
+# ── Quota check ────────────────────────────────────────────────────────
+
+from app.billing.quota import check_folder_quota, QuotaExceeded  # noqa: E402
+
+
+class QuotaCheckRequest(BaseModel):
+    feature: str
+    estimated_files: int
+
+
+@router.post("/quota/check")
+async def quota_check(
+    payload: QuotaCheckRequest,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict:
+    """Pre-flight folder quota check. 402 if tier limits would be exceeded."""
+    if payload.feature != "folder_index":
+        raise HTTPException(status_code=400, detail="Unknown feature")
+    try:
+        await check_folder_quota(
+            user_id=current_user.id,
+            tier=current_user.tier,
+            estimated_files=payload.estimated_files,
+            db=db,
+        )
+    except QuotaExceeded as exc:
+        raise HTTPException(
+            status_code=402,
+            detail={"reason": exc.reason, "message": str(exc)},
+        )
+    return {"ok": True}

app/api/routes/chat.py

@@ -5,13 +5,19 @@ WebSocket chat is handled by the unified device WS endpoint (/api/v1/ws/device).
 
 from __future__ import annotations
 
-from fastapi import APIRouter, Depends
+import uuid
+from typing import Literal
+
+from fastapi import APIRouter, Depends, HTTPException
 from fastapi.responses import JSONResponse
 from pydantic import BaseModel
 
 from app.api.deps import get_current_user
+from app.core.brief_agent import run_home_brief, run_project_brief
 from app.core.deep_agent import run_home
 from app.core.llm import embed
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import async_session
 from app.schemas import ChatRequest, UserProfile
 
 router = APIRouter(prefix="/chat", tags=["chat"])
@@ -45,6 +51,57 @@ async def chat(
     return JSONResponse(content={"response": response})
 
 
+class _BriefRequest(BaseModel):
+    mode: Literal["home", "project"]
+    project_id: str | None = None
+
+
+class _BriefResponse(BaseModel):
+    response: str
+
+
+@router.post("/brief", response_model=_BriefResponse)
+async def brief(
+    body: _BriefRequest,
+    current_user: UserProfile = Depends(get_current_user),
+) -> _BriefResponse:
+    """REST fallback for brief when the device WebSocket is not ready."""
+    if body.mode == "project":
+        if not body.project_id:
+            raise HTTPException(status_code=422, detail="project_id required for project mode")
+        try:
+            uuid.UUID(body.project_id)
+        except ValueError:
+            raise HTTPException(status_code=422, detail="project_id must be a valid UUID")
+
+    request_id = str(uuid.uuid4())
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        memory_context = await memory.enrich_context(
+            current_user.id,
+            "",
+            trace_id=request_id,
+            session_id=request_id,
+        )
+
+    context: dict = {
+        "_debug": {"request_id": request_id, "user_id": current_user.id},
+        **memory_context,
+    }
+
+    chunks: list[str] = []
+    if body.mode == "project":
+        stream = run_project_brief(current_user.id, body.project_id, context)  # type: ignore[arg-type]
+    else:
+        stream = run_home_brief(current_user.id, context)
+
+    async for event_type, data in stream:
+        if event_type == "token" and data:
+            chunks.append(str(data))
+
+    return _BriefResponse(response="".join(chunks))
+
+
 @router.post("/embed", response_model=_EmbedResponse)
 async def embed_text(
     body: _EmbedRequest,

app/api/routes/device_ws.py

@@ -9,7 +9,7 @@ available during the WebSocket handshake).
 
 Protocol:
   1. Client connects → JWT validated → connection accepted.
-  2. Client sends ``device_hello`` frame: ``{ type, device_id, agent_ids }``.
+  2. Client sends ``device_hello`` frame: ``{ type, device_id, scout_ids }``.
   3. Backend registers the connection in ``DeviceConnectionManager``.
   4. Session enters message dispatch loop + heartbeat.
 
@@ -39,22 +39,30 @@ from fastapi import APIRouter, WebSocket, WebSocketDisconnect
 from jose import JWTError, jwt
 from sqlalchemy import update
 
-from app.api.routes.agent_setup import handle_journey_message, handle_journey_start
+from app.api.routes.scout_setup import handle_journey_message, handle_journey_start
 from app.config.settings import settings
-from app.core.agent_runner import trigger_pending_runs
+from app.core.scout_runner import trigger_pending_runs
-from app.core.deep_agent import run_floating_stream, run_home_stream
+from app.core.scout_session_buffer import session_buffer
+from app.core.brief_agent import run_home_brief, run_project_brief
+from app.core.deep_agent import run_contextual_stream, run_home_stream, run_task_brief_research_stream
+from app.core.output_formatter import extract_canvas_block
 from app.core.device_manager import device_manager
 from app.core.memory_middleware import MemoryMiddleware
 from app.core.output_formatter import StreamFormatter
 from app.core.ws_context import clear_client_executor, set_client_executor
 from app.db import async_session
-from app.models import AgentRunLog
+from app.models import ScoutRunLog
-from app.schemas import WsFrameType
+from app.schemas import WsFrameType, WsStreamEnd
+from app.schemas.contextual import ContextualScope, render_scope_block
 
 logger = logging.getLogger(__name__)
 
 router = APIRouter(prefix="/ws", tags=["device-ws"])
 
+# ── v7 folder index session state ─────────────────────────────────────
+# Keyed by sessionId; value: { user_id, project_id, processed, total, cancelled }
+_index_sessions: dict[str, dict] = {}
+
 _HEARTBEAT_INTERVAL = 30  # seconds
 _PONG_TIMEOUT = 10  # seconds — grace window after a ping
 
@@ -92,7 +100,7 @@ async def device_ws(websocket: WebSocket) -> None:
         if hello.get("type") != WsFrameType.device_hello:
             raise ValueError("expected device_hello as first frame")
         device_id: str = hello["device_id"]
-        agent_ids: list[str] = hello.get("agent_ids", [])
+        scout_ids: list[str] = hello.get("scout_ids", [])
     except (KeyError, ValueError, json.JSONDecodeError) as exc:
         logger.warning("device_ws: invalid device_hello from user=%s: %s", user_id, exc)
         await websocket.close(code=1008)
@@ -101,10 +109,10 @@ async def device_ws(websocket: WebSocket) -> None:
     # ── 3. Register connection ────────────────────────────────────────
     device_manager.register(user_id, device_id, websocket)
     logger.info(
-        "device_ws: connected user=%s device=%s agents=%s",
+        "device_ws: connected user=%s device=%s scouts=%s",
         user_id,
         device_id,
-        agent_ids,
+        scout_ids,
     )
 
     # Trigger any overdue agent runs now that the device is connected.
@@ -153,9 +161,14 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
                 _handle_home_request(websocket, user_id, frame)
             )
 
-        elif frame_type == WsFrameType.floating_request:
+        elif frame_type == WsFrameType.brief_request:
             asyncio.create_task(
-                _handle_floating_request(websocket, user_id, frame)
+                _handle_brief_request(websocket, user_id, frame)
+            )
+
+        elif frame_type == WsFrameType.task_brief_request:
+            asyncio.create_task(
+                _handle_task_brief_request(websocket, user_id, frame)
             )
 
         elif frame_type == WsFrameType.journey_start:
@@ -168,6 +181,29 @@ async def _message_loop(websocket: WebSocket, user_id: str) -> None:
                 _handle_journey_message(websocket, user_id, frame)
             )
 
+        elif frame_type == WsFrameType.index_session_start:
+            asyncio.create_task(
+                _handle_index_session_start(websocket, user_id, frame)
+            )
+
+        elif frame_type == WsFrameType.index_file_batch:
+            asyncio.create_task(
+                _handle_index_file_batch(websocket, user_id, frame)
+            )
+
+        elif frame_type == WsFrameType.index_session_cancel:
+            await _handle_index_session_cancel(websocket, frame)
+
+        elif frame_type == WsFrameType.contextual_request:
+            asyncio.create_task(
+                _handle_contextual_request(websocket, user_id, frame)
+            )
+
+        elif frame_type == WsFrameType.contextual_scope_update:
+            asyncio.create_task(
+                _handle_contextual_scope_update(websocket, user_id, frame)
+            )
+
         elif frame_type == "pong":
             # Heartbeat ack — nothing to do, connection is alive.
             pass
@@ -199,11 +235,13 @@ async def _handle_home_request(
     request_id = frame.get("request_id") or str(uuid4())
     message: str = frame.get("message", "")
     session_id: str = frame.get("session_id") or str(uuid4())
+    project_id: str | None = frame.get("project_id") or frame.get("projectId") or None
     logger.info(
-        "device_ws: home_request_start user=%s req=%s session=%s msg=%s",
+        "device_ws: home_request_start user=%s req=%s session=%s project=%s msg=%s",
         user_id,
         request_id,
         session_id,
+        project_id,
         message[:200],
     )
 
@@ -220,6 +258,7 @@ async def _handle_home_request(
     context: dict = {
         "conversation_history": frame.get("conversation_history", []),
         "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+        "format_prefs": frame.get("format_prefs"),
         **memory_context,
     }
 
@@ -227,7 +266,7 @@ async def _handle_home_request(
     set_client_executor(executor)
     response_chunks: list[str] = []
     try:
-        event_stream = run_home_stream(user_id, message, context)
+        event_stream = run_home_stream(user_id, message, context, project_id=project_id)
         formatter = StreamFormatter(request_id=request_id)
         async for ws_frame in formatter.format(event_stream):
             await websocket.send_text(ws_frame.model_dump_json())
@@ -257,26 +296,41 @@ async def _handle_home_request(
     )
 
 
-async def _handle_floating_request(
+# ── v8 Contextual Sidebar Handlers ───────────────────────────────────
+
+
+def get_session_buffer(user_id: str, session_id: str, channel: str = "contextual"):
+    """Return a session-scoped buffer proxy for the given user+session.
+
+    Returns a _ContextualBufferProxy that exposes append_system_message().
+    Defined at module level so tests can monkeypatch it.
+    The channel kwarg is accepted for forward-compatibility.
+    """
+    from app.core.scout_session_buffer import ContextualBufferProxy  # noqa: PLC0415
+    return ContextualBufferProxy(session_buffer, user_id, session_id)
+
+
+async def _handle_contextual_request(
     websocket: WebSocket,
     user_id: str,
     frame: dict,
 ) -> None:
-    """Handle a floating_request frame — streams FloatingFormatter output back on the socket."""
+    """Handle a contextual_request frame — runs the contextual agent and streams frames."""
     request_id = frame.get("request_id") or str(uuid4())
     message: str = frame.get("message", "")
     session_id: str = frame.get("session_id") or str(uuid4())
-    scope: dict = frame.get("scope", {})
+    scope_payload: dict = frame.get("scope", {})
     logger.info(
-        "device_ws: floating_request_start user=%s req=%s session=%s scope=%s msg=%s",
+        "device_ws: contextual_request_start user=%s req=%s session=%s msg=%s",
         user_id,
         request_id,
         session_id,
-        json.dumps(scope, ensure_ascii=True)[:200],
         message[:200],
     )
 
-    # ── Memory: enrich context before LLM call ────────────────────────
+    scope = ContextualScope.model_validate(scope_payload)
+
+    # Enrich context with memory before the LLM call.
     async with async_session() as db:
         memory = MemoryMiddleware(db)
         memory_context = await memory.enrich_context(
@@ -287,7 +341,8 @@ async def _handle_floating_request(
         )
 
     context: dict = {
-        "scope": scope,
+        "conversation_history": frame.get("conversation_history", []),
+        "format_prefs": frame.get("format_prefs"),
         "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
         **memory_context,
     }
@@ -296,7 +351,12 @@ async def _handle_floating_request(
     set_client_executor(executor)
     response_chunks: list[str] = []
     try:
-        event_stream = run_floating_stream(user_id, message, context)
+        event_stream = run_contextual_stream(
+            user_id=user_id,
+            message=message,
+            context=context,
+            scope=scope,
+        )
         formatter = StreamFormatter(request_id=request_id)
         async for ws_frame in formatter.format(event_stream):
             await websocket.send_text(ws_frame.model_dump_json())
@@ -304,20 +364,20 @@ async def _handle_floating_request(
                 response_chunks.append(ws_frame.chunk)  # type: ignore[union-attr]
     except Exception as exc:
         logger.error(
-            "device_ws: floating_request failed user=%s req=%s: %s",
+            "device_ws: contextual_request failed user=%s req=%s: %s",
             user_id, request_id, exc,
         )
     finally:
         clear_client_executor()
 
-    # ── Memory: store episode after response ──────────────────────────
+    # Store episode so the contextual agent can recall prior turns.
     async with async_session() as db:
         memory = MemoryMiddleware(db)
         await memory.store_episode(
             user_id, session_id, message, "".join(response_chunks), trace_id=request_id
         )
     logger.info(
-        "device_ws: floating_request_end user=%s req=%s session=%s response_chars=%d",
+        "device_ws: contextual_request_end user=%s req=%s session=%s response_chars=%d",
         user_id,
         request_id,
         session_id,
@@ -325,6 +385,206 @@ async def _handle_floating_request(
     )
 
 
+async def _handle_contextual_scope_update(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Handle a contextual_scope_update frame.
+
+    Injects a synthetic system message into the session buffer so the next
+    agent turn knows the user navigated.  No LLM call is made.
+    """
+    session_id: str = frame.get("session_id") or str(uuid4())
+    scope = ContextualScope.model_validate(frame.get("scope", {}))
+    block = render_scope_block(scope)
+    buf = get_session_buffer(user_id, session_id, channel="contextual")
+    buf.append_system_message(
+        f"User navigated to a new view. {block} Treat this as the new active context."
+    )
+    await websocket.send_text(json.dumps({
+        "type": WsFrameType.contextual_scope_ack,
+        "session_id": session_id,
+    }))
+    logger.info(
+        "device_ws: contextual_scope_update user=%s session=%s page=%s",
+        user_id, session_id, scope.page,
+    )
+
+
+async def _handle_brief_request(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Handle a brief_request frame — streams plain-text brief back on the socket.
+
+    No episode storage — briefs are not conversations.
+    """
+    import uuid as _uuid
+
+    request_id = frame.get("request_id") or str(uuid4())
+    session_id = frame.get("session_id") or str(uuid4())
+    mode: str = frame.get("mode", "home")
+    project_id: str | None = frame.get("project_id")
+
+    logger.info(
+        "device_ws: brief_request_start user=%s req=%s mode=%s project_id=%s",
+        user_id, request_id, mode, project_id,
+    )
+
+    # Validate project_id for project mode before touching LLM.
+    if mode == "project":
+        try:
+            if not project_id:
+                raise ValueError("project_id required for project mode")
+            _uuid.UUID(project_id)
+        except (ValueError, AttributeError) as exc:
+            logger.warning(
+                "device_ws: brief_request invalid project_id user=%s req=%s: %s",
+                user_id, request_id, exc,
+            )
+            await websocket.send_text(
+                WsStreamEnd(request_id=request_id, error=str(exc)).model_dump_json()
+            )
+            return
+
+    # Enrich context with memory (no user message — use empty string as probe).
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        memory_context = await memory.enrich_context(
+            user_id,
+            "",
+            trace_id=request_id,
+            session_id=session_id,
+        )
+
+    context: dict = {
+        "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+        "format_prefs": frame.get("format_prefs"),
+        **memory_context,
+    }
+
+    executor = await _make_ws_executor(websocket, user_id)
+    set_client_executor(executor)
+    try:
+        if mode == "project":
+            event_stream = run_project_brief(user_id, project_id, context)  # type: ignore[arg-type]
+        else:
+            event_stream = run_home_brief(user_id, context)
+
+        formatter = StreamFormatter(request_id=request_id)
+        async for ws_frame in formatter.format(event_stream):
+            await websocket.send_text(ws_frame.model_dump_json())
+    except Exception as exc:
+        logger.error(
+            "device_ws: brief_request failed user=%s req=%s: %s",
+            user_id, request_id, exc,
+        )
+        await websocket.send_text(
+            WsStreamEnd(request_id=request_id, error=str(exc)).model_dump_json()
+        )
+    finally:
+        clear_client_executor()
+
+    logger.info(
+        "device_ws: brief_request_end user=%s req=%s mode=%s",
+        user_id, request_id, mode,
+    )
+
+
+# ── v6 Task Brief Handler ────────────────────────────────────────────
+
+
+async def _handle_task_brief_request(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Handle a task_brief_request frame — Stage-1 executive assistant deep research.
+
+    Streams the briefing markdown back to the client.
+    On stream_end, emits a ``canvas_draft`` mutation if the agent produced one.
+    """
+    request_id = frame.get("request_id") or str(uuid4())
+    session_id = frame.get("session_id") or str(uuid4())
+    task_id: str = frame.get("task_id") or frame.get("taskId") or ""
+    project_id: str | None = frame.get("project_id") or frame.get("projectId") or None
+
+    logger.info(
+        "device_ws: task_brief_request_start user=%s req=%s task=%s project=%s [cache_miss]",
+        user_id, request_id, task_id, project_id,
+    )
+
+    if not task_id:
+        await websocket.send_text(
+            WsStreamEnd(request_id=request_id, error="task_id is required").model_dump_json()
+        )
+        return
+
+    async with async_session() as db:
+        memory = MemoryMiddleware(db)
+        memory_context = await memory.enrich_context(
+            user_id,
+            f"task brief: {task_id}",
+            trace_id=request_id,
+            session_id=session_id,
+        )
+
+    context: dict = {
+        "_debug": {"request_id": request_id, "session_id": session_id, "user_id": user_id},
+        "format_prefs": frame.get("format_prefs"),
+        **memory_context,
+    }
+
+    executor = await _make_ws_executor(websocket, user_id)
+    set_client_executor(executor)
+    response_chunks: list[str] = []
+
+    try:
+        event_stream = run_task_brief_research_stream(user_id, task_id, context, project_id=project_id)
+        formatter = StreamFormatter(request_id=request_id)
+        async for ws_frame in formatter.format(event_stream):
+            if ws_frame.type == "stream_text":  # type: ignore[union-attr]
+                response_chunks.append(ws_frame.chunk)  # type: ignore[union-attr]
+                await websocket.send_text(ws_frame.model_dump_json())
+            elif ws_frame.type == "stream_start":
+                await websocket.send_text(ws_frame.model_dump_json())
+            # stream_end is emitted below with mutations — skip formatter's version
+    except Exception as exc:
+        logger.error(
+            "device_ws: task_brief_request failed user=%s req=%s task=%s: %s",
+            user_id, request_id, task_id, exc,
+        )
+        await websocket.send_text(
+            WsStreamEnd(request_id=request_id, error=str(exc)).model_dump_json()
+        )
+        return
+    finally:
+        clear_client_executor()
+
+    # Extract canvas block then emit stream_end with optional mutations.
+    full_response = "".join(response_chunks)
+    _visible, canvas_content, canvas_kind = extract_canvas_block(full_response)
+
+    mutations: list[dict] = []
+    if canvas_content:
+        mutations.append({
+            "type": "canvas_draft",
+            "content": canvas_content,
+            "kind": canvas_kind,
+        })
+
+    await websocket.send_text(
+        WsStreamEnd(request_id=request_id, mutations=mutations or None).model_dump_json()
+    )
+
+    logger.info(
+        "device_ws: task_brief_request_end user=%s req=%s task=%s response_chars=%d canvas=%s",
+        user_id, request_id, task_id, len(full_response), canvas_kind or "none",
+    )
+
+
 # ── v4 Journey Handlers ─────────────────────────────────────────────
 
 
@@ -382,6 +642,174 @@ async def _handle_journey_message(
         clear_client_executor()
 
 
+# ── v7 Folder Index Handlers ──────────────────────────────────────────
+
+
+async def _handle_index_session_start(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Register a new folder index session.  No response sent — client is declaring intent."""
+    session_id: str = frame.get("sessionId") or frame.get("session_id") or ""
+    project_id: str | None = frame.get("projectId") or frame.get("project_id")
+    total: int = int(frame.get("totalFiles") or frame.get("total_files") or 0)
+
+    if not session_id:
+        logger.warning("device_ws: index_session_start missing sessionId user=%s", user_id)
+        return
+
+    _index_sessions[session_id] = {
+        "user_id": user_id,
+        "project_id": project_id,
+        "processed": 0,
+        "total": total,
+        "cancelled": False,
+    }
+    logger.info(
+        "device_ws: index_session_start user=%s session=%s project=%s total=%d",
+        user_id, session_id, project_id, total,
+    )
+
+
+async def _handle_index_session_cancel(
+    websocket: WebSocket,
+    frame: dict,
+) -> None:
+    """Mark a session as cancelled and emit index_session_done(cancelled)."""
+    session_id: str = frame.get("sessionId") or frame.get("session_id") or ""
+    session = _index_sessions.get(session_id)
+    if session:
+        session["cancelled"] = True
+
+    await websocket.send_text(json.dumps({
+        "type": WsFrameType.index_session_done,
+        "sessionId": session_id,
+        "status": "cancelled",
+    }))
+    _index_sessions.pop(session_id, None)
+    logger.info("device_ws: index_session_cancel session=%s", session_id)
+
+
+async def _handle_index_file_batch(
+    websocket: WebSocket,
+    user_id: str,
+    frame: dict,
+) -> None:
+    """Process a batch of files for an index session, streaming results back."""
+    # Lazy imports to avoid heavy load at module startup.
+    from app.core.folder_indexer import (  # noqa: PLC0415
+        summarize_image,
+        summarize_pdf,
+        summarize_docx,
+        summarize_text,
+    )
+    from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+    from app.billing.quota import add_token_usage  # noqa: PLC0415
+
+    session_id: str = frame.get("sessionId") or frame.get("session_id") or ""
+    files: list[dict] = frame.get("files", [])
+
+    session = _index_sessions.get(session_id)
+    if not session or session.get("cancelled"):
+        return
+
+    async with async_session() as db:
+        tier = await tier_manager.get_tier(user_id, db)
+        raw_cap = tier_manager.get_feature_value(tier, "folder_monthly_tokens")
+        cap: int | None = None if raw_cap == -1 else raw_cap
+
+        for file_info in files:
+            if session.get("cancelled"):
+                return
+
+            # Electron's toSnakeCase converts payload keys, so accept both forms.
+            rel_path: str = file_info.get("relPath") or file_info.get("rel_path") or ""
+            kind: str = file_info.get("kind") or "text"
+            content: str = file_info.get("content") or ""
+            ext: str = file_info.get("ext") or ""
+            mime: str = file_info.get("mime") or "application/octet-stream"
+            name: str = rel_path.split("/")[-1] or rel_path
+
+            try:
+                if kind == "image":
+                    res = await summarize_image(image_b64=content, mime=mime)
+                elif kind == "pdf":
+                    res = await summarize_pdf(pdf_b64=content, name=name)
+                elif kind == "docx":
+                    res = await summarize_docx(docx_b64=content, name=name)
+                else:
+                    res = await summarize_text(content=content, ext=ext, name=name)
+            except Exception as exc:
+                logger.warning(
+                    "device_ws: index_file_batch summarize failed session=%s path=%s: %s",
+                    session_id, rel_path, exc,
+                )
+                await websocket.send_text(json.dumps({
+                    "type": WsFrameType.index_file_result,
+                    "sessionId": session_id,
+                    "relPath": rel_path,
+                    "summary": None,
+                    "tokensUsed": 0,
+                    "error": str(exc),
+                }))
+                session["processed"] += 1
+                continue
+
+            # Account for token usage and check cap.
+            usage = await add_token_usage(
+                user_id=user_id,
+                feature="folder_index",
+                tokens=res.tokens_used,
+                db=db,
+                cap=cap,
+            )
+
+            await websocket.send_text(json.dumps({
+                "type": WsFrameType.index_file_result,
+                "sessionId": session_id,
+                "relPath": rel_path,
+                "summary": res.summary,
+                "tokensUsed": res.tokens_used,
+            }))
+            session["processed"] += 1
+
+            if usage.exhausted:
+                await websocket.send_text(json.dumps({
+                    "type": WsFrameType.index_session_done,
+                    "sessionId": session_id,
+                    "status": "quota_exceeded",
+                }))
+                _index_sessions.pop(session_id, None)
+                logger.info(
+                    "device_ws: index_session quota_exceeded user=%s session=%s",
+                    user_id, session_id,
+                )
+                return
+
+        # After processing the batch, emit progress.
+        processed = session["processed"]
+        total = session["total"]
+        await websocket.send_text(json.dumps({
+            "type": WsFrameType.index_session_progress,
+            "sessionId": session_id,
+            "processed": processed,
+            "total": total,
+        }))
+
+        if processed >= total:
+            await websocket.send_text(json.dumps({
+                "type": WsFrameType.index_session_done,
+                "sessionId": session_id,
+                "status": "completed",
+            }))
+            _index_sessions.pop(session_id, None)
+            logger.info(
+                "device_ws: index_session_done completed user=%s session=%s processed=%d",
+                user_id, session_id, processed,
+            )
+
+
 # ── Heartbeat ─────────────────────────────────────────────────────────
 
 async def _heartbeat_loop(websocket: WebSocket) -> None:
@@ -394,14 +822,14 @@ async def _heartbeat_loop(websocket: WebSocket) -> None:
 # ── Disconnect cleanup ────────────────────────────────────────────────
 
 async def _mark_runs_disconnected(user_id: str) -> None:
-    """Mark all in-progress AgentRunLog rows as 'error' for this user."""
+    """Mark all in-progress ScoutRunLog rows as 'error' for this user."""
     try:
         async with async_session() as db:
             await db.execute(
-                update(AgentRunLog)
+                update(ScoutRunLog)
                 .where(
-                    AgentRunLog.user_id == user_id,
+                    ScoutRunLog.user_id == user_id,
-                    AgentRunLog.status == "running",
+                    ScoutRunLog.status == "running",
                 )
                 .values(
                     status="error",

app/api/routes/memory.py

@@ -0,0 +1,225 @@
+"""Memory management routes — view/edit/delete user memory tiers.
+
+All routes require authentication. Data is always user-scoped.
+"""
+
+from __future__ import annotations
+
+import logging
+from datetime import datetime, timezone
+from typing import Annotated
+
+from fastapi import APIRouter, Depends, Header, HTTPException, status
+from pydantic import BaseModel, Field
+from sqlalchemy import delete, select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.api.deps import get_current_user
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import get_session
+from app.models import (
+    ExtractionQueue,
+    MemoryAssociative,
+    MemoryCore,
+    MemoryEpisodic,
+    MemoryProactive,
+    MemoryRelation,
+)
+from app.schemas import UserProfile
+
+router = APIRouter(prefix="/memory", tags=["memory"])
+
+logger = logging.getLogger(__name__)
+
+_ALLOWED_PREDICATES = {
+    "works_at",
+    "reports_to",
+    "stakeholder_of",
+    "last_contacted_on",
+    "owes_followup",
+    "manages",
+    "collaborates_with",
+    "owns",
+    "member_of",
+    "custom",
+}
+
+
+# ── Response schemas ─────────────────────────────────────────────────────────
+
+class RelationOut(BaseModel):
+    id: str
+    subject_label: str
+    subject_type: str
+    predicate: str
+    object_label: str
+    object_type: str
+    confidence: float
+    last_confirmed_at: int | None = None  # epoch ms
+
+
+class RelationPatch(BaseModel):
+    subject_label: str | None = None
+    object_label: str | None = None
+    predicate: str | None = None
+    confidence: float | None = Field(None, ge=0.0, le=1.0)
+
+
+class CoreAddBody(BaseModel):
+    key: str = Field(..., min_length=1, max_length=255)
+    value: str = Field(..., min_length=1)
+
+
+# ── Helpers ──────────────────────────────────────────────────────────────────
+
+def _relation_to_out(row: MemoryRelation) -> RelationOut:
+    last_ms: int | None = None
+    if row.last_confirmed_at is not None:
+        last_ms = int(row.last_confirmed_at.timestamp() * 1000)
+    return RelationOut(
+        id=row.id,
+        subject_label=row.subject_label,
+        subject_type=row.subject_type,
+        predicate=row.predicate,
+        object_label=row.object_label,
+        object_type=row.object_type,
+        confidence=row.confidence,
+        last_confirmed_at=last_ms,
+    )
+
+
+# ── Routes ───────────────────────────────────────────────────────────────────
+
+@router.get("/core", response_model=dict[str, str])
+async def get_core_memory(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, str]:
+    """Return all core memory k/v pairs (plaintext) for the current user."""
+    mw = MemoryMiddleware(db)
+    blocks = await mw.list_core_blocks(current_user.id)
+    return {b["label"]: b["value"] for b in blocks}
+
+
+@router.delete("/core/{key}", status_code=status.HTTP_204_NO_CONTENT)
+async def delete_core_key(
+    key: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> None:
+    """Delete a single core memory key (GDPR Art. 17)."""
+    mw = MemoryMiddleware(db)
+    deleted = await mw.delete_core(current_user.id, key)
+    if not deleted:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Key not found")
+
+
+@router.post("/core", status_code=status.HTTP_201_CREATED, response_model=dict[str, str])
+async def add_core_key(
+    body: CoreAddBody,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> dict[str, str]:
+    """Add or overwrite a core memory key/value pair."""
+    mw = MemoryMiddleware(db)
+    await mw.update_core(current_user.id, body.key, body.value)
+    return {body.key: body.value}
+
+
+@router.get("/relational", response_model=list[RelationOut])
+async def get_relational_memory(
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> list[RelationOut]:
+    """Return all relational memory rows for the current user."""
+    mw = MemoryMiddleware(db)
+    rows = await mw.query_relations(current_user.id, limit=200)
+    return [_relation_to_out(r) for r in rows]
+
+
+@router.patch("/relational/{relation_id}", response_model=RelationOut)
+async def patch_relation(
+    relation_id: str,
+    body: RelationPatch,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> RelationOut:
+    """Edit a relation row's labels, predicate, or confidence."""
+    if body.predicate is not None and body.predicate not in _ALLOWED_PREDICATES:
+        raise HTTPException(
+            status_code=status.HTTP_422_UNPROCESSABLE_ENTITY,
+            detail=f"predicate must be one of: {sorted(_ALLOWED_PREDICATES)}",
+        )
+
+    result = await db.execute(
+        select(MemoryRelation).where(
+            MemoryRelation.id == relation_id,
+            MemoryRelation.user_id == current_user.id,
+        )
+    )
+    row = result.scalar_one_or_none()
+    if row is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Relation not found")
+
+    if body.subject_label is not None:
+        row.subject_label = body.subject_label
+    if body.object_label is not None:
+        row.object_label = body.object_label
+    if body.predicate is not None:
+        row.predicate = body.predicate
+    if body.confidence is not None:
+        row.confidence = body.confidence
+        row.last_confirmed_at = datetime.now(timezone.utc)
+
+    await db.commit()
+    await db.refresh(row)
+    logger.info("memory: patch_relation user=%s relation=%s", current_user.id, relation_id)
+    return _relation_to_out(row)
+
+
+@router.delete("/relational/{relation_id}", status_code=status.HTTP_204_NO_CONTENT)
+async def delete_relation(
+    relation_id: str,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> None:
+    """Hard-delete a relation row (GDPR Art. 17)."""
+    result = await db.execute(
+        select(MemoryRelation).where(
+            MemoryRelation.id == relation_id,
+            MemoryRelation.user_id == current_user.id,
+        )
+    )
+    row = result.scalar_one_or_none()
+    if row is None:
+        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail="Relation not found")
+    await db.delete(row)
+    await db.commit()
+    logger.info("memory: delete_relation user=%s relation=%s", current_user.id, relation_id)
+
+
+@router.post("/forget-all", status_code=status.HTTP_204_NO_CONTENT)
+async def forget_all(
+    x_confirm: Annotated[str | None, Header(alias="X-Confirm")] = None,
+    current_user: UserProfile = Depends(get_current_user),
+    db: AsyncSession = Depends(get_session),
+) -> None:
+    """Wipe all memory tiers for the current user (GDPR Art. 17).
+
+    Requires ``X-Confirm: true`` header. Does NOT delete the user account.
+    """
+    if x_confirm != "true":
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail="Missing or invalid X-Confirm header. Send X-Confirm: true to confirm.",
+        )
+
+    uid = current_user.id
+    await db.execute(delete(MemoryCore).where(MemoryCore.user_id == uid))
+    await db.execute(delete(MemoryAssociative).where(MemoryAssociative.user_id == uid))
+    await db.execute(delete(MemoryEpisodic).where(MemoryEpisodic.user_id == uid))
+    await db.execute(delete(MemoryProactive).where(MemoryProactive.user_id == uid))
+    await db.execute(delete(MemoryRelation).where(MemoryRelation.user_id == uid))
+    await db.execute(delete(ExtractionQueue).where(ExtractionQueue.user_id == uid))
+    await db.commit()
+    logger.warning("memory: forget_all GDPR wipe user=%s", uid)

app/api/routes/scout_setup.py

@@ -1,4 +1,4 @@
-"""Chatbot Journey — WS-based guided conversation to build an AgentConfig.
+"""Chatbot Journey — WS-based guided conversation to build an ScoutConfig.
 
 The journey is driven entirely through WebSocket frames (no REST endpoints).
 The device WS handler dispatches ``journey_start`` and ``journey_message``
@@ -13,7 +13,7 @@ Journey flow:
   3. FE sends ``journey_message`` frames for each user reply.
   4. Server appends the user message, calls the LLM (which may read files
      via tools), and sends back a ``journey_reply``.
-  5. After 3-5 turns the LLM wraps up by emitting an ``AgentConfig`` JSON
+  5. After 3-5 turns the LLM wraps up by emitting an ``ScoutConfig`` JSON
      block delimited by ``AGENT_CONFIG_START`` / ``AGENT_CONFIG_END``.
   6. Server parses and validates the JSON with Pydantic, sends
      ``journey_reply`` with ``done=True`` and the serialised config.
@@ -34,7 +34,7 @@ from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, Tool
 from app.agents.filesystem_agent import make_directory_tools
 from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
 from app.core.llm import get_agent_llm, model_for_agent
-from app.schemas import AgentConfig
+from app.schemas import ScoutConfig
 
 logger = logging.getLogger(__name__)
 
@@ -42,7 +42,7 @@ logger = logging.getLogger(__name__)
 
 _SESSION_TTL_SECONDS: int = 1800  # 30 minutes
 
-# Sentinel strings used to delimit the LLM-produced AgentConfig JSON.
+# Sentinel strings used to delimit the LLM-produced ScoutConfig JSON.
 _CONFIG_START = "AGENT_CONFIG_START"
 _CONFIG_END = "AGENT_CONFIG_END"
 
@@ -92,7 +92,7 @@ def get_journey_session(session_id: str, user_id: str) -> JourneySession | None:
 _JOURNEY_SYSTEM_PROMPT = """\
 You are a friendly assistant helping a freelancer configure a data-extraction agent.
 Your job is to understand what files the user has in their directory and produce a
-structured AgentConfig JSON that the extraction agent will use as its instruction set.
+structured ScoutConfig JSON that the extraction agent will use as its instruction set.
 
 You have access to file-system tools to explore the user's directory:
 - list_directory: see folder structure and file names
@@ -122,7 +122,7 @@ Cover these topics based on what you discovered:
 4. Date extraction (e.g. "by Friday" → dueDate)
 5. Exclusion rules (e.g. skip newsletters, skip files with no project match)
 
-### Step 4 — Produce the AgentConfig JSON
+### Step 4 — Produce the ScoutConfig JSON
 Once you are ≥ 90% confident, output the final config between these exact markers
 (each on its own line):
 
@@ -168,7 +168,7 @@ def _build_system_prompt(
 ) -> tuple[str, Any]:
     """Return ``(compiled_system_prompt, langfuse_prompt_obj_or_None)``."""
     existing_section = (
-        "\nThe user already has the following AgentConfig — refine it based on their answers:\n"
+        "\nThe user already has the following ScoutConfig — refine it based on their answers:\n"
         f"```json\n{existing_config}\n```\n"
         if existing_config
         else ""
@@ -189,11 +189,11 @@ def _build_system_prompt(
     return compiled, prompt_obj
 
 
-# ── AgentConfig extraction ────────────────────────────────────────────────
+# ── ScoutConfig extraction ────────────────────────────────────────────────
 
 
 def _extract_agent_config(text: str) -> str | None:
-    """Return validated AgentConfig JSON string from between markers, or None.
+    """Return validated ScoutConfig JSON string from between markers, or None.
 
     Parses the JSON with Pydantic to ensure it conforms to the schema before
     returning.  Returns None if markers are absent or JSON is invalid.
@@ -206,10 +206,10 @@ def _extract_agent_config(text: str) -> str | None:
     if not raw:
         return None
     try:
-        parsed = AgentConfig.model_validate_json(raw)
+        parsed = ScoutConfig.model_validate_json(raw)
         return parsed.model_dump_json()
     except Exception as exc:
-        logger.warning("agent_setup: failed to parse AgentConfig JSON: %s", exc)
+        logger.warning("agent_setup: failed to parse ScoutConfig JSON: %s", exc)
         return None
 
 
@@ -475,7 +475,7 @@ async def handle_journey_message(
         if turns >= _MAX_TURNS:
             nudge_content = (
                 "[System: You have enough information. Please generate the final "
-                f"AgentConfig JSON now, wrapped in {_CONFIG_START} / {_CONFIG_END} markers.]"
+                f"ScoutConfig JSON now, wrapped in {_CONFIG_START} / {_CONFIG_END} markers.]"
             )
             session.history.append({"role": "user", "content": nudge_content})
 

app/api/routes/scouts.py

@@ -1,12 +1,12 @@
-"""Agent routes.
+"""Scout routes.
 
 Backend responsibilities are intentionally minimal:
-    GET  /agents/catalog         — static catalog for UI display
+    GET  /scouts/catalog         — static catalog for UI display
-    POST /agents/can-create      — billing eligibility check
+    POST /scouts/can-create      — billing eligibility check
-    POST /agents/trigger         — trigger a local agent run
+    POST /scouts/trigger         — trigger a local scout run
 
-Agent configuration is owned by the Electron app and is not persisted
+Scout configuration is owned by the Electron app and is not persisted
-in backend agent-config tables.
+in backend scout-config tables.
 """
 
 from __future__ import annotations
@@ -16,28 +16,31 @@ import logging
 import uuid
 from datetime import datetime, timezone
 
-logger = logging.getLogger(__name__)
-
 from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy import func, select
 from sqlalchemy.ext.asyncio import AsyncSession
 
+from pydantic import BaseModel
+
 from app.api.deps import get_current_user
 from app.billing.tier_manager import FEATURES
-from app.core.agent_runner import is_agent_running, run_local_agent
+from app.core.scout_runner import is_agent_running, run_local_agent
 from app.core.device_manager import device_manager
+from app.core.note_summarizer import generate_note_summary
 from app.db import get_session
-from app.models import AgentRunLog, LocalAgentConfig
+from app.models import ScoutRunLog, LocalScoutConfig
 from app.schemas import (
-    AgentCatalogItem,
+    ScoutCatalogItem,
-    AgentCreationCheckRequest,
+    ScoutCreationCheckRequest,
-    AgentCreationCheckResponse,
+    ScoutCreationCheckResponse,
-    AgentRunLogResponse,
+    ScoutRunLogResponse,
-    AgentTriggerRequest,
+    ScoutTriggerRequest,
     UserProfile,
 )
 
-router = APIRouter(prefix="/agents", tags=["agents"])
+logger = logging.getLogger(__name__)
+
+router = APIRouter(prefix="/scouts", tags=["scouts"])
 
 
 # ── Datetime helpers ──────────────────────────────────────────────────
@@ -67,11 +70,11 @@ def _to_data_types(values: list[str]) -> list[str]:
     return result
 
 
-def _to_run_log_response(log: AgentRunLog) -> AgentRunLogResponse:
+def _to_run_log_response(log: ScoutRunLog) -> ScoutRunLogResponse:
-    return AgentRunLogResponse(
+    return ScoutRunLogResponse(
         id=log.id,
-        agent_id=log.agent_id,
+        agent_id=log.scout_id,
-        agent_type=log.agent_type,  # type: ignore[arg-type]
+        agent_type=log.scout_type,  # type: ignore[arg-type]
         status=log.status,  # type: ignore[arg-type]
         items_processed=log.items_processed,
         items_created=log.items_created,
@@ -105,9 +108,9 @@ async def _enforce_run_frequency(
         hour=0, minute=0, second=0, microsecond=0
     )
     result = await db.execute(
-        select(func.count(AgentRunLog.id)).where(
+        select(func.count(ScoutRunLog.id)).where(
-            AgentRunLog.user_id == user_id,
+            ScoutRunLog.user_id == user_id,
-            AgentRunLog.started_at >= today_start,
+            ScoutRunLog.started_at >= today_start,
         )
     )
     runs_today: int = result.scalar_one()
@@ -121,28 +124,28 @@ async def _enforce_run_frequency(
 
 # ── Catalog ───────────────────────────────────────────────────────────
 
-@router.get("/catalog", response_model=list[AgentCatalogItem])
+@router.get("/catalog", response_model=list[ScoutCatalogItem])
 async def get_agent_catalog(
     current_user: UserProfile = Depends(get_current_user),
-) -> list[AgentCatalogItem]:
+) -> list[ScoutCatalogItem]:
     """Return the static list of available agent types and their descriptions."""
     return [
-        AgentCatalogItem(
+        ScoutCatalogItem(
             type="local_directory",
             name="Local Directory Monitor",
             description="Watches local directories, extracts data from files using AI",
         ),
-        AgentCatalogItem(
+        ScoutCatalogItem(
             type="gmail",
             name="Gmail Connector",
             description="Scans Gmail inbox, extracts tasks/notes from emails",
         ),
-        AgentCatalogItem(
+        ScoutCatalogItem(
             type="teams",
             name="Microsoft Teams Connector",
             description="Monitors Teams messages, extracts action items",
         ),
-        AgentCatalogItem(
+        ScoutCatalogItem(
             type="outlook",
             name="Outlook Connector",
             description="Scans Outlook inbox, extracts tasks/notes",
@@ -150,11 +153,11 @@ async def get_agent_catalog(
     ]
 
 
-@router.post("/can-create", response_model=AgentCreationCheckResponse)
+@router.post("/can-create", response_model=ScoutCreationCheckResponse)
 async def can_create_agent(
-    body: AgentCreationCheckRequest,
+    body: ScoutCreationCheckRequest,
     current_user: UserProfile = Depends(get_current_user),
-) -> AgentCreationCheckResponse:
+) -> ScoutCreationCheckResponse:
     """Check if the user can create one more agent based on billing tier.
 
     Since configuration is client-owned, the Electron app sends its current
@@ -162,7 +165,7 @@ async def can_create_agent(
     """
     limit: int = FEATURES.get(current_user.tier, FEATURES["free"])["batch_active"]
     allowed = limit == -1 or body.active_agents < limit
-    return AgentCreationCheckResponse(
+    return ScoutCreationCheckResponse(
         allowed=allowed,
         tier=current_user.tier,
         active_agents=body.active_agents,
@@ -170,12 +173,12 @@ async def can_create_agent(
     )
 
 
-@router.post("/trigger", response_model=AgentRunLogResponse, status_code=status.HTTP_202_ACCEPTED)
+@router.post("/trigger", response_model=ScoutRunLogResponse, status_code=status.HTTP_202_ACCEPTED)
 async def trigger_agent_run(
-    body: AgentTriggerRequest,
+    body: ScoutTriggerRequest,
     current_user: UserProfile = Depends(get_current_user),
     db: AsyncSession = Depends(get_session),
-) -> AgentRunLogResponse:
+) -> ScoutRunLogResponse:
     """Trigger a local agent run using client-provided configuration."""
     _enforce_agent_limit(current_user.tier, body.active_agents)
     await _enforce_run_frequency(current_user.tier, current_user.id, db)
@@ -185,7 +188,7 @@ async def trigger_agent_run(
         if body.last_run_at
         else None
     )
-    config = LocalAgentConfig(
+    config = LocalScoutConfig(
         id=str(uuid.uuid4()),
         user_id=current_user.id,
         device_id=body.device_id,
@@ -193,7 +196,7 @@ async def trigger_agent_run(
         directory_paths=[body.directory],
         data_types=_to_data_types(body.what_to_extract),
         prompt_template=body.custom_agent_prompt or "",
-        agent_config=body.agent_config,
+        scout_config=body.agent_config,
         file_extensions=[],
         schedule_cron=body.batch_interval,
         enabled=True,
@@ -209,9 +212,9 @@ async def trigger_agent_run(
             detail="Agent is already running. Only one run per agent is allowed at a time.",
         )
 
-    run_log = AgentRunLog(
+    run_log = ScoutRunLog(
-        agent_id=stable_agent_id,
+        scout_id=stable_agent_id,
-        agent_type="local",
+        scout_type="local",
         user_id=current_user.id,
         status="running",
     )
@@ -230,3 +233,25 @@ async def trigger_agent_run(
     )
 
     return _to_run_log_response(run_log)
+
+
+# ── Note summary endpoint ──────────────────────────────────────────────────────
+
+
+class NoteSummarizeRequest(BaseModel):
+    title: str
+    content: str
+
+
+class NoteSummarizeResponse(BaseModel):
+    summary: str
+
+
+@router.post("/notes/summarize", response_model=NoteSummarizeResponse)
+async def summarize_note(
+    body: NoteSummarizeRequest,
+    current_user: UserProfile = Depends(get_current_user),
+) -> NoteSummarizeResponse:
+    """Generate an AI summary for a note.  Used by the Electron backfill on startup."""
+    summary = await generate_note_summary(body.title, body.content)
+    return NoteSummarizeResponse(summary=summary)

app/billing/quota.py

@@ -0,0 +1,139 @@
+"""Quota checks and atomic token-usage accounting for folder integration."""
+from __future__ import annotations
+
+from dataclasses import dataclass
+from datetime import datetime, timezone
+
+from sqlalchemy import select, update
+from sqlalchemy.dialects.postgresql import insert as pg_insert
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.billing.tier_manager import TierManager
+from app.models import MonthlyTokenUsage
+from app.schemas import BillingTier
+
+
+class QuotaExceeded(Exception):
+    """Raised when a folder operation cannot proceed under the user's tier."""
+
+    def __init__(self, reason: str, message: str) -> None:
+        super().__init__(message)
+        self.reason = reason  # "max_files" | "monthly_tokens"
+
+
+@dataclass
+class TokenUsageResult:
+    tokens_used: int
+    exhausted: bool
+
+
+def _current_year_month() -> str:
+    return datetime.now(timezone.utc).strftime("%Y-%m")
+
+
+_tier_manager = TierManager()
+
+
+async def check_folder_quota(
+    *,
+    user_id: str,
+    tier: BillingTier,
+    estimated_files: int,
+    db: AsyncSession,
+) -> None:
+    """Raise QuotaExceeded if folder_max_files or folder_monthly_tokens
+    would be violated. -1 in either feature means unlimited."""
+    max_files = _tier_manager.get_feature_value(tier, "folder_max_files")
+    if max_files != -1 and estimated_files > max_files:
+        raise QuotaExceeded(
+            "max_files",
+            f"Folder has {estimated_files} files; tier '{tier}' allows max {max_files}.",
+        )
+
+    cap = _tier_manager.get_feature_value(tier, "folder_monthly_tokens")
+    if cap == -1:
+        return
+    ym = _current_year_month()
+    row = (
+        await db.execute(
+            select(MonthlyTokenUsage).where(
+                MonthlyTokenUsage.user_id == user_id,
+                MonthlyTokenUsage.year_month == ym,
+                MonthlyTokenUsage.feature == "folder_index",
+            )
+        )
+    ).scalar_one_or_none()
+    used = row.tokens_used if row else 0
+    if used >= cap:
+        raise QuotaExceeded(
+            "monthly_tokens",
+            f"Monthly token budget exhausted ({used}/{cap}); resets next month.",
+        )
+
+
+async def add_token_usage(
+    *,
+    user_id: str,
+    feature: str,
+    tokens: int,
+    db: AsyncSession,
+    cap: int | None = None,
+) -> TokenUsageResult:
+    """Atomically add `tokens` to MonthlyTokenUsage row for (user, current month, feature).
+
+    Uses PostgreSQL ``INSERT … ON CONFLICT DO UPDATE`` when available; falls
+    back to a read-then-write on other engines (e.g. aiosqlite in tests).
+    Returns post-update total and whether cap is exhausted.
+    """
+    ym = _current_year_month()
+
+    # Detect dialect to choose between native upsert and portable fallback.
+    dialect_name: str = db.bind.dialect.name if db.bind is not None else ""  # type: ignore[union-attr]
+
+    if dialect_name == "postgresql":
+        # Native atomic upsert — production path.
+        stmt = (
+            pg_insert(MonthlyTokenUsage)
+            .values(
+                user_id=user_id,
+                year_month=ym,
+                feature=feature,
+                tokens_used=tokens,
+            )
+            .on_conflict_do_update(
+                index_elements=["user_id", "year_month", "feature"],
+                set_={"tokens_used": MonthlyTokenUsage.tokens_used + tokens},
+            )
+            .returning(MonthlyTokenUsage.tokens_used)
+        )
+        used: int = (await db.execute(stmt)).scalar_one()
+        await db.commit()
+    else:
+        # Portable fallback — used in tests (SQLite) and any non-PG engine.
+        row = (
+            await db.execute(
+                select(MonthlyTokenUsage).where(
+                    MonthlyTokenUsage.user_id == user_id,
+                    MonthlyTokenUsage.year_month == ym,
+                    MonthlyTokenUsage.feature == feature,
+                )
+            )
+        ).scalar_one_or_none()
+
+        if row is None:
+            row = MonthlyTokenUsage(
+                user_id=user_id,
+                year_month=ym,
+                feature=feature,
+                tokens_used=tokens,
+            )
+            db.add(row)
+        else:
+            row.tokens_used += tokens
+
+        await db.commit()
+        await db.refresh(row)
+        used = row.tokens_used
+
+    exhausted = cap is not None and cap != -1 and used >= cap
+    return TokenUsageResult(tokens_used=used, exhausted=exhausted)

app/billing/tier_manager.py

@@ -25,6 +25,12 @@ FEATURES: dict[str, dict[str, Any]] = {
         "providers": 1,
         "batch_builder": False,
         "sso": False,
+        "real_embeddings": False,       # keyword fallback only
+        "realtime_extraction": False,   # batch queue (Phase 2)
+        "relational_memory": False,     # relational tier (Phase 3) — Pro+
+        "proactive_mining": False,      # Power+ only (Phase 5)
+        "folder_max_files": 200,
+        "folder_monthly_tokens": 100_000,
     },
     "pro": {
         "agents": -1,           # unlimited
@@ -33,6 +39,12 @@ FEATURES: dict[str, dict[str, Any]] = {
         "providers": -1,
         "batch_builder": False,
         "sso": False,
+        "real_embeddings": True,        # pgvector cosine search
+        "realtime_extraction": True,    # fire-and-forget asyncio.create_task
+        "relational_memory": True,      # person/project predicates
+        "proactive_mining": False,      # Power+ only (Phase 5)
+        "folder_max_files": 5000,
+        "folder_monthly_tokens": 2_000_000,
     },
     "power": {
         "agents": -1,
@@ -41,6 +53,12 @@ FEATURES: dict[str, dict[str, Any]] = {
         "providers": -1,
         "batch_builder": True,
         "sso": False,
+        "real_embeddings": True,
+        "realtime_extraction": True,
+        "relational_memory": True,      # all predicates incl. custom
+        "proactive_mining": True,       # scheduled pattern mining (Phase 5)
+        "folder_max_files": -1,         # unlimited
+        "folder_monthly_tokens": -1,    # unlimited
     },
     "team": {
         "agents": -1,
@@ -49,6 +67,12 @@ FEATURES: dict[str, dict[str, Any]] = {
         "providers": -1,
         "batch_builder": True,
         "sso": True,
+        "real_embeddings": True,
+        "realtime_extraction": True,
+        "relational_memory": True,      # all predicates incl. custom
+        "proactive_mining": True,       # scheduled pattern mining (Phase 5)
+        "folder_max_files": -1,         # unlimited
+        "folder_monthly_tokens": -1,    # unlimited
     },
 }
 
@@ -107,6 +131,13 @@ class TierManager:
             )
             raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=detail)
 
+    def get_feature_value(self, tier: BillingTier, feature: str) -> int:
+        """Return integer feature value for tier. -1 means unlimited."""
+        value = FEATURES.get(tier, FEATURES["free"]).get(feature)
+        if not isinstance(value, int):
+            return 0
+        return value
+
     # ── Rate limiting ────────────────────────────────────────────────────
 
     def get_rate_limit(self, tier: BillingTier) -> int:

app/config/settings.py

@@ -16,17 +16,23 @@ class Settings(BaseSettings):
     ANTHROPIC_API_KEY: str = ""
     GOOGLE_API_KEY: str = ""
     CEREBRAS_API_KEY: str = ""
+    GROQ_API_KEY: str = ""
+    DEEPSEEK_API_KEY: str = ""
 
     LLM_MODEL: str = "gpt-4o"
     LLM_EMBED_MODEL: str = "text-embedding-3-small"
 
     # Per-agent model overrides. Leave empty to fall back to LLM_MODEL.
-    LLM_MODEL_CLASSIFIER: str = ""        # _infer_floating_domain (intent routing)
+    LLM_MODEL_CLASSIFIER: str = ""        # classifier (intent routing, future use)
     LLM_MODEL_HOME_AGENT: str = ""        # home-agent (run_single_agent / stream)
-    LLM_MODEL_FLOATING_AGENT: str = ""    # floating-agent (contextual chat)
     LLM_MODEL_UNIFIED_PROCESSOR: str = "" # unified-processor (agent_runner)
     LLM_MODEL_CLOUD_PROCESSOR: str = ""   # cloud-processor (agent_runner)
-    LLM_MODEL_SETUP_AGENT: str = ""       # agent-setup journey
+    LLM_MODEL_BRIEF_AGENT: str = ""            # brief-agent (home + project text briefs)
+    LLM_MODEL_TASK_BRIEF_AGENT: str = ""      # task-brief-agent (per-task deep research)
+    LLM_MODEL_SETUP_AGENT: str = ""           # agent-setup journey
+    LLM_MODEL_MEMORY_EXTRACTOR: str = ""  # memory-extractor (Phase 2 extract/decide)
+    LLM_MODEL_MEMORY_MINER: str = ""      # memory-miner (Phase 5 proactive mining)
+    LLM_MODEL_MEMORY_AUDITOR: str = ""    # memory-auditor (Phase 7 weekly audit)
 
     # GitHub Copilot OAuth token storage directory.
     # Leave empty to use the LiteLLM default (~/.config/litellm/github_copilot).
@@ -69,9 +75,11 @@ class Settings(BaseSettings):
     LANGFUSE_PUBLIC_KEY: str = ""
     LANGFUSE_BASE_URL: str = "https://cloud.langfuse.com"
 
+    SCHEDULER_ENABLED: bool = True
+
     ENV: Literal["dev", "prod"] = "dev"
 
-    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8")
+    model_config = SettingsConfigDict(env_file=".env", env_file_encoding="utf-8", extra="ignore")
 
 
 settings = Settings()

app/core/brief_agent.py

@@ -0,0 +1,228 @@
+"""Brief agent — produces plain-text home and project status briefs.
+
+Read-only tool subset only. Never calls _normalize_tagged_list_lines —
+the brief prompt forbids XML tags, so skipping post-processing is intentional.
+"""
+
+from __future__ import annotations
+
+from collections.abc import AsyncGenerator
+from datetime import date
+from typing import Any
+
+from app.agents.note_agent import NOTE_READ_TOOLS
+from app.agents.project_agent import PROJECT_READ_TOOLS
+from app.agents.task_agent import TASK_READ_TOOLS
+from app.agents.timeline_agent import TIMELINE_READ_TOOLS
+from app.core.deep_agent import (
+    _language_instruction,
+    _proactive_hints_injection,
+    _read_only_memory_tools,
+    _relational_memory_injection,
+    _run_single_agent_stream,
+    _trace_id_from_context,
+    build_brief_multi_project_manifest,
+)
+from app.core.langfuse_client import compile_prompt, get_prompt_or_fallback
+
+_LANGUAGE_NAMES: dict[str, str] = {
+    "en": "English", "it": "Italian", "es": "Spanish",
+    "fr": "French", "de": "German",
+    "english": "English", "italian": "Italian", "italiano": "Italian",
+    "spanish": "Spanish", "español": "Spanish",
+    "french": "French", "français": "French",
+    "german": "German", "deutsch": "German",
+}
+
+_HOME_BRIEF_FALLBACK = """\
+You are the user's personal assistant producing a short daily brief.
+
+ROLE
+Act like a calm, attentive secretary writing a stand-up note for your boss.
+Warm and human, never breezy. Never cheerful filler, never emojis, never
+"here is your brief" meta-text. The user is opening the app mid-workday and
+is probably stressed — your job is to lower cognitive load, not add noise.
+
+TOOLS — always call before writing
+Pull fresh data every run. Do not invent counts or titles. Use at minimum:
+- list_tasks_due_today — tasks the user owes today
+- list_timelines_today — events starting or ending today
+- list_all_projects — projects currently in progress or at risk
+- memory_list_blocks / memory_get — personal context about people, clients,
+  payment habits, working preferences
+If a tool returns nothing, simply omit that topic. Never report zeros.
+
+WHAT TO INCLUDE
+1. Tasks due today (title + priority; group the 1-2 most important).
+2. Timeline events starting or ending today (and anything that starts/ends
+   tomorrow if the user has a very light day).
+3. Active projects that need a nudge — stalled, blocked, or awaiting input.
+4. Memory-aware colour where it sharpens the brief. Examples:
+   - "Client Rossi tends to pay late — the Acme invoice is 6 days out."
+   - "You usually dislike meetings before 10:00 — the call at 09:30 is unusual."
+   Only add a memory line when it changes what the user does. Do not pad.
+
+WHAT TO OMIT
+- Zero-counts ("no overdue items", "0 meetings today").
+- Statistics ("2 active projects, 3 completed tasks").
+- Headers, titles, greetings, sign-offs, dates, emojis, slang.
+- Meta-phrases ("here is", "let me know if", "hope this helps").
+- XML/HTML tags of any kind. Plain prose only.
+
+LIGHT-DAY CLAUSE
+If tasks + events + active-project-nudges together produce fewer than two
+sentences of content, also list 1-2 projects in status on_hold or waiting
+and ask a single, specific question about them — e.g. "Is the Bianchi
+redesign still paused, or ready to pick back up?" One question max, grounded
+in a real project name.
+
+VOICE
+- Calm. Concise. Human. Short sentences.
+- Use **bold** sparingly for task titles, project names, and people's names.
+- No bullet lists. Flow as 2-4 sentences of prose.
+
+LENGTH
+2-4 sentences total. Hard cap 4. If the day is truly empty, one sentence.
+
+Respond in the user's language ({language}). Today is {today}.\
+"""
+
+_PROJECT_BRIEF_FALLBACK = """\
+You are the project assistant producing a short status brief for ONE project.
+
+ROLE
+A senior project manager summarising state-of-play for the owner. Factual,
+sharp, forward-looking. Never reassuring filler, never emojis.
+
+SCOPE
+Work only with project_id = {project_id}. Do not mention or pull data from
+other projects. Use tools to fetch fresh data:
+- get_project — current status, dates, description
+- list_tasks(project_id) — open work, split by status
+- list_timelines(project_id) — milestones hit, upcoming, overdue
+- list_notes(project_id) — any recent decisions or blockers
+- memory_get — relevant context about the client, collaborators, constraints
+
+STRUCTURE — follow exactly, one short paragraph per section, no headers
+1. **State.** One sentence: current phase, health (on track / at risk / blocked),
+   and why. Cite the concrete signal (overdue milestone, stalled tasks, recent
+   blocker note).
+2. **What's moving.** What was completed or progressed recently. Name specific
+   tasks or milestones.
+3. **Next steps.** The 1-3 most important things the user should do next, in
+   priority order. Be concrete — task name, who owns it, when due if known.
+   If waiting on someone else, name them and what the ask is.
+4. **Risks / memory-flagged items.** One line max. Only include when there is
+   a real risk or a relevant memory (e.g. late-paying client, tight deadline,
+   scope change). Omit the section entirely if nothing to say.
+
+WHAT TO OMIT
+- Zero-counts ("no overdue tasks").
+- Generic advice ("keep up the good work").
+- Greetings, headers, bullet lists, emojis, sign-offs, meta-phrases.
+- XML/HTML tags or bracketed id lists. Plain prose only.
+
+VOICE
+- Direct. Factual. No fluff.
+- Use **bold** sparingly for task titles, milestone names, and the owner's name.
+- Short sentences. Prefer verbs over nouns ("Client review is blocking release"
+  not "There is a blocker which is the client review").
+
+LENGTH
+4-8 sentences total across the 3-4 sections. Hard cap 8.
+
+Respond in the user's language ({language}). Today is {today}.\
+"""
+
+
+def _resolve_language(context: dict[str, Any]) -> str:
+    core = context.get("core_memory") or {}
+    raw = (core.get("language") or "en").strip().lower()
+    return _LANGUAGE_NAMES.get(raw, raw.title()) or "English"
+
+
+def _build_read_tools(user_id: str, trace_id: str | None) -> list[Any]:
+    return [
+        *TASK_READ_TOOLS,
+        *PROJECT_READ_TOOLS,
+        *TIMELINE_READ_TOOLS,
+        *NOTE_READ_TOOLS,
+        *_read_only_memory_tools(user_id, trace_id),
+    ]
+
+
+async def run_home_brief(
+    user_id: str,
+    context: dict[str, Any],
+) -> AsyncGenerator[tuple[str, Any], None]:
+    """Stream a plain-text daily home brief.
+
+    Yields (event_type, data) tuples identical to _run_single_agent_stream.
+    Do NOT post-process output through _normalize_tagged_list_lines.
+    """
+    from app.agents.folder_agent import FOLDER_TOOLS
+
+    trace_id = _trace_id_from_context(context)
+    today = date.today().isoformat()
+    language = _resolve_language(context)
+
+    raw_template, langfuse_prompt = get_prompt_or_fallback("home_brief", _HOME_BRIEF_FALLBACK)
+    system_prompt = compile_prompt(raw_template, langfuse_prompt, language=language, today=today)
+    system_prompt += _relational_memory_injection(context)
+    system_prompt += _proactive_hints_injection(context)
+    system_prompt += _language_instruction(context)
+    if today not in system_prompt:
+        system_prompt += f"\nToday is {today}."
+
+    brief_manifest = await build_brief_multi_project_manifest()
+    system_prompt = system_prompt + ("\n\n" + brief_manifest if brief_manifest else "")
+
+    tools = [*_build_read_tools(user_id, trace_id), *FOLDER_TOOLS]
+    async for event in _run_single_agent_stream(
+        user_id=user_id,
+        system_prompt=system_prompt,
+        message="Generate the daily brief.",
+        context=context,
+        langfuse_prompt=langfuse_prompt,
+        agent_name="brief-agent",
+        tools=tools,
+    ):
+        yield event
+
+
+async def run_project_brief(
+    user_id: str,
+    project_id: str,
+    context: dict[str, Any],
+) -> AsyncGenerator[tuple[str, Any], None]:
+    """Stream a plain-text project status brief for project_id.
+
+    Yields (event_type, data) tuples identical to _run_single_agent_stream.
+    Do NOT post-process output through _normalize_tagged_list_lines.
+    """
+    trace_id = _trace_id_from_context(context)
+    today = date.today().isoformat()
+    language = _resolve_language(context)
+
+    raw_template, langfuse_prompt = get_prompt_or_fallback("project_brief", _PROJECT_BRIEF_FALLBACK)
+    system_prompt = compile_prompt(
+        raw_template, langfuse_prompt,
+        language=language, today=today, project_id=project_id,
+    )
+    system_prompt += _relational_memory_injection(context)
+    system_prompt += _proactive_hints_injection(context)
+    system_prompt += _language_instruction(context)
+    if today not in system_prompt:
+        system_prompt += f"\nToday is {today}."
+
+    tools = _build_read_tools(user_id, trace_id)
+    async for event in _run_single_agent_stream(
+        user_id=user_id,
+        system_prompt=system_prompt,
+        message=f"Generate the project status brief for project {project_id}.",
+        context=context,
+        langfuse_prompt=langfuse_prompt,
+        agent_name="brief-agent",
+        tools=tools,
+    ):
+        yield event

app/core/embeddings.py

@@ -0,0 +1,34 @@
+"""OpenAI embedding helper for associative memory tier.
+
+Single public function: ``embed_text(text) -> list[float] | None``.
+Returns None on any failure — callers must implement a keyword fallback.
+Never raises; all exceptions are logged as warnings.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from openai import AsyncOpenAI
+
+logger = logging.getLogger(__name__)
+
+_MAX_INPUT_CHARS = 8000
+_EMBEDDING_MODEL = "text-embedding-3-small"
+
+
+async def embed_text(text: str) -> list[float] | None:
+    """Call OpenAI text-embedding-3-small. Return None on failure (caller falls back to keyword)."""
+    try:
+        client = AsyncOpenAI()
+        truncated = text[:_MAX_INPUT_CHARS]
+        response = await client.embeddings.create(
+            input=truncated,
+            model=_EMBEDDING_MODEL,
+        )
+        result: list[float] = response.data[0].embedding
+        logger.debug("embeddings: embed_text dims=%d", len(result))
+        return result
+    except Exception as exc:
+        logger.warning("embeddings: embed_text failed: %s", exc)
+        return None

app/core/folder_indexer.py

@@ -0,0 +1,183 @@
+"""Per-file summarisation for project folder integration."""
+from __future__ import annotations
+
+import base64
+import io
+from dataclasses import dataclass
+
+from langchain_core.messages import HumanMessage, SystemMessage
+from pypdf import PdfReader
+from docx import Document as DocxDocument
+
+from app.core.langfuse_client import (
+    compile_prompt,
+    extract_usage,
+    get_langfuse,
+    get_prompt_or_fallback,
+)
+from app.core.llm import get_llm
+
+_TEXT_FALLBACK = (
+    "You are summarising a file for an AI assistant that helps the user manage a project.\n"
+    "Produce a single sentence (<=30 words, <=200 chars) that captures the file's purpose "
+    "and most important detail.\nFile extension: {ext}\nFile name: {name}\nContent (truncated if long):\n{content}"
+)
+_IMAGE_FALLBACK = (
+    "You are summarising an image attached to a project folder.\n"
+    "Produce a single sentence (<=30 words, <=200 chars) describing what the image shows "
+    "and any obvious purpose (logo, screenshot, diagram, photo of a whiteboard, etc.)."
+)
+_MAX_INPUT_CHARS = 6000
+
+
+@dataclass
+class IndexResult:
+    summary: str
+    tokens_used: int
+
+
+async def _llm_text(messages: list) -> object:
+    """Make the LLM call for text summarisation.
+
+    Defined as a standalone async function so tests can patch it cleanly
+    without needing to mock the LLM object itself.
+    """
+    llm = get_llm(model="gpt-4o-mini", temperature=0.2)
+    return await llm.ainvoke(messages)
+
+
+async def _llm_vision(messages: list) -> object:
+    """Make the LLM call for vision (image) summarisation.
+
+    Accepts the message list and returns the response directly, mirroring
+    the ``_llm_text`` caller pattern so tests can patch it at the module level.
+    """
+    llm = get_llm(model="gpt-4o-mini", temperature=0.2)
+    return await llm.ainvoke(messages)
+
+
+async def summarize_image(*, image_b64: str, mime: str, file_name: str | None = None) -> IndexResult:
+    """Return a compact summary of an image file using vision.
+
+    Parameters
+    ----------
+    image_b64:
+        Base64-encoded image bytes.
+    mime:
+        MIME type of the image, e.g. ``"image/png"``.
+    file_name:
+        Optional file name, attached to the Langfuse trace as input metadata.
+    """
+    template, prompt_obj = get_prompt_or_fallback("folder_file_summary_image", _IMAGE_FALLBACK)
+    messages = [
+        SystemMessage(content=template),
+        HumanMessage(content=[
+            {"type": "text", "text": "Summarise this image."},
+            {"type": "image_url", "image_url": {"url": f"data:{mime};base64,{image_b64}"}},
+        ]),
+    ]
+    lf = get_langfuse()
+    if lf is not None:
+        with lf.start_as_current_observation(
+            as_type="generation",
+            name="folder-summarize-image",
+            model="gpt-4o-mini",
+            prompt=prompt_obj,
+            input={"file_name": file_name, "mime": mime},
+        ) as gen:
+            response = await _llm_vision(messages)
+            usage = extract_usage(response)
+            gen.update(output=response.content, usage_details=usage)
+    else:
+        response = await _llm_vision(messages)
+        usage = extract_usage(response)
+    summary = (response.content or "").strip()[:500]
+    return IndexResult(summary=summary, tokens_used=usage.get("total", 0))
+
+
+async def summarize_text(*, content: str, ext: str, name: str) -> IndexResult:
+    """Return a compact summary of a text file.
+
+    Parameters
+    ----------
+    content:
+        Raw text content of the file (will be truncated to _MAX_INPUT_CHARS).
+    ext:
+        File extension including the leading dot, e.g. ``".md"``.
+    name:
+        File name, e.g. ``"kickoff.md"``.
+    """
+    template, prompt_obj = get_prompt_or_fallback("folder_file_summary_text", _TEXT_FALLBACK)
+    truncated = content[:_MAX_INPUT_CHARS]
+    compiled = compile_prompt(template, prompt_obj, ext=ext, name=name, content=truncated)
+    messages = [
+        SystemMessage(content=compiled),
+        HumanMessage(content="Summarise this file."),
+    ]
+    lf = get_langfuse()
+    if lf is not None:
+        with lf.start_as_current_observation(
+            as_type="generation",
+            name="folder-summarize-text",
+            model="gpt-4o-mini",
+            prompt=prompt_obj,
+            input={"file_name": name, "ext": ext, "content_chars": len(truncated)},
+        ) as gen:
+            response = await _llm_text(messages)
+            usage = extract_usage(response)
+            gen.update(output=response.content, usage_details=usage)
+    else:
+        response = await _llm_text(messages)
+        usage = extract_usage(response)
+    summary = (response.content or "").strip()[:500]
+    return IndexResult(summary=summary, tokens_used=usage.get("total", 0))
+
+
+def _extract_pdf_text(pdf_b64: str) -> str:
+    buf = io.BytesIO(base64.b64decode(pdf_b64))
+    reader = PdfReader(buf)
+    parts: list[str] = []
+    for page in reader.pages:
+        try:
+            parts.append(page.extract_text() or "")
+        except Exception:
+            continue
+    return "\n".join(parts).strip()
+
+
+def _extract_docx_text(docx_b64: str) -> str:
+    buf = io.BytesIO(base64.b64decode(docx_b64))
+    doc = DocxDocument(buf)
+    return "\n".join(p.text for p in doc.paragraphs if p.text).strip()
+
+
+async def summarize_pdf(*, pdf_b64: str, name: str) -> IndexResult:
+    """Return a compact summary of a PDF file.
+
+    Parameters
+    ----------
+    pdf_b64:
+        Base64-encoded PDF bytes.
+    name:
+        File name, e.g. ``"report.pdf"``.
+    """
+    text = _extract_pdf_text(pdf_b64)
+    if not text:
+        return IndexResult(summary="Could not extract text", tokens_used=0)
+    return await summarize_text(content=text, ext=".pdf", name=name)
+
+
+async def summarize_docx(*, docx_b64: str, name: str) -> IndexResult:
+    """Return a compact summary of a DOCX file.
+
+    Parameters
+    ----------
+    docx_b64:
+        Base64-encoded DOCX bytes.
+    name:
+        File name, e.g. ``"spec.docx"``.
+    """
+    text = _extract_docx_text(docx_b64)
+    if not text:
+        return IndexResult(summary="Could not extract text", tokens_used=0)
+    return await summarize_text(content=text, ext=".docx", name=name)

app/core/llm.py

@@ -51,6 +51,10 @@ def _api_key_for_model(model: str) -> str | None:
         return settings.GOOGLE_API_KEY or None
     if model.startswith("cerebras/"):
         return settings.CEREBRAS_API_KEY or None
+    if model.startswith("groq/"):
+        return settings.GROQ_API_KEY or None
+    if model.startswith("deepseek/"):
+        return settings.DEEPSEEK_API_KEY or None
     if model.startswith("github_copilot/"):
         # GitHub Copilot uses OAuth device-flow tokens managed by LiteLLM.
         # No API key is required; returning None lets LiteLLM handle auth.
@@ -99,10 +103,15 @@ def get_llm(
 _AGENT_MODEL_SETTINGS: dict[str, Callable[[], str]] = {
     "classifier":          lambda: settings.LLM_MODEL_CLASSIFIER or settings.LLM_MODEL,
     "home-agent":          lambda: settings.LLM_MODEL_HOME_AGENT or settings.LLM_MODEL,
-    "floating-agent":      lambda: settings.LLM_MODEL_FLOATING_AGENT or settings.LLM_MODEL,
     "unified-processor":   lambda: settings.LLM_MODEL_UNIFIED_PROCESSOR or settings.LLM_MODEL,
     "cloud-processor":     lambda: settings.LLM_MODEL_CLOUD_PROCESSOR or settings.LLM_MODEL,
+    "brief-agent":         lambda: settings.LLM_MODEL_BRIEF_AGENT or settings.LLM_MODEL,
+    "task-brief-agent":    lambda: settings.LLM_MODEL_TASK_BRIEF_AGENT or settings.LLM_MODEL,
     "setup":               lambda: settings.LLM_MODEL_SETUP_AGENT or settings.LLM_MODEL,
+    "memory-extractor":    lambda: settings.LLM_MODEL_MEMORY_EXTRACTOR or "gpt-4o-mini",
+    "memory-miner":        lambda: settings.LLM_MODEL_MEMORY_MINER or "gpt-4o-mini",
+    "memory-auditor":      lambda: settings.LLM_MODEL_MEMORY_AUDITOR or settings.LLM_MODEL,
+    "note-summarizer":     lambda: "gpt-4o-mini",
 }
 
 

app/core/memory_extraction.py

@@ -0,0 +1,450 @@
+"""Mem0-style Extract/Update pipeline — Phase 2.
+
+Runs after every ``store_episode`` call to distil durable facts, preferences,
+routines, and relations from the latest conversation turn.
+
+Entry point: ``run_extraction(db, user_id, last_user_msg, last_assistant_msg, session_id)``
+
+Design notes
+------------
+- Two gpt-4o-mini calls per turn: extract candidates, then decide action per candidate.
+- Short-circuit: if no existing neighbours → ADD without a second LLM call (cost saving).
+- Zero-trust: never logs decrypted user content; relation subject/object labels are
+  treated as identifiers (safe to log per spec).
+- Must not raise into the request path — caller wraps in asyncio.create_task().
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Any, Literal
+
+from pydantic import BaseModel, Field
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.langfuse_client import get_langfuse, get_prompt_or_fallback, extract_usage, langfuse_context
+from app.core.llm import get_agent_llm, model_for_agent
+
+logger = logging.getLogger(__name__)
+
+# ── Fallback prompts (used when Langfuse unavailable) ─────────────────────────
+
+_EXTRACTION_FALLBACK = (
+    "You are a memory extractor for a personal AI secretary. Given the last conversation "
+    "turn, the user's core memory, and recent episode summaries, identify durable facts, "
+    "preferences, routines, and person/project relations worth remembering.\n\n"
+    "Output JSON matching this schema exactly:\n"
+    '{{"candidates": [{{"type": "<fact|preference|relation|routine>", '
+    '"content": "<short canonical statement>", '
+    '"target_tier": "<core|associative|relational|proactive>", '
+    '"subject": null, "predicate": null, "object": null, "confidence": 0.7}}]}}\n\n'
+    "Rules:\n"
+    "- Skip small talk, greetings, one-off questions.\n"
+    "- Max 5 candidates per call.\n"
+    "- Only extract durable information (still true next week).\n"
+    "- For type=relation: subject/predicate/object required.\n"
+    "- Default confidence=0.7.\n\n"
+    "## Last turn\n{last_turn}\n\n"
+    "## Core memory (current)\n{core_memory}\n\n"
+    "## Recent episodes\n{recent_episodes}"
+)
+
+_DECIDE_FALLBACK = (
+    "You are a memory update decision engine. Given a new memory candidate and a list of "
+    "existing memories from the same tier, decide what action to take.\n\n"
+    "Respond with exactly one word: ADD, UPDATE, DELETE, or NOOP.\n\n"
+    "- ADD: new information not in existing memories.\n"
+    "- UPDATE: contradicts or supersedes an existing memory.\n"
+    "- DELETE: states something is no longer true.\n"
+    "- NOOP: already captured accurately.\n\n"
+    "## New candidate\n{candidate}\n\n"
+    "## Existing memories (same tier, top neighbours)\n{existing_memories}"
+)
+
+
+# ── Pydantic schemas ───────────────────────────────────────────────────────────
+
+class MemoryCandidate(BaseModel):
+    type: Literal["fact", "preference", "relation", "routine"]
+    content: str
+    target_tier: Literal["core", "associative", "relational", "proactive"]
+    subject: str | None = None
+    predicate: str | None = None
+    object: str | None = None
+    confidence: float = Field(default=0.7, ge=0.0, le=1.0)
+
+
+class ExtractionResult(BaseModel):
+    candidates: list[MemoryCandidate] = Field(default_factory=list)
+
+
+# ── Task 2.1 — Extract candidates ─────────────────────────────────────────────
+
+async def extract_candidates(
+    last_turn: str,
+    core_memory: dict[str, str],
+    recent_episodes: list[str],
+) -> ExtractionResult:
+    """Call gpt-4o-mini to extract memory candidates from the latest turn.
+
+    Returns an ExtractionResult (may be empty on failure — never raises).
+    """
+    core_str = "\n".join(f"{k}: {v}" for k, v in core_memory.items()) or "(empty)"
+    episodes_str = "\n---\n".join(recent_episodes[-5:]) or "(none)"
+
+    template, prompt_obj = get_prompt_or_fallback("memory_extraction", _EXTRACTION_FALLBACK)
+
+    # Compile with Langfuse variable syntax ({{var}}) or fallback {var}
+    if prompt_obj is not None:
+        try:
+            system_text = prompt_obj.compile(
+                last_turn=last_turn,
+                core_memory=core_str,
+                recent_episodes=episodes_str,
+            )
+            if isinstance(system_text, list):
+                system_text = "\n".join(m.get("content", "") for m in system_text if isinstance(m, dict))
+        except Exception as exc:
+            logger.warning("memory_extraction: compile failed: %s", exc)
+            system_text = template.format(
+                last_turn=last_turn,
+                core_memory=core_str,
+                recent_episodes=episodes_str,
+            )
+    else:
+        system_text = template.format(
+            last_turn=last_turn,
+            core_memory=core_str,
+            recent_episodes=episodes_str,
+        )
+
+    llm = get_agent_llm("memory-extractor", temperature=0)
+    # Bind JSON mode so the model always returns parseable output.
+    llm_json = llm.bind(response_format={"type": "json_object"})  # type: ignore[attr-defined]
+
+    lf = get_langfuse()
+    try:
+        from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+        messages = [
+            SystemMessage(content=system_text),
+            HumanMessage(content="Extract memory candidates as JSON."),
+        ]
+
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="memory-extraction",
+                model=model_for_agent("memory-extractor"),
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm_json.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm_json.ainvoke(messages)
+
+        raw = json.loads(response.content)
+        result = ExtractionResult.model_validate(raw)
+        logger.info("memory_extraction: extracted %d candidates", len(result.candidates))
+        return result
+
+    except Exception as exc:
+        logger.warning("memory_extraction: extract_candidates failed: %s", exc)
+        return ExtractionResult(candidates=[])
+
+
+# ── Task 2.2 — Decide action ──────────────────────────────────────────────────
+
+async def decide_action(
+    candidate: MemoryCandidate,
+    existing: list[str],
+) -> Literal["ADD", "UPDATE", "DELETE", "NOOP"]:
+    """Decide what to do with a candidate given existing memories in the same tier.
+
+    Short-circuits to ADD without an LLM call when existing is empty (cost saving).
+    Never raises.
+    """
+    if not existing:
+        return "ADD"
+
+    candidate_str = f"[{candidate.type}] {candidate.content}"
+    existing_str = "\n".join(f"- {m}" for m in existing)
+
+    template, prompt_obj = get_prompt_or_fallback("memory_decide_action", _DECIDE_FALLBACK)
+
+    if prompt_obj is not None:
+        try:
+            system_text = prompt_obj.compile(
+                candidate=candidate_str,
+                existing_memories=existing_str,
+            )
+            if isinstance(system_text, list):
+                system_text = "\n".join(m.get("content", "") for m in system_text if isinstance(m, dict))
+        except Exception as exc:
+            logger.warning("memory_extraction: decide compile failed: %s", exc)
+            system_text = template.format(candidate=candidate_str, existing_memories=existing_str)
+    else:
+        system_text = template.format(candidate=candidate_str, existing_memories=existing_str)
+
+    llm = get_agent_llm("memory-extractor", temperature=0)
+    lf = get_langfuse()
+
+    try:
+        from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+        messages = [
+            SystemMessage(content=system_text),
+            HumanMessage(content="Decide action."),
+        ]
+
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="memory-decide-action",
+                model=model_for_agent("memory-extractor"),
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm.ainvoke(messages)
+
+        verb = response.content.strip().upper()
+        if verb in ("ADD", "UPDATE", "DELETE", "NOOP"):
+            return verb  # type: ignore[return-value]
+        logger.warning("memory_extraction: unexpected decide verb=%r, defaulting ADD", verb)
+        return "ADD"
+
+    except Exception as exc:
+        logger.warning("memory_extraction: decide_action failed: %s", exc)
+        return "ADD"
+
+
+# ── Task 2.3 — Pipeline orchestrator ──────────────────────────────────────────
+
+async def run_extraction(
+    db: AsyncSession,
+    user_id: str,
+    last_user_msg: str,
+    last_assistant_msg: str,
+    session_id: str | None,
+) -> None:
+    """Full Mem0-style extract/update pipeline for one conversation turn.
+
+    Steps:
+    1. Load core memory + last 5 episodes.
+    2. extract_candidates() → up to 5 MemoryCandidate objects.
+    3. For each candidate: find top-3 neighbours → decide_action() → apply.
+    4. Trace via Langfuse.
+
+    Never raises — wraps everything in try/except.
+    """
+    try:
+        await _run_extraction_inner(db, user_id, last_user_msg, last_assistant_msg, session_id)
+    except Exception as exc:
+        logger.warning("memory_extraction: run_extraction failed user=%s: %s", user_id, exc)
+
+
+async def _run_extraction_inner(
+    db: AsyncSession,
+    user_id: str,
+    last_user_msg: str,
+    last_assistant_msg: str,
+    session_id: str | None,
+) -> None:
+    from app.core.memory_middleware import MemoryMiddleware  # noqa: PLC0415
+
+    middleware = MemoryMiddleware(db)
+    fernet = await middleware._get_fernet(user_id)
+    if fernet is None:
+        logger.warning("memory_extraction: no fernet for user=%s, skipping", user_id)
+        return
+
+    # 1. Load context
+    core: dict[str, str] = await middleware._load_core(user_id, fernet)
+    episodes: list[str] = await middleware._load_episodic(user_id, fernet, session_id=session_id)
+
+    last_turn = f"User: {last_user_msg}\nAssistant: {last_assistant_msg}"
+
+    lf = get_langfuse()
+
+    async def _run(trace_id: str | None) -> dict[str, Any]:
+        # 2. Extract candidates
+        result = await extract_candidates(last_turn, core, episodes)
+        if not result.candidates:
+            logger.info("memory_extraction: no candidates user=%s", user_id)
+            return {"candidates": 0, "applied": 0}
+
+        logger.info(
+            "memory_extraction: processing %d candidates user=%s trace=%s",
+            len(result.candidates),
+            user_id,
+            trace_id or "-",
+        )
+
+        # 3. Apply each candidate
+        applied = 0
+        actions: list[str] = []
+        for candidate in result.candidates:
+            try:
+                await _apply_candidate(middleware, db, user_id, fernet, candidate, trace_id)
+                applied += 1
+                actions.append(f"{candidate.type}:{candidate.target_tier}")
+            except Exception as exc:
+                logger.warning(
+                    "memory_extraction: apply failed candidate=%r user=%s: %s",
+                    candidate.content[:80],
+                    user_id,
+                    exc,
+                )
+
+        logger.info(
+            "memory_extraction: applied %d/%d candidates user=%s",
+            applied,
+            len(result.candidates),
+            user_id,
+        )
+        return {"candidates": len(result.candidates), "applied": applied, "actions": actions}
+
+    with langfuse_context(user_id=user_id, session_id=session_id):
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="span",
+                name="memory-extraction-pipeline",
+                input={"last_turn_preview": last_turn[:200]},
+            ) as span:
+                summary = await _run(trace_id=span.id)
+                span.update(output=summary)
+            try:
+                lf.flush()
+            except Exception:
+                pass
+        else:
+            await _run(trace_id=None)
+
+
+async def _apply_candidate(
+    middleware: Any,
+    db: AsyncSession,
+    user_id: str,
+    fernet: Any,
+    candidate: MemoryCandidate,
+    trace_id: str | None,
+) -> None:
+    """Fetch neighbours, decide action, apply to the appropriate tier."""
+
+    neighbours: list[str] = []
+
+    if candidate.target_tier == "core":
+        # For core tier: neighbours are existing core block values for similar keys.
+        blocks = await middleware.list_core_blocks(user_id)
+        neighbours = [b["value"] for b in blocks[:3]]
+
+    elif candidate.target_tier == "associative":
+        neighbours = await middleware.search_archival(user_id, candidate.content, top_k=3)
+
+    elif candidate.target_tier == "relational":
+        # Relation candidates handled specially — passed to upsert_relation directly.
+        # Neighbours: search by subject label if available.
+        neighbours = []
+
+    elif candidate.target_tier == "proactive":
+        neighbours = await middleware.search_recall(user_id, candidate.content, top_k=3)
+
+    action = await decide_action(candidate, neighbours)
+    logger.info(
+        "memory_extraction: candidate type=%s tier=%s action=%s",
+        candidate.type,
+        candidate.target_tier,
+        action,
+    )
+
+    if action == "NOOP":
+        return
+
+    if candidate.target_tier == "relational":
+        # Always upsert relations — decide_action skipped (no neighbour search).
+        if candidate.subject and candidate.predicate and candidate.object:
+            await _upsert_relation(
+                middleware, db, user_id, candidate, trace_id
+            )
+        return
+
+    if action in ("ADD", "UPDATE"):
+        if candidate.target_tier == "core":
+            # Derive a short key from the content (first 40 chars, snake_cased).
+            key = _content_to_key(candidate.content)
+            await middleware.update_core(user_id, key, candidate.content, trace_id=trace_id)
+
+        elif candidate.target_tier == "associative":
+            await middleware.store_associative(user_id, candidate.content)
+
+        elif candidate.target_tier == "proactive":
+            await _store_proactive_stub(middleware, db, user_id, candidate, fernet)
+
+    elif action == "DELETE":
+        if candidate.target_tier == "core":
+            key = _content_to_key(candidate.content)
+            await middleware.delete_core(user_id, key)
+
+
+def _content_to_key(content: str) -> str:
+    """Derive a short snake_case key from a content string (first 40 chars)."""
+    import re  # noqa: PLC0415
+    slug = re.sub(r"[^a-z0-9]+", "_", content[:40].lower()).strip("_")
+    return slug or "memory"
+
+
+async def _upsert_relation(
+    middleware: Any,
+    db: AsyncSession,
+    user_id: str,
+    candidate: MemoryCandidate,
+    trace_id: str | None,
+) -> None:
+    """Upsert a relation row via MemoryMiddleware.upsert_relation (Phase 3)."""
+    await middleware.upsert_relation(
+        user_id=user_id,
+        subject=candidate.subject or "unknown",
+        subject_type="unknown",
+        predicate=candidate.predicate or "related_to",
+        object_=candidate.object or "unknown",
+        object_type="unknown",
+        confidence=candidate.confidence,
+    )
+    logger.info(
+        "memory_extraction: upserted relation subject=%s predicate=%s object=%s",
+        candidate.subject,
+        candidate.predicate,
+        candidate.object,
+    )
+
+
+async def _store_proactive_stub(
+    middleware: Any,
+    db: AsyncSession,
+    user_id: str,
+    candidate: MemoryCandidate,
+    fernet: Any,
+) -> None:
+    """Store a proactive pattern row directly (MemoryProactive model)."""
+    import uuid  # noqa: PLC0415
+    from app.models import MemoryProactive  # noqa: PLC0415
+    from app.core.memory_middleware import _encrypt  # noqa: PLC0415
+
+    encrypted = _encrypt(fernet, candidate.content)
+    row = MemoryProactive(
+        id=str(uuid.uuid4()),
+        user_id=user_id,
+        pattern_encrypted=encrypted,
+        confidence=candidate.confidence,
+        source="inferred",
+    )
+    db.add(row)
+    try:
+        await db.commit()
+        logger.info("memory_extraction: stored proactive pattern user=%s", user_id)
+    except Exception as exc:
+        logger.warning("memory_extraction: store proactive failed: %s", exc)
+        await db.rollback()

app/core/memory_maintenance.py

@@ -0,0 +1,581 @@
+"""Memory maintenance jobs — Phase 3/5.
+
+Three entrypoints called by the scheduler (APScheduler) registered in app/main.py:
+
+  drain_extraction_queue(db) — Free-tier batch extraction (Phase 2/5).
+  mine_proactive_patterns(db, user_id) — Power+ pattern mining (Phase 5).
+  decay_relations(db, user_id) — confidence decay + pruning for memory_relations (Phase 3).
+
+All are safe to call manually or from tests; they never raise.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import uuid
+from datetime import datetime, timedelta, timezone
+
+from cryptography.fernet import Fernet
+from sqlalchemy import select
+from sqlalchemy.ext.asyncio import AsyncSession
+
+from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback
+from app.models import MemoryAssociative, MemoryEpisodic, MemoryProactive, MemoryRelation, User
+
+logger = logging.getLogger(__name__)
+
+# Decay parameters for relations
+_DECAY_FACTOR = 0.95
+_DECAY_PERIOD_DAYS = 30
+_PRUNE_THRESHOLD = 0.2
+
+# Proactive pattern decay: 10 % per 7 days since last sighting
+_PROACTIVE_DECAY_FACTOR = 0.9
+_PROACTIVE_DECAY_PERIOD_DAYS = 7
+_PROACTIVE_PRUNE_THRESHOLD = 0.2
+
+# Mining: require at least this many episodes to attempt pattern extraction
+_MIN_EPISODES_FOR_MINING = 3
+_MINING_LOOKBACK_DAYS = 30
+
+# Audit: caps to control token cost
+_AUDIT_MAX_FACTS = 50
+_AUDIT_MAX_LABELS = 100
+
+
+async def decay_relations(db: AsyncSession, user_id: str) -> None:
+    """Apply confidence decay to all relation rows for a user.
+
+    Decay rule: confidence *= 0.95 for every 30 days since last_confirmed_at.
+    Rows whose confidence falls below 0.2 are deleted.
+
+    Never raises — wraps in try/except.
+    """
+    try:
+        await _decay_relations_inner(db, user_id)
+    except Exception as exc:
+        logger.warning("memory_maintenance: decay_relations failed user=%s: %s", user_id, exc)
+
+
+async def _decay_relations_inner(db: AsyncSession, user_id: str) -> None:
+    result = await db.execute(
+        select(MemoryRelation).where(MemoryRelation.user_id == user_id)
+    )
+    rows = result.scalars().all()
+    now = datetime.now(timezone.utc)
+    deleted = 0
+    decayed = 0
+
+    for row in rows:
+        reference = row.last_confirmed_at or row.created_at
+        if reference is None:
+            continue
+        if reference.tzinfo is None:
+            reference = reference.replace(tzinfo=timezone.utc)
+
+        days_elapsed = (now - reference).days
+        if days_elapsed < _DECAY_PERIOD_DAYS:
+            continue
+
+        periods = days_elapsed // _DECAY_PERIOD_DAYS
+        new_confidence = row.confidence * (_DECAY_FACTOR ** periods)
+
+        if new_confidence < _PRUNE_THRESHOLD:
+            await db.delete(row)
+            deleted += 1
+            logger.info(
+                "memory_maintenance: pruned relation id=%s user=%s subject=%s predicate=%s "
+                "confidence=%.3f (below threshold)",
+                row.id, user_id, row.subject_label, row.predicate, new_confidence,
+            )
+        else:
+            row.confidence = new_confidence
+            decayed += 1
+
+    try:
+        await db.commit()
+        logger.info(
+            "memory_maintenance: decay_relations user=%s decayed=%d deleted=%d",
+            user_id, decayed, deleted,
+        )
+    except Exception as exc:
+        logger.warning("memory_maintenance: decay_relations commit failed user=%s: %s", user_id, exc)
+        await db.rollback()
+
+
+async def drain_extraction_queue(db: AsyncSession) -> None:
+    """Process pending ExtractionQueue rows for Free-tier users.
+
+    Each row corresponds to a stored episode that should be fed through the
+    Mem0-style extraction pipeline. Rows are deleted after successful processing.
+    Never raises — wraps in try/except.
+    """
+    try:
+        await _drain_extraction_queue_inner(db)
+    except Exception as exc:
+        logger.warning("memory_maintenance: drain_extraction_queue failed: %s", exc)
+
+
+async def _drain_extraction_queue_inner(db: AsyncSession) -> None:
+    from app.models import ExtractionQueue  # noqa: PLC0415
+
+    result = await db.execute(select(ExtractionQueue))
+    rows = result.scalars().all()
+
+    if not rows:
+        logger.debug("memory_maintenance: drain_extraction_queue nothing to drain")
+        return
+
+    logger.info("memory_maintenance: drain_extraction_queue pending=%d", len(rows))
+
+    from app.core.memory_extraction import run_extraction  # noqa: PLC0415
+
+    processed = 0
+    for row in rows:
+        try:
+            await run_extraction(
+                db=db,
+                user_id=row.user_id,
+                last_user_msg="",
+                last_assistant_msg="",
+                session_id=None,
+            )
+            await db.delete(row)
+            await db.commit()
+            processed += 1
+        except Exception as exc:
+            logger.warning(
+                "memory_maintenance: drain failed row=%s user=%s: %s",
+                row.id, row.user_id, exc,
+            )
+            await db.rollback()
+
+    logger.info("memory_maintenance: drain_extraction_queue processed=%d/%d", processed, len(rows))
+
+
+async def mine_proactive_patterns(db: AsyncSession, user_id: str) -> None:
+    """Mine recurring behavioral patterns from last 30 days of episodes (Power+ only).
+
+    Steps:
+    1. Gate on proactive_mining tier feature.
+    2. Load + decrypt last 30 days of episodic summaries.
+    3. Call gpt-4o-mini to identify recurring patterns.
+    4. Encrypt and store each pattern in memory_proactive.
+    5. Apply decay to existing proactive rows.
+
+    Never raises — wraps in try/except.
+    """
+    try:
+        await _mine_proactive_patterns_inner(db, user_id)
+    except Exception as exc:
+        logger.warning("memory_maintenance: mine_proactive_patterns failed user=%s: %s", user_id, exc)
+
+
+async def _mine_proactive_patterns_inner(db: AsyncSession, user_id: str) -> None:
+    from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+
+    tier = await tier_manager.get_tier(user_id, db)
+    if not tier_manager.check_feature(tier, "proactive_mining"):
+        logger.debug("memory_maintenance: mine_proactive_patterns skipped (tier=%s)", tier)
+        return
+
+    # Load user Fernet key
+    result = await db.execute(select(User).where(User.id == user_id))
+    user = result.scalar_one_or_none()
+    if user is None or not user.encryption_key:
+        logger.warning("memory_maintenance: mine_proactive_patterns no encryption_key user=%s", user_id)
+        return
+
+    fernet = Fernet(user.encryption_key.encode())
+    cutoff = datetime.now(timezone.utc) - timedelta(days=_MINING_LOOKBACK_DAYS)
+
+    episodes_result = await db.execute(
+        select(MemoryEpisodic)
+        .where(
+            MemoryEpisodic.user_id == user_id,
+            MemoryEpisodic.created_at >= cutoff,
+        )
+        .order_by(MemoryEpisodic.created_at.asc())
+    )
+    episode_rows = episodes_result.scalars().all()
+
+    if len(episode_rows) < _MIN_EPISODES_FOR_MINING:
+        logger.info(
+            "memory_maintenance: mine_proactive_patterns skipped user=%s episodes=%d (< %d)",
+            user_id, len(episode_rows), _MIN_EPISODES_FOR_MINING,
+        )
+        return
+
+    summaries: list[str] = []
+    for ep in episode_rows:
+        try:
+            plaintext = fernet.decrypt(ep.summary_encrypted.encode()).decode()
+            summaries.append(plaintext)
+        except Exception:
+            pass
+
+    if not summaries:
+        return
+
+    patterns = await _extract_proactive_patterns(summaries)
+    if not patterns:
+        logger.info("memory_maintenance: mine_proactive_patterns user=%s no patterns extracted", user_id)
+        return
+
+    stored = 0
+    for pattern_text in patterns:
+        try:
+            encrypted = fernet.encrypt(pattern_text.encode()).decode()
+            row = MemoryProactive(
+                id=str(uuid.uuid4()),
+                user_id=user_id,
+                pattern_encrypted=encrypted,
+                confidence=0.7,
+                source="inferred",
+            )
+            db.add(row)
+            stored += 1
+        except Exception as exc:
+            logger.warning("memory_maintenance: failed to store pattern user=%s: %s", user_id, exc)
+
+    try:
+        await db.commit()
+        logger.info(
+            "memory_maintenance: mine_proactive_patterns user=%s stored=%d",
+            user_id, stored,
+        )
+    except Exception as exc:
+        logger.warning("memory_maintenance: mine_proactive_patterns commit failed user=%s: %s", user_id, exc)
+        await db.rollback()
+        return
+
+    await _decay_proactive_patterns(db, user_id, fernet)
+
+
+async def _extract_proactive_patterns(summaries: list[str]) -> list[str]:
+    """Call memory-miner LLM to identify recurring behavioral/temporal patterns."""
+    from app.core.llm import get_agent_llm  # noqa: PLC0415
+
+    llm = get_agent_llm("memory-miner", temperature=0)
+    combined = "\n---\n".join(summaries[-20:])  # cap at last 20 to control token usage
+    prompt = (
+        "You are analyzing conversation history for a personal AI secretary. "
+        "Identify 3-5 recurring temporal or behavioral patterns (e.g. 'always works late on Thursdays', "
+        "'prefers bullet-point summaries', 'frequently asks about Project Acme status'). "
+        "Return each pattern as a plain, short English sentence on its own line. "
+        "No numbering, no bullet points, no extra text.\n\n"
+        f"Conversation history:\n{combined}"
+    )
+    try:
+        response = await llm.ainvoke(prompt)
+        text = response.content if hasattr(response, "content") else str(response)
+        lines = [line.strip() for line in str(text).splitlines() if line.strip()]
+        return lines[:5]
+    except Exception as exc:
+        logger.warning("memory_maintenance: _extract_proactive_patterns LLM failed: %s", exc)
+        return []
+
+
+async def _decay_proactive_patterns(db: AsyncSession, user_id: str, fernet: Fernet) -> None:
+    """Decay confidence of existing proactive patterns; prune below threshold."""
+    result = await db.execute(
+        select(MemoryProactive).where(MemoryProactive.user_id == user_id)
+    )
+    rows = result.scalars().all()
+    now = datetime.now(timezone.utc)
+    deleted = 0
+    decayed = 0
+
+    for row in rows:
+        reference = row.created_at
+        if reference is None:
+            continue
+        if reference.tzinfo is None:
+            reference = reference.replace(tzinfo=timezone.utc)
+
+        days_elapsed = (now - reference).days
+        if days_elapsed < _PROACTIVE_DECAY_PERIOD_DAYS:
+            continue
+
+        periods = days_elapsed // _PROACTIVE_DECAY_PERIOD_DAYS
+        new_confidence = row.confidence * (_PROACTIVE_DECAY_FACTOR ** periods)
+
+        if new_confidence < _PROACTIVE_PRUNE_THRESHOLD:
+            await db.delete(row)
+            deleted += 1
+        else:
+            row.confidence = new_confidence
+            decayed += 1
+
+    try:
+        await db.commit()
+        logger.info(
+            "memory_maintenance: decay_proactive user=%s decayed=%d deleted=%d",
+            user_id, decayed, deleted,
+        )
+    except Exception as exc:
+        logger.warning("memory_maintenance: decay_proactive commit failed user=%s: %s", user_id, exc)
+        await db.rollback()
+
+
+# ── Phase 7: weekly memory audit ──────────────────────────────────────────────
+
+_AUDIT_CONTRADICTIONS_FALLBACK = (
+    "You are auditing a personal AI assistant's memory bank. "
+    "Each fact has an ID in brackets. "
+    "Find pairs that directly contradict each other "
+    "(e.g. 'prefers morning meetings' vs 'never schedules before noon'). "
+    "For each contradiction, pick the ID to DELETE (the older or less specific one). "
+    'Return ONLY a valid JSON array, no markdown fences: '
+    '[{{"delete": "<id>", "reason": "<one line>"}}]. '
+    "If no contradictions, return [].\n\n"
+    "Facts:\n{facts}"
+)
+
+_AUDIT_CANONICALIZE_FALLBACK = (
+    "You are auditing entity labels in a personal AI assistant's relational memory. "
+    "These are names of people, companies, projects, or topics. "
+    "Group labels that clearly refer to the same real-world entity "
+    "(e.g. 'giulia', 'Giulia', 'Giulia R.' → canonical 'Giulia'). "
+    "Return ONLY a valid JSON array, no markdown fences: "
+    '[{{"canonical": "<best label>", "variants": ["<v1>", "<v2>"]}}]. '
+    "Only include groups with at least one variant. Singletons: omit.\n\n"
+    "Labels:\n{labels}"
+)
+
+
+async def audit_memory(db: AsyncSession, user_id: str) -> None:
+    """Weekly audit: contradiction scan on associative facts + label canonicalization on relations.
+
+    Steps:
+    1. Decrypt up to _AUDIT_MAX_FACTS associative rows; send list to memory-auditor LLM.
+    2. LLM flags rows to delete (direct contradictions); hard-delete them.
+    3. Collect unique subject/object labels from memory_relations; ask LLM to group duplicates.
+    4. Rewrite variant labels to their canonical form in-place.
+
+    Never raises — wraps in try/except.
+    """
+    try:
+        await _audit_memory_inner(db, user_id)
+    except Exception as exc:
+        logger.warning("memory_maintenance: audit_memory failed user=%s: %s", user_id, exc)
+
+
+async def _audit_memory_inner(db: AsyncSession, user_id: str) -> None:
+    result = await db.execute(select(User).where(User.id == user_id))
+    user = result.scalar_one_or_none()
+    if user is None or not user.encryption_key:
+        logger.warning("memory_maintenance: audit_memory no encryption_key user=%s", user_id)
+        return
+
+    fernet = Fernet(user.encryption_key.encode())
+    await _scan_associative_contradictions(db, user_id, fernet)
+    await _canonicalize_relation_labels(db, user_id)
+
+
+async def _scan_associative_contradictions(
+    db: AsyncSession,
+    user_id: str,
+    fernet: Fernet,
+) -> None:
+    """Decrypt associative facts, ask LLM to flag contradictions, delete superseded rows."""
+    result = await db.execute(
+        select(MemoryAssociative)
+        .where(MemoryAssociative.user_id == user_id)
+        .order_by(MemoryAssociative.updated_at.desc())
+        .limit(_AUDIT_MAX_FACTS)
+    )
+    rows = result.scalars().all()
+    if len(rows) < 2:
+        return
+
+    id_to_text: dict[str, str] = {}
+    for row in rows:
+        try:
+            plaintext = fernet.decrypt(row.content_encrypted.encode()).decode()
+            id_to_text[row.id] = plaintext
+        except Exception:
+            pass
+
+    if len(id_to_text) < 2:
+        return
+
+    id_list = list(id_to_text.keys())
+    numbered = "\n".join(
+        f"{i + 1}. [{rid}] {id_to_text[rid]}" for i, rid in enumerate(id_list)
+    )
+
+    template, prompt_obj = get_prompt_or_fallback(
+        "memory_audit_contradictions", _AUDIT_CONTRADICTIONS_FALLBACK
+    )
+    system_text = compile_prompt(template, prompt_obj, facts=numbered)
+
+    from app.core.llm import get_agent_llm, model_for_agent  # noqa: PLC0415
+    from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+
+    llm = get_agent_llm("memory-auditor", temperature=0)
+    lf = get_langfuse()
+    messages = [
+        SystemMessage(content=system_text),
+        HumanMessage(content="Audit facts for contradictions."),
+    ]
+    try:
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="memory-audit-contradictions",
+                model=model_for_agent("memory-auditor"),
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm.ainvoke(messages)
+
+        text = response.content if hasattr(response, "content") else str(response)
+        deletions = json.loads(text.strip())
+        if not isinstance(deletions, list):
+            return
+    except Exception as exc:
+        logger.warning(
+            "memory_maintenance: _scan_associative_contradictions LLM/parse failed user=%s: %s",
+            user_id, exc,
+        )
+        return
+
+    deleted = 0
+    for item in deletions:
+        if not isinstance(item, dict):
+            continue
+        rid = item.get("delete")
+        if not rid or rid not in id_to_text:
+            continue
+        result2 = await db.execute(
+            select(MemoryAssociative).where(
+                MemoryAssociative.id == rid,
+                MemoryAssociative.user_id == user_id,
+            )
+        )
+        target = result2.scalar_one_or_none()
+        if target:
+            await db.delete(target)
+            deleted += 1
+            logger.info(
+                "memory_maintenance: audit deleted contradiction id=%s user=%s reason=%s",
+                rid, user_id, item.get("reason", ""),
+            )
+
+    if deleted:
+        try:
+            await db.commit()
+        except Exception as exc:
+            logger.warning(
+                "memory_maintenance: audit contradiction commit failed user=%s: %s", user_id, exc
+            )
+            await db.rollback()
+
+    logger.info(
+        "memory_maintenance: _scan_associative_contradictions user=%s deleted=%d", user_id, deleted
+    )
+
+
+async def _canonicalize_relation_labels(db: AsyncSession, user_id: str) -> None:
+    """Group near-duplicate entity labels in memory_relations and unify to canonical form."""
+    result = await db.execute(
+        select(MemoryRelation).where(MemoryRelation.user_id == user_id)
+    )
+    rows = result.scalars().all()
+    if not rows:
+        return
+
+    all_labels: set[str] = set()
+    for row in rows:
+        all_labels.add(row.subject_label)
+        all_labels.add(row.object_label)
+
+    labels_list = sorted(all_labels)[:_AUDIT_MAX_LABELS]
+    if len(labels_list) < 2:
+        return
+
+    labels_block = "\n".join(f"- {lbl}" for lbl in labels_list)
+    template, prompt_obj = get_prompt_or_fallback(
+        "memory_audit_canonicalize", _AUDIT_CANONICALIZE_FALLBACK
+    )
+    system_text = compile_prompt(template, prompt_obj, labels=labels_block)
+
+    from app.core.llm import get_agent_llm, model_for_agent  # noqa: PLC0415
+    from langchain_core.messages import HumanMessage, SystemMessage  # noqa: PLC0415
+
+    llm = get_agent_llm("memory-auditor", temperature=0)
+    lf = get_langfuse()
+    messages = [
+        SystemMessage(content=system_text),
+        HumanMessage(content="Canonicalize entity labels."),
+    ]
+    try:
+        if lf:
+            with lf.start_as_current_observation(
+                as_type="generation",
+                name="memory-audit-canonicalize",
+                model=model_for_agent("memory-auditor"),
+                prompt=prompt_obj,
+                input=messages,
+            ) as gen:
+                response = await llm.ainvoke(messages)
+                gen.update(output=response.content, usage=extract_usage(response))
+        else:
+            response = await llm.ainvoke(messages)
+
+        text = response.content if hasattr(response, "content") else str(response)
+        groups = json.loads(text.strip())
+        if not isinstance(groups, list):
+            return
+    except Exception as exc:
+        logger.warning(
+            "memory_maintenance: _canonicalize_relation_labels LLM/parse failed user=%s: %s",
+            user_id, exc,
+        )
+        return
+
+    # Build variant → canonical map
+    remap: dict[str, str] = {}
+    for group in groups:
+        if not isinstance(group, dict):
+            continue
+        canonical = group.get("canonical", "")
+        variants = group.get("variants") or []
+        if not canonical:
+            continue
+        for v in variants:
+            if isinstance(v, str) and v != canonical:
+                remap[v] = canonical
+
+    if not remap:
+        return
+
+    updated = 0
+    for row in rows:
+        changed = False
+        if row.subject_label in remap:
+            row.subject_label = remap[row.subject_label]
+            changed = True
+        if row.object_label in remap:
+            row.object_label = remap[row.object_label]
+            changed = True
+        if changed:
+            updated += 1
+
+    if updated:
+        try:
+            await db.commit()
+            logger.info(
+                "memory_maintenance: _canonicalize_relation_labels user=%s updated=%d",
+                user_id, updated,
+            )
+        except Exception as exc:
+            logger.warning(
+                "memory_maintenance: canonicalize commit failed user=%s: %s", user_id, exc
+            )
+            await db.rollback()

app/core/memory_middleware.py

@@ -18,8 +18,10 @@ Usage:
 
 from __future__ import annotations
 
+import asyncio
 import logging
 import uuid
+from datetime import datetime, timezone
 from typing import Any
 
 from cryptography.fernet import Fernet, InvalidToken
@@ -27,15 +29,22 @@ from sqlalchemy import select
 from sqlalchemy.ext.asyncio import AsyncSession
 
 from app.models import (
+    ExtractionQueue,
     MemoryAssociative,
     MemoryCore,
     MemoryEpisodic,
     MemoryProactive,
+    MemoryRelation,
     User,
 )
 
 logger = logging.getLogger(__name__)
 
+
+def _now() -> datetime:
+    return datetime.now(timezone.utc)
+
+
 # Tuning constants
 _ASSOCIATIVE_TOP_K = 5
 _EPISODIC_RECENT_N = 10
@@ -64,26 +73,31 @@ class MemoryMiddleware:
           associative_memory — [plaintext_content, ...]  (top-k by keyword match)
           episodic_memory    — [plaintext_summary, ...]  (most recent N)
           proactive_hints    — [plaintext_pattern, ...]  (above threshold)
+          relational_memory  — ["subject --predicate--> object", ...] (top 10, Pro+)
         """
         fernet = await self._get_fernet(user_id)
         if fernet is None:
             return {}
 
+        user_dbg = await self._get_user_debug(user_id)
+        user_tier: str = user_dbg.get("tier") or "free"
+
         core = await self._load_core(user_id, fernet)
-        associative = await self._load_associative(user_id, message, fernet)
+        associative = await self._load_associative(user_id, message, fernet, user_tier=user_tier)
         episodic = await self._load_episodic(user_id, fernet, session_id=session_id)
         proactive = await self._load_proactive(user_id, fernet)
+        relational = await self._load_relational(user_id, user_tier=user_tier)
 
-        user_dbg = await self._get_user_debug(user_id)
         logger.info(
-            "memory: enrich_context trace=%s user=%s tier=%s core=%d associative=%d episodic=%d proactive=%d",
+            "memory: enrich_context trace=%s user=%s tier=%s core=%d associative=%d episodic=%d proactive=%d relational=%d",
             trace_id or "-",
             user_id,
-            user_dbg.get("tier") or "-",
+            user_tier,
             len(core),
             len(associative),
             len(episodic),
             len(proactive),
+            len(relational),
         )
 
         return {
@@ -91,6 +105,7 @@ class MemoryMiddleware:
             "associative_memory": associative,
             "episodic_memory": episodic,
             "proactive_hints": proactive,
+            "relational_memory": relational,
         }
 
     async def store_episode(
@@ -104,7 +119,10 @@ class MemoryMiddleware:
         """Summarise and store a completed interaction in episodic memory.
 
         The summary is a simple heuristic concatenation (no LLM call) to keep
-        latency low. Full LLM summarisation can be added in a later step.
+        latency low. After committing the episode row, dispatches the Mem0-style
+        extraction pipeline:
+          - Pro/Power/Team → asyncio.create_task (fire-and-forget, realtime).
+          - Free → enqueue an ExtractionQueue row for the daily cron.
         """
         fernet = await self._get_fernet(user_id)
         if fernet is None:
@@ -113,26 +131,95 @@ class MemoryMiddleware:
         summary = f"User: {message[:200]}\nAssistant: {response[:200]}"
         encrypted = _encrypt(fernet, summary)
 
-        row = MemoryEpisodic(
+        episode = MemoryEpisodic(
             id=str(uuid.uuid4()),
             user_id=user_id,
             summary_encrypted=encrypted,
             session_id=session_id,
         )
-        self._db.add(row)
+        self._db.add(episode)
+        episode_id: str = episode.id
         try:
             await self._db.commit()
             user_dbg = await self._get_user_debug(user_id)
+            tier = user_dbg.get("tier") or "free"
             logger.info(
                 "memory: store_episode trace=%s user=%s tier=%s session=%s",
                 trace_id or "-",
                 user_id,
-                user_dbg.get("tier") or "-",
+                tier,
                 session_id,
             )
         except Exception as exc:
             logger.error("memory: store_episode failed user=%s: %s", user_id, exc)
             await self._db.rollback()
+            return
+
+        # ── Dispatch extraction pipeline (Phase 2) ────────────────────────────
+        await self._dispatch_extraction(
+            user_id=user_id,
+            episode_id=episode_id,
+            last_user_msg=message,
+            last_assistant_msg=response,
+            session_id=session_id,
+        )
+
+    async def _dispatch_extraction(
+        self,
+        user_id: str,
+        episode_id: str,
+        last_user_msg: str,
+        last_assistant_msg: str,
+        session_id: str | None,
+    ) -> None:
+        """Route extraction to realtime task or batch queue based on user tier."""
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+
+        tier = await tier_manager.get_tier(user_id, self._db)
+
+        if tier_manager.check_feature(tier, "realtime_extraction"):
+            # Pro/Power/Team: fire-and-forget in the background.
+            # Must open a fresh session — request session closes after handler returns.
+            from app.core.memory_extraction import run_extraction  # noqa: PLC0415
+            from app.db import async_session  # noqa: PLC0415
+
+            async def _task() -> None:
+                try:
+                    async with async_session() as fresh_db:
+                        await run_extraction(
+                            db=fresh_db,
+                            user_id=user_id,
+                            last_user_msg=last_user_msg,
+                            last_assistant_msg=last_assistant_msg,
+                            session_id=session_id,
+                        )
+                except Exception as exc:
+                    logger.warning(
+                        "memory: extraction task failed user=%s: %s", user_id, exc
+                    )
+
+            asyncio.create_task(_task())
+            logger.info("memory: realtime extraction dispatched user=%s", user_id)
+        else:
+            # Free tier: enqueue for daily batch cron.
+            queue_row = ExtractionQueue(
+                id=str(uuid.uuid4()),
+                user_id=user_id,
+                episode_id=episode_id,
+            )
+            self._db.add(queue_row)
+            try:
+                await self._db.commit()
+                logger.info(
+                    "memory: extraction enqueued (batch) user=%s episode=%s",
+                    user_id,
+                    episode_id,
+                )
+            except Exception as exc:
+                logger.warning(
+                    "memory: extraction queue insert failed user=%s: %s", user_id, exc
+                )
+                await self._db.rollback()
 
     async def update_core(self, user_id: str, key: str, value: str, trace_id: str | None = None) -> None:
         """Upsert a core memory key/value for a user."""
@@ -255,6 +342,143 @@ class MemoryMiddleware:
         logger.info("memory: replace_core user=%s label=%s changed=1", user_id, label)
         return True
 
+    async def store_associative(
+        self,
+        user_id: str,
+        content: str,
+        entity_type: str | None = None,
+        entity_id: str | None = None,
+    ) -> None:
+        """Store associative memory; embed if user tier has real_embeddings."""
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+        from app.core.embeddings import embed_text  # noqa: PLC0415
+
+        fernet = await self._get_fernet(user_id)
+        if fernet is None:
+            return
+
+        encrypted = _encrypt(fernet, content)
+
+        user_dbg = await self._get_user_debug(user_id)
+        user_tier = user_dbg.get("tier") or "free"
+
+        embedding: list[float] | None = None
+        if tier_manager.check_feature(user_tier, "real_embeddings"):
+            embedding = await embed_text(content)
+
+        row = MemoryAssociative(
+            id=str(uuid.uuid4()),
+            user_id=user_id,
+            content_encrypted=encrypted,
+            embedding=embedding,
+            entity_type=entity_type,
+            entity_id=entity_id,
+        )
+        self._db.add(row)
+        try:
+            await self._db.commit()
+            logger.info(
+                "memory: store_associative user=%s embedded=%s",
+                user_id,
+                embedding is not None,
+            )
+        except Exception as exc:
+            logger.error("memory: store_associative failed user=%s: %s", user_id, exc)
+            await self._db.rollback()
+
+    async def upsert_relation(
+        self,
+        user_id: str,
+        subject: str,
+        subject_type: str,
+        predicate: str,
+        object_: str,
+        object_type: str,
+        *,
+        confidence: float = 0.7,
+        source_episode_id: str | None = None,
+        notes: str | None = None,
+    ) -> None:
+        """Insert or update a relation row.  Matches on (user_id, subject_label, predicate, object_label).
+
+        subject_label / object_label are plaintext entity identifiers — not encrypted.
+        notes is optional; encrypted with user Fernet if provided.
+        """
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+
+        user_dbg = await self._get_user_debug(user_id)
+        user_tier = user_dbg.get("tier") or "free"
+        if not tier_manager.check_feature(user_tier, "relational_memory"):
+            logger.debug("memory: upsert_relation skipped (tier=%s no relational_memory)", user_tier)
+            return
+
+        notes_encrypted: bytes | None = None
+        if notes:
+            fernet = await self._get_fernet(user_id)
+            if fernet:
+                notes_encrypted = fernet.encrypt(notes.encode())
+
+        result = await self._db.execute(
+            select(MemoryRelation).where(
+                MemoryRelation.user_id == user_id,
+                MemoryRelation.subject_label == subject,
+                MemoryRelation.predicate == predicate,
+                MemoryRelation.object_label == object_,
+            )
+        )
+        existing = result.scalar_one_or_none()
+
+        if existing is not None:
+            existing.subject_type = subject_type
+            existing.object_type = object_type
+            existing.confidence = confidence
+            existing.last_confirmed_at = _now()
+            if notes_encrypted is not None:
+                existing.notes_encrypted = notes_encrypted
+        else:
+            self._db.add(MemoryRelation(
+                id=str(uuid.uuid4()),
+                user_id=user_id,
+                subject_label=subject,
+                subject_type=subject_type,
+                predicate=predicate,
+                object_label=object_,
+                object_type=object_type,
+                confidence=confidence,
+                source_episode_id=source_episode_id,
+                notes_encrypted=notes_encrypted,
+            ))
+
+        try:
+            await self._db.commit()
+            logger.info(
+                "memory: upsert_relation user=%s subject=%s predicate=%s object=%s",
+                user_id, subject, predicate, object_,
+            )
+        except Exception as exc:
+            logger.error("memory: upsert_relation failed user=%s: %s", user_id, exc)
+            await self._db.rollback()
+
+    async def query_relations(
+        self,
+        user_id: str,
+        subject: str | None = None,
+        predicate: str | None = None,
+        object_: str | None = None,
+        limit: int = 20,
+    ) -> list[MemoryRelation]:
+        """Query relation rows for a user with optional filters."""
+        q = select(MemoryRelation).where(MemoryRelation.user_id == user_id)
+        if subject is not None:
+            q = q.where(MemoryRelation.subject_label == subject)
+        if predicate is not None:
+            q = q.where(MemoryRelation.predicate == predicate)
+        if object_ is not None:
+            q = q.where(MemoryRelation.object_label == object_)
+        q = q.order_by(MemoryRelation.confidence.desc()).limit(limit)
+        result = await self._db.execute(q)
+        return list(result.scalars().all())
+
     async def insert_archival(self, user_id: str, content: str, source: str = "manual") -> None:
         """Insert a long-term archival memory entry."""
         fernet = await self._get_fernet(user_id)
@@ -343,13 +567,26 @@ class MemoryMiddleware:
 
     async def _get_user_debug(self, user_id: str) -> dict[str, str | None]:
         """Load lightweight user debug fields for trace logs."""
+        from app.config.settings import settings  # noqa: PLC0415
+        from app.models import Subscription  # noqa: PLC0415
+
         result = await self._db.execute(select(User).where(User.id == user_id))
         user = result.scalar_one_or_none()
         if user is None:
             return {"tier": None}
-        return {
+
-            "tier": user.tier,
+        sub_result = await self._db.execute(
-        }
+            select(Subscription.tier).where(Subscription.user_id == user_id)
+        )
+        sub_tier: str | None = sub_result.scalar_one_or_none()
+        if sub_tier:
+            tier = sub_tier
+        elif settings.ENV == "dev":
+            tier = "power"
+        else:
+            tier = user.tier or "free"
+
+        return {"tier": tier}
 
     async def _load_core(self, user_id: str, fernet: Fernet) -> dict[str, str]:
         result = await self._db.execute(
@@ -364,14 +601,49 @@ class MemoryMiddleware:
         return out
 
     async def _load_associative(
-        self, user_id: str, message: str, fernet: Fernet
+        self, user_id: str, message: str, fernet: Fernet, *, user_tier: str = "free"
     ) -> list[str]:
         """Load top-k associative memories.
 
-        Production: uses pgvector cosine similarity on the message embedding.
+        Pro+: pgvector cosine similarity on the message embedding (real_embeddings feature).
-        Current implementation: keyword-based fallback (no external embedding call)
+        Free / embedding failure: keyword-ordered fallback (most recent rows).
-        so tests pass without a live OpenAI key.
         """
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+        from app.core.embeddings import embed_text  # noqa: PLC0415
+
+        if tier_manager.check_feature(user_tier, "real_embeddings"):
+            vec = await embed_text(message)
+            if vec is not None:
+                try:
+                    result = await self._db.execute(
+                        select(MemoryAssociative)
+                        .where(
+                            MemoryAssociative.user_id == user_id,
+                            MemoryAssociative.embedding.isnot(None),
+                        )
+                        .order_by(MemoryAssociative.embedding.cosine_distance(vec))
+                        .limit(_ASSOCIATIVE_TOP_K)
+                    )
+                    rows = result.scalars().all()
+                    out: list[str] = []
+                    for row in rows:
+                        plaintext = _safe_decrypt(fernet, row.content_encrypted)
+                        if plaintext is not None:
+                            out.append(plaintext)
+                    logger.info(
+                        "memory: _load_associative user=%s mode=vector hits=%d",
+                        user_id,
+                        len(out),
+                    )
+                    return out
+                except Exception as exc:
+                    logger.warning(
+                        "memory: vector search failed user=%s, falling back to keyword: %s",
+                        user_id,
+                        exc,
+                    )
+
+        # Keyword fallback: most recent rows
         result = await self._db.execute(
             select(MemoryAssociative)
             .where(MemoryAssociative.user_id == user_id)
@@ -379,7 +651,7 @@ class MemoryMiddleware:
             .limit(_ASSOCIATIVE_TOP_K)
         )
         rows = result.scalars().all()
-        out: list[str] = []
+        out = []
         for row in rows:
             plaintext = _safe_decrypt(fernet, row.content_encrypted)
             if plaintext is not None:
@@ -408,6 +680,26 @@ class MemoryMiddleware:
                 out.append(plaintext)
         return out
 
+    async def _load_relational(self, user_id: str, *, user_tier: str = "free") -> list[str]:
+        """Return top-10 relation strings for Pro+ users; empty list for Free."""
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+
+        if not tier_manager.check_feature(user_tier, "relational_memory"):
+            return []
+
+        result = await self._db.execute(
+            select(MemoryRelation)
+            .where(MemoryRelation.user_id == user_id)
+            .order_by(MemoryRelation.confidence.desc())
+            .limit(10)
+        )
+        rows = result.scalars().all()
+        out = [
+            f"{r.subject_label} --{r.predicate}--> {r.object_label}"
+            for r in rows
+        ]
+        return out
+
     async def _load_proactive(self, user_id: str, fernet: Fernet) -> list[str]:
         result = await self._db.execute(
             select(MemoryProactive)

app/core/note_summarizer.py

@@ -0,0 +1,51 @@
+"""Note summarizer — generates a compact AI summary for a note.
+
+Called fire-and-forget from create_note / update_note tools so the
+``notes.ai_summary`` column stays current without blocking the agent loop.
+"""
+
+from __future__ import annotations
+
+import logging
+
+from langchain_core.messages import HumanMessage, SystemMessage
+
+from app.core.langfuse_client import get_prompt_or_fallback
+from app.core.llm import get_agent_llm
+
+logger = logging.getLogger(__name__)
+
+_FALLBACK_PROMPT = """\
+Summarize this note in <=250 characters. Be terse and dense.
+Keep proper nouns, dates, decisions, and action items.
+Do not start with "This note".
+Respond with the summary text only — no intro, no labels.
+
+Title: {title}
+Content: {content}"""
+
+_MAX_CONTENT_CHARS = 4000
+
+
+async def generate_note_summary(title: str, content: str) -> str:
+    """Return a <=250-char summary of *title* + *content*.
+
+    Uses the Langfuse ``note_summary`` prompt (hot-swappable) with a local
+    fallback.  Truncates *content* to 4000 chars before sending to avoid
+    token waste on large notes.
+    """
+    template, _ = get_prompt_or_fallback("note_summary", _FALLBACK_PROMPT)
+    trimmed = content[:_MAX_CONTENT_CHARS]
+    system_prompt = template.format(title=title, content=trimmed)
+
+    try:
+        llm = get_agent_llm("note-summarizer")
+        response = await llm.ainvoke([
+            SystemMessage(content=system_prompt),
+            HumanMessage(content="Generate the summary."),
+        ])
+        text = response.content if isinstance(response.content, str) else ""
+        return text.strip()[:250]
+    except Exception as exc:
+        logger.warning("note_summarizer: failed to generate summary: %s", exc)
+        return ""

app/core/output_formatter.py

@@ -2,12 +2,36 @@
 
 from __future__ import annotations
 
+import re
 from collections.abc import AsyncGenerator
 from typing import Any
 
-from app.schemas import WsFloatingDomain, WsStreamEnd, WsStreamStart, WsStreamText
+from app.schemas import WsStreamEnd, WsStreamStart, WsStreamText
 
-WsFrame = WsStreamStart | WsStreamText | WsStreamEnd | WsFloatingDomain
+# Matches <canvas kind="...">...</canvas> blocks (single-line or multiline).
+_CANVAS_BLOCK_RE = re.compile(
+    r'<canvas\s+kind=["\']([^"\']+)["\']>(.*?)</canvas>',
+    re.DOTALL | re.IGNORECASE,
+)
+
+
+def extract_canvas_block(text: str) -> tuple[str, str | None, str | None]:
+    """Strip the first <canvas kind="...">...</canvas> block from *text*.
+
+    Returns ``(visible_text, canvas_content, canvas_kind)``.
+    ``canvas_content`` and ``canvas_kind`` are ``None`` when no block is found.
+    """
+    match = _CANVAS_BLOCK_RE.search(text)
+    if not match:
+        return text, None, None
+
+    canvas_kind = match.group(1).strip()
+    canvas_content = match.group(2).strip()
+    visible = text[: match.start()] + text[match.end() :]
+    visible = visible.strip()
+    return visible, canvas_content, canvas_kind
+
+WsFrame = WsStreamStart | WsStreamText | WsStreamEnd
 
 
 class StreamFormatter:
@@ -23,14 +47,6 @@ class StreamFormatter:
         started = False
 
         async for event_type, data in event_stream:
-            if event_type == "floating_domain":
-                if isinstance(data, dict):
-                    yield WsFloatingDomain(
-                        request_id=self.request_id,
-                        domain=data,
-                    )
-                continue
-
             if event_type != "token":
                 continue
 

app/core/scout_runner.py

@@ -48,7 +48,7 @@ from app.core.llm import get_agent_llm, model_for_agent
 from app.core.preprocessors import detect_content_type, preprocess
 from app.core.ws_context import clear_client_executor, execute_on_client, set_client_executor
 from app.db import async_session
-from app.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
+from app.models import ScoutRunLog, CloudScoutConfig, LocalScoutConfig
 
 logger = logging.getLogger(__name__)
 
@@ -169,7 +169,7 @@ def _is_overdue(schedule_cron: str, last_run_at: datetime | None) -> bool:
         next_run: datetime = cron.get_next(datetime)
         return now >= next_run
     except Exception as exc:
-        logger.warning("agent_runner: cannot parse cron %r: %s", schedule_cron, exc)
+        logger.warning("scout_runner: cannot parse cron %r: %s", schedule_cron, exc)
         return False
 
 
@@ -287,11 +287,10 @@ async def _run_agent_with_tools(
                 return final_text
 
             for call in response.tool_calls:
-                call_id = str(call.get("id", ""))
                 call_name = str(call.get("name", ""))
                 call_args = call.get("args", {})
                 logger.info(
-                    "agent_runner: tool_call name=%s args=%s",
+                    "scout_runner: tool_call name=%s args=%s",
                     call_name,
                     json.dumps(call_args, ensure_ascii=True)[:800],
                 )
@@ -306,7 +305,7 @@ async def _run_agent_with_tools(
                     tool_output = await tool_fn.ainvoke(call_args)
 
                 logger.info(
-                    "agent_runner: tool_result name=%s output=%s",
+                    "scout_runner: tool_result name=%s output=%s",
                     call_name,
                     str(tool_output)[:200],
                 )
@@ -361,7 +360,7 @@ async def _scan_directories(
         try:
             result = await execute_on_client(action="list_directory", data={"path": path})
         except Exception as exc:
-            logger.warning("agent_runner: list_directory failed %r: %s", path, exc)
+            logger.warning("scout_runner: list_directory failed %r: %s", path, exc)
             return
         for entry in result.get("entries", []):
             entry_path = entry.get("path", "")
@@ -415,7 +414,7 @@ async def _fetch_projects() -> list[dict]:
         result = await execute_on_client(action="select", table="projects")
         return result.get("rows", [])
     except Exception as exc:
-        logger.warning("agent_runner: failed to fetch projects: %s", exc)
+        logger.warning("scout_runner: failed to fetch projects: %s", exc)
         return []
 
 
@@ -443,7 +442,7 @@ async def _fetch_domain_entities(domain: str, project_id: str) -> list[dict]:
         )
         return result.get("rows", [])
     except Exception as exc:
-        logger.warning("agent_runner: failed to fetch %s: %s", domain, exc)
+        logger.warning("scout_runner: failed to fetch %s: %s", domain, exc)
         return []
 
 
@@ -556,8 +555,8 @@ def _get_no_match_behavior(agent_config: dict) -> str:
 
 async def run_local_agent(
     user_id: str,
-    config: LocalAgentConfig,
+    config: LocalScoutConfig,
-    run_log: AgentRunLog,
+    run_log: ScoutRunLog,
     device_mgr: DeviceConnectionManager,
     run_context: dict | None = None,
 ) -> None:
@@ -587,7 +586,7 @@ async def run_local_agent(
 
     if not is_online:
         logger.info(
-            "agent_runner: skip run=%s — device %r offline for user=%s",
+            "scout_runner: skip run=%s — device %r offline for user=%s",
             run_id,
             target_device_id or "<any>",
             user_id,
@@ -606,7 +605,7 @@ async def run_local_agent(
     errors: list[str] = []
     items_processed = 0
     items_created = 0
-    agent_config: dict = config.agent_config or {}
+    agent_config: dict = config.scout_config or {}
     processing_tools = _build_processing_tools(config.data_types)
 
     try:
@@ -617,7 +616,7 @@ async def run_local_agent(
             last_run_at=config.last_run_at,
         )
         logger.info(
-            "agent_runner: run=%s found %d file(s) after filtering", run_id, len(file_paths)
+            "scout_runner: run=%s found %d file(s) after filtering", run_id, len(file_paths)
         )
 
         if not file_paths:
@@ -642,7 +641,7 @@ async def run_local_agent(
                 raw_content: str = file_result.get("content", "")
                 if not raw_content.strip():
                     logger.debug(
-                        "agent_runner: run=%s skipping empty file %r", run_id, file_path
+                        "scout_runner: run=%s skipping empty file %r", run_id, file_path
                     )
                     continue
 
@@ -652,16 +651,21 @@ async def run_local_agent(
                 preprocessed = preprocess(content_type, raw_content)
 
                 logger.info(
-                    "agent_runner: run=%s file=%r content_type=%s clean_len=%d",
+                    "scout_runner: run=%s file=%r content_type=%s clean_len=%d",
                     run_id, file_path, content_type, len(preprocessed.clean_text),
                 )
 
                 # ── Phase B: single LLM call ─────────────────────────
                 extraction_rules = _get_extraction_rules(agent_config, content_type)
                 no_match_behavior = _get_no_match_behavior(agent_config)
-                global_rules_lines = "\n".join(
+                base_global_rules = list(agent_config.get("global_rules", []))
-                    f"- {r}" for r in agent_config.get("global_rules", [])
+                if "notes" in config.data_types:
-                )
+                    base_global_rules.append(
+                        "For notes: when updating an existing note use `propose_note_edit` "
+                        "(type=append/insert/replace) so the user can review AI changes. "
+                        "Only call `update_note` for complete content replacement without review."
+                    )
+                global_rules_lines = "\n".join(f"- {r}" for r in base_global_rules)
                 metadata_section = _format_metadata(preprocessed.metadata)
 
                 system_prompt = compile_prompt(
@@ -707,19 +711,19 @@ async def run_local_agent(
                     projects_block = _format_projects(projects)
 
                 logger.info(
-                    "agent_runner: run=%s file=%r created=%d result=%s",
+                    "scout_runner: run=%s file=%r created=%d result=%s",
                     run_id, file_path, file_created, result_text[:200],
                 )
 
             except Exception as exc:
                 errors.append(f"Error processing '{file_path}': {exc}")
                 logger.error(
-                    "agent_runner: run=%s file=%r failed: %s", run_id, file_path, exc
+                    "scout_runner: run=%s file=%r failed: %s", run_id, file_path, exc
                 )
 
     except Exception as exc:
         errors.append(f"Agent run failed: {exc}")
-        logger.error("agent_runner: run=%s failed: %s", run_id, exc)
+        logger.error("scout_runner: run=%s failed: %s", run_id, exc)
     finally:
         _running_agents.discard(agent_id)
         clear_client_executor()
@@ -740,7 +744,7 @@ async def run_local_agent(
         errors=errors,
     )
     logger.info(
-        "agent_runner: run=%s done status=%s processed=%d created=%d errors=%d",
+        "scout_runner: run=%s done status=%s processed=%d created=%d errors=%d",
         run_id,
         final_status,
         items_processed,
@@ -758,7 +762,7 @@ async def run_local_agent(
             })
         except Exception as exc:
             logger.warning(
-                "agent_runner: run=%s failed to send run_complete: %s", run_id, exc
+                "scout_runner: run=%s failed to send run_complete: %s", run_id, exc
             )
 
 
@@ -769,8 +773,8 @@ _CLOUD_DEFAULT_LOOKBACK_DAYS: int = 7
 
 async def run_cloud_agent(
     user_id: str,
-    config: CloudAgentConfig,
+    config: CloudScoutConfig,
-    run_log: AgentRunLog,
+    run_log: ScoutRunLog,
     device_mgr: DeviceConnectionManager,
 ) -> None:
     """Execute a cloud connector agent run end-to-end.
@@ -793,7 +797,7 @@ async def run_cloud_agent(
     # ── 1. Device online check ─────────────────────────────────────────
     if not device_mgr.is_online(user_id):
         logger.info(
-            "agent_runner: skip cloud run=%s — no device online for user=%s",
+            "scout_runner: skip cloud run=%s — no device online for user=%s",
             run_id,
             user_id,
         )
@@ -818,7 +822,7 @@ async def run_cloud_agent(
     try:
         credentials_info = decrypt_token(config.oauth_token_encrypted)
     except ValueError as exc:
-        logger.error("agent_runner: failed to decrypt OAuth token for agent %s: %s", config.id, exc)
+        logger.error("scout_runner: failed to decrypt OAuth token for agent %s: %s", config.id, exc)
         await _finalize_run(
             run_log,
             status="error",
@@ -864,7 +868,7 @@ async def run_cloud_agent(
             raw_messages = []
     except RuntimeError as exc:
         logger.error(
-            "agent_runner: provider fetch failed for cloud agent %s: %s", config.id, exc
+            "scout_runner: provider fetch failed for cloud agent %s: %s", config.id, exc
         )
         await _finalize_run(
             run_log,
@@ -877,7 +881,7 @@ async def run_cloud_agent(
         return
 
     logger.info(
-        "agent_runner: cloud agent %s fetched %d item(s) from %s for user=%s",
+        "scout_runner: cloud agent %s fetched %d item(s) from %s for user=%s",
         config.id,
         len(raw_messages),
         config.provider,
@@ -937,16 +941,16 @@ async def run_cloud_agent(
             new_encrypted = encrypt_token(refreshed)
             async with async_session() as db:
                 cfg_result = await db.execute(
-                    select(CloudAgentConfig).where(CloudAgentConfig.id == config.id)
+                    select(CloudScoutConfig).where(CloudScoutConfig.id == config.id)
                 )
                 cfg_row = cfg_result.scalar_one_or_none()
                 if cfg_row:
                     cfg_row.oauth_token_encrypted = new_encrypted
                     await db.commit()
-            logger.debug("agent_runner: refreshed OAuth token persisted for agent %s", config.id)
+            logger.debug("scout_runner: refreshed OAuth token persisted for agent %s", config.id)
         except Exception as exc:
             logger.warning(
-                "agent_runner: failed to persist refreshed token for agent %s: %s",
+                "scout_runner: failed to persist refreshed token for agent %s: %s",
                 config.id,
                 exc,
             )
@@ -970,7 +974,7 @@ async def run_cloud_agent(
         config_type="cloud",
     )
     logger.info(
-        "agent_runner: cloud run=%s done status=%s processed=%d created=%d errors=%d",
+        "scout_runner: cloud run=%s done status=%s processed=%d created=%d errors=%d",
         run_id,
         final_status,
         items_processed,
@@ -992,7 +996,7 @@ async def trigger_pending_runs(
     Called as a background task from the device WS endpoint on ``device_hello``.
     """
     logger.info(
-        "agent_runner: pending-run scan skipped for user=%s device=%s (client-owned agent config)",
+        "scout_runner: pending-run scan skipped for user=%s device=%s (client-owned agent config)",
         user_id,
         device_id,
     )
@@ -1003,7 +1007,7 @@ async def trigger_pending_runs(
 
 
 async def _finalize_run(
-    run_log: AgentRunLog,
+    run_log: ScoutRunLog,
     *,
     status: str,
     items_processed: int = 0,
@@ -1027,14 +1031,14 @@ async def _finalize_run(
             if update_config_last_run and config_id:
                 if config_type == "local":
                     cfg_result = await db.execute(
-                        select(LocalAgentConfig).where(LocalAgentConfig.id == config_id)
+                        select(LocalScoutConfig).where(LocalScoutConfig.id == config_id)
                     )
                     cfg = cfg_result.scalar_one_or_none()
                     if cfg:
                         cfg.last_run_at = now
                 elif config_type == "cloud":
                     cfg_result = await db.execute(
-                        select(CloudAgentConfig).where(CloudAgentConfig.id == config_id)
+                        select(CloudScoutConfig).where(CloudScoutConfig.id == config_id)
                     )
                     cfg = cfg_result.scalar_one_or_none()
                     if cfg:
@@ -1043,5 +1047,5 @@ async def _finalize_run(
             await db.commit()
     except Exception as exc:
         logger.error(
-            "agent_runner: failed to finalize run_log=%s: %s", run_log.id, exc
+            "scout_runner: failed to finalize run_log=%s: %s", run_log.id, exc
         )

app/core/scout_session_buffer.py

@@ -0,0 +1,96 @@
+"""In-process TTL buffer for per-session LangChain message history.
+
+Stores the full message list (including AIMessage with tool_calls and ToolMessage)
+keyed by (user_id, session_id), so agents can reconstruct tool-call context across
+conversation turns without it being lossy through the wire.
+
+Single-process only. For multi-worker deployments, replace the _SessionBuffer
+implementation with one backed by Redis (serialize LangChain messages to dicts via
+message_to_dict / messages_from_dict from langchain_core.messages).
+"""
+from __future__ import annotations
+
+import time
+from threading import Lock
+
+from langchain_core.messages import BaseMessage
+
+SESSION_TTL_SECONDS = 1800  # 30-minute idle expiry
+MAX_MESSAGES_PER_SESSION = 80  # cap to avoid unbounded memory growth
+
+
+class _SessionBuffer:
+    def __init__(self) -> None:
+        self._store: dict[tuple[str, str], tuple[float, list[BaseMessage]]] = {}
+        self._lock = Lock()
+
+    def _evict_stale(self) -> None:
+        now = time.monotonic()
+        stale = [k for k, (ts, _) in self._store.items() if now - ts > SESSION_TTL_SECONDS]
+        for k in stale:
+            del self._store[k]
+
+    def get(self, user_id: str, session_id: str) -> list[BaseMessage] | None:
+        key = (user_id, session_id)
+        with self._lock:
+            entry = self._store.get(key)
+            if entry is None:
+                return None
+            ts, msgs = entry
+            if time.monotonic() - ts > SESSION_TTL_SECONDS:
+                del self._store[key]
+                return None
+            self._store[key] = (time.monotonic(), msgs)
+            return list(msgs)
+
+    def set(self, user_id: str, session_id: str, messages: list[BaseMessage]) -> None:
+        key = (user_id, session_id)
+        capped = messages[-MAX_MESSAGES_PER_SESSION:]
+        with self._lock:
+            self._evict_stale()
+            self._store[key] = (time.monotonic(), capped)
+
+    def clear(self, user_id: str, session_id: str) -> None:
+        with self._lock:
+            self._store.pop((user_id, session_id), None)
+
+    def append_system_message(self, user_id: str, session_id: str, text: str) -> None:
+        """Append a synthetic system message to the buffer for the given session.
+
+        Creates the session slot if it does not yet exist.  Used by the
+        contextual_scope_update handler to inject navigation events without
+        making an LLM call.
+        """
+        from langchain_core.messages import SystemMessage  # noqa: PLC0415
+
+        key = (user_id, session_id)
+        with self._lock:
+            entry = self._store.get(key)
+            if entry is None:
+                msgs: list[BaseMessage] = [SystemMessage(content=text)]
+            else:
+                _, existing = entry
+                msgs = list(existing) + [SystemMessage(content=text)]
+            capped = msgs[-MAX_MESSAGES_PER_SESSION:]
+            self._store[key] = (time.monotonic(), capped)
+
+
+class ContextualBufferProxy:
+    """Thin wrapper around _SessionBuffer that closes over user_id + session_id.
+
+    Returned by get_session_buffer() so callers can call
+    ``proxy.append_system_message(text)`` without threading user_id/session_id
+    through every call site.
+    """
+
+    def __init__(self, buf: "_SessionBuffer", user_id: str, session_id: str) -> None:
+        self._buf = buf
+        self._user_id = user_id
+        self._session_id = session_id
+
+    def append_system_message(self, text: str) -> None:
+        self._buf.append_system_message(self._user_id, self._session_id, text)
+
+
+# Module-level singleton — same pattern as _pending_states in api/app/api/routes/auth.py
+session_buffer = _SessionBuffer()

app/core/ws_context.py

@@ -7,10 +7,32 @@ The callback sends a `tool_call` WS frame and awaits the `tool_result`.
 
 from __future__ import annotations
 
+import re
 from contextvars import ContextVar
 from typing import Any, Callable, Coroutine
 from uuid import uuid4
 
+_SNAKE_TO_CAMEL_RE = re.compile(r"_([a-z])")
+
+
+def _key_to_camel(key: str) -> str:
+    return _SNAKE_TO_CAMEL_RE.sub(lambda m: m.group(1).upper(), key)
+
+
+def _keys_to_camel(obj: Any) -> Any:
+    """Recursively convert dict keys from snake_case to camelCase.
+
+    Mirrors the JS-side ``toCamelCase`` applied to incoming WS frames in
+    ``adiuvAI/src/main/api/backend-client.ts``. The Electron executor wraps
+    tool_result payloads in ``toSnakeCase`` before sending; this restores the
+    camelCase schema property names that the tool code expects to read.
+    """
+    if isinstance(obj, dict):
+        return {_key_to_camel(k): _keys_to_camel(v) for k, v in obj.items()}
+    if isinstance(obj, list):
+        return [_keys_to_camel(v) for v in obj]
+    return obj
+
 # Holds the execute callback for the current WS session.
 # Set by the chat WS handler before the orchestrator runs; cleared after.
 _client_executor: ContextVar[Callable[[dict], Coroutine[Any, Any, dict]]] = ContextVar(
@@ -82,6 +104,7 @@ async def execute_on_client(
         payload["limit"] = limit
 
     result = await callback(payload)
+    result = _keys_to_camel(result)
     collector = _tool_result_collector.get(None)
     if collector is not None:
         collector.append({

app/integrations/gmail.py

@@ -8,7 +8,7 @@ blocking the event loop.
 Token refresh is handled transparently: when the stored access token has
 expired, ``google.auth.transport.requests.Request`` will use the refresh
 token to obtain a fresh one.  The caller is responsible for persisting
-any refreshed credentials back to ``CloudAgentConfig.oauth_token_encrypted``
+any refreshed credentials back to ``CloudScoutConfig.oauth_token_encrypted``
 (see ``agent_runner.run_cloud_agent``).
 
 Credential dict shape (Google OAuth2):

app/main.py

@@ -4,6 +4,10 @@ import logging
 from fastapi import FastAPI
 from fastapi.middleware.cors import CORSMiddleware
 
+from app.api.middleware.rate_limit import TierRateLimitMiddleware
+from app.api.middleware.sanitizer import SanitizerMiddleware
+from app.config.settings import settings
+
 logging.basicConfig(
     level=logging.INFO,
     format="%(asctime)s %(levelname)s %(name)s: %(message)s",
@@ -11,9 +15,66 @@ logging.basicConfig(
 logging.getLogger("sqlalchemy.engine").setLevel(logging.WARNING)
 logging.getLogger("sqlalchemy.pool").setLevel(logging.WARNING)
 
-from app.api.middleware.rate_limit import TierRateLimitMiddleware
+
-from app.api.middleware.sanitizer import SanitizerMiddleware
+async def _memory_audit_cron_tick() -> None:
-from app.config.settings import settings
+    """Weekly cron: contradiction scan + label canonicalization for all users (Phase 7)."""
+    import logging  # noqa: PLC0415
+    _log = logging.getLogger(__name__)
+    _log.info("memory audit cron tick: starting")
+    try:
+        from app.db import async_session  # noqa: PLC0415
+        from app.core.memory_maintenance import audit_memory  # noqa: PLC0415
+        from app.models import User  # noqa: PLC0415
+        from sqlalchemy import select  # noqa: PLC0415
+
+        async with async_session() as db:
+            result = await db.execute(select(User.id))
+            user_ids: list[str] = list(result.scalars().all())
+
+        for uid in user_ids:
+            try:
+                async with async_session() as db:
+                    await audit_memory(db, uid)
+            except Exception as exc:
+                _log.warning("memory audit cron tick: audit_memory failed user=%s: %s", uid, exc)
+
+        _log.info("memory audit cron tick: done users=%d", len(user_ids))
+    except Exception as exc:
+        _log.warning("memory audit cron tick: failed: %s", exc)
+
+
+async def _memory_cron_tick() -> None:
+    """Hourly cron: drain Free-tier extraction queue + mine proactive patterns for Power+ users."""
+    import logging  # noqa: PLC0415
+    _log = logging.getLogger(__name__)
+    _log.info("memory cron tick: starting")
+    try:
+        from app.db import async_session  # noqa: PLC0415
+        from app.core.memory_maintenance import drain_extraction_queue, mine_proactive_patterns  # noqa: PLC0415
+        from app.billing.tier_manager import tier_manager  # noqa: PLC0415
+        from app.models import User  # noqa: PLC0415
+        from sqlalchemy import select  # noqa: PLC0415
+
+        async with async_session() as db:
+            await drain_extraction_queue(db)
+
+        # mine proactive patterns for every Power+ user
+        async with async_session() as db:
+            result = await db.execute(select(User.id))
+            user_ids: list[str] = list(result.scalars().all())
+
+        for uid in user_ids:
+            try:
+                async with async_session() as db:
+                    tier = await tier_manager.get_tier(uid, db)
+                    if tier_manager.check_feature(tier, "proactive_mining"):
+                        await mine_proactive_patterns(db, uid)
+            except Exception as exc:
+                _log.warning("memory cron tick: mine_proactive_patterns failed user=%s: %s", uid, exc)
+
+        _log.info("memory cron tick: done users=%d", len(user_ids))
+    except Exception as exc:
+        _log.warning("memory cron tick: failed: %s", exc)
 
 
 @asynccontextmanager
@@ -21,8 +82,21 @@ async def lifespan(app: FastAPI):
     # Startup: ensure agent tool modules are loaded.
     import app.agents  # noqa: F401
 
+    scheduler = None
+    if settings.SCHEDULER_ENABLED:
+        from apscheduler.schedulers.asyncio import AsyncIOScheduler  # noqa: PLC0415
+
+        scheduler = AsyncIOScheduler()
+        scheduler.add_job(_memory_cron_tick, "interval", hours=1, id="memory_cron")
+        scheduler.add_job(_memory_audit_cron_tick, "interval", weeks=1, id="memory_audit_cron")
+        scheduler.start()
+        logging.getLogger(__name__).info("memory cron scheduler started (interval=1h)")
+
     yield
 
+    if scheduler is not None:
+        scheduler.shutdown(wait=False)
+
     # Shutdown: dispose SQLAlchemy connection pool
     from app.db import engine
     await engine.dispose()
@@ -50,13 +124,14 @@ def create_app() -> FastAPI:
     app.add_middleware(SanitizerMiddleware)
     app.add_middleware(TierRateLimitMiddleware)
 
-    from app.api.routes import agents, auth, billing, chat, device_ws
+    from app.api.routes import scouts, auth, billing, chat, device_ws, memory
 
     app.include_router(auth.router,       prefix="/api/v1")
     app.include_router(chat.router,       prefix="/api/v1")
     app.include_router(billing.router,    prefix="/api/v1")
-    app.include_router(agents.router,     prefix="/api/v1")
+    app.include_router(scouts.router,     prefix="/api/v1")
     app.include_router(device_ws.router,  prefix="/api/v1")
+    app.include_router(memory.router,     prefix="/api/v1")
 
     @app.get("/api/v1/health", tags=["health"])
     async def health() -> dict:

app/models.py

@@ -1,19 +1,20 @@
 """SQLAlchemy ORM models for all persistent tables.
 
-Only auth, billing, agent config, and memory data live here.
+Only auth, billing, scout config, and memory data live here.
 User content (notes, tasks, etc.) lives exclusively on the client.
 
 Table inventory:
   users               — account credentials + tier
   refresh_tokens      — hashed refresh token store
   subscriptions       — Stripe subscription records
-  local_agent_configs — per-device batch agent configs
+  local_scout_configs — per-device batch scout configs
-  cloud_agent_configs — OAuth-backed cloud agent configs
+  cloud_scout_configs — OAuth-backed cloud scout configs
-  agent_run_logs      — execution history for all agents
+  scout_run_logs      — execution history for all scouts
   memory_core         — per-user persistent key/value preferences (encrypted)
   memory_associative  — per-user semantic memory with embeddings (encrypted)
   memory_episodic     — per-user session summaries (encrypted)
   memory_proactive    — per-user behavioral patterns (encrypted)
+  memory_relations    — per-user entity/relation graph (Mem0g-light, Phase 3)
 """
 
 from __future__ import annotations
@@ -21,6 +22,7 @@ from __future__ import annotations
 import uuid
 from datetime import datetime, timezone
 
+from pgvector.sqlalchemy import Vector
 from sqlalchemy import (
     Boolean,
     DateTime,
@@ -29,6 +31,7 @@ from sqlalchemy import (
     ForeignKey,
     Integer,
     JSON,
+    LargeBinary,
     String,
     Text,
     Uuid,
@@ -155,8 +158,8 @@ class Subscription(Base):
     user: Mapped[User] = relationship(back_populates="subscription")
 
 
-class LocalAgentConfig(Base):
+class LocalScoutConfig(Base):
-    __tablename__ = "local_agent_configs"
+    __tablename__ = "local_scout_configs"
 
     id: Mapped[str] = mapped_column(
         Uuid(as_uuid=False), primary_key=True, default=_uuid
@@ -169,7 +172,7 @@ class LocalAgentConfig(Base):
     directory_paths: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     data_types: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     prompt_template: Mapped[str] = mapped_column(Text, nullable=False, default="")
-    agent_config: Mapped[dict | None] = mapped_column(JSON, nullable=True)
+    scout_config: Mapped[dict | None] = mapped_column(JSON, nullable=True)
     file_extensions: Mapped[list] = mapped_column(JSON, nullable=False, default=list)
     schedule_cron: Mapped[str] = mapped_column(String(100), nullable=False, default="0 */6 * * *")
     enabled: Mapped[bool] = mapped_column(Boolean, nullable=False, default=True)
@@ -181,17 +184,17 @@ class LocalAgentConfig(Base):
         DateTime(timezone=True), nullable=False, server_default=func.now(), onupdate=func.now()
     )
 
-    run_logs: Mapped[list[AgentRunLog]] = relationship(
+    run_logs: Mapped[list["ScoutRunLog"]] = relationship(
-        back_populates="local_agent",
+        back_populates="local_scout",
-        primaryjoin="and_(AgentRunLog.agent_id == LocalAgentConfig.id, AgentRunLog.agent_type == 'local')",
+        primaryjoin="and_(ScoutRunLog.scout_id == LocalScoutConfig.id, ScoutRunLog.scout_type == 'local')",
-        foreign_keys="AgentRunLog.agent_id",
+        foreign_keys="ScoutRunLog.scout_id",
         cascade="all, delete-orphan",
-        overlaps="run_logs,cloud_agent",
+        overlaps="run_logs,cloud_scout",
     )
 
 
-class CloudAgentConfig(Base):
+class CloudScoutConfig(Base):
-    __tablename__ = "cloud_agent_configs"
+    __tablename__ = "cloud_scout_configs"
 
     id: Mapped[str] = mapped_column(
         Uuid(as_uuid=False), primary_key=True, default=_uuid
@@ -215,51 +218,63 @@ class CloudAgentConfig(Base):
         DateTime(timezone=True), nullable=False, server_default=func.now(), onupdate=func.now()
     )
 
-    run_logs: Mapped[list[AgentRunLog]] = relationship(
+    run_logs: Mapped[list["ScoutRunLog"]] = relationship(
-        back_populates="cloud_agent",
+        back_populates="cloud_scout",
-        primaryjoin="and_(AgentRunLog.agent_id == CloudAgentConfig.id, AgentRunLog.agent_type == 'cloud')",
+        primaryjoin="and_(ScoutRunLog.scout_id == CloudScoutConfig.id, ScoutRunLog.scout_type == 'cloud')",
-        foreign_keys="AgentRunLog.agent_id",
+        foreign_keys="ScoutRunLog.scout_id",
         cascade="all, delete-orphan",
-        overlaps="run_logs,local_agent",
+        overlaps="run_logs,local_scout",
     )
 
 
-class AgentRunLog(Base):
+class ScoutRunLog(Base):
-    __tablename__ = "agent_run_logs"
+    __tablename__ = "scout_run_logs"
 
     id: Mapped[str] = mapped_column(
         Uuid(as_uuid=False), primary_key=True, default=_uuid
     )
-    # Plain string — not a FK because it references either local_agent_configs or cloud_agent_configs
+    # Plain string — not a FK because it references either local_scout_configs or cloud_scout_configs
-    # depending on agent_type. Query by (agent_id, agent_type) to locate the source config.
+    # depending on scout_type. Query by (scout_id, scout_type) to locate the source config.
-    agent_id: Mapped[str] = mapped_column(String(255), nullable=False, index=True)
+    scout_id: Mapped[str] = mapped_column(String(255), nullable=False, index=True)
-    agent_type: Mapped[str] = mapped_column(AgentTypeEnum, nullable=False)
+    scout_type: Mapped[str] = mapped_column(AgentTypeEnum, nullable=False)
     user_id: Mapped[str] = mapped_column(
         Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), nullable=False, index=True
     )
     status: Mapped[str] = mapped_column(AgentStatusEnum, nullable=False, default="running")
     items_processed: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
     items_created: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    tokens_used: Mapped[int] = mapped_column(Integer, nullable=False, default=0, server_default="0")
     errors: Mapped[list | None] = mapped_column(JSON, nullable=True)
     started_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )
     completed_at: Mapped[datetime | None] = mapped_column(DateTime(timezone=True), nullable=True)
 
-    local_agent: Mapped[LocalAgentConfig | None] = relationship(
+    local_scout: Mapped["LocalScoutConfig | None"] = relationship(
         back_populates="run_logs",
-        primaryjoin="and_(AgentRunLog.agent_id == LocalAgentConfig.id, AgentRunLog.agent_type == 'local')",
+        primaryjoin="and_(ScoutRunLog.scout_id == LocalScoutConfig.id, ScoutRunLog.scout_type == 'local')",
-        foreign_keys="AgentRunLog.agent_id",
+        foreign_keys="ScoutRunLog.scout_id",
-        overlaps="run_logs,cloud_agent",
+        overlaps="run_logs,cloud_scout",
     )
-    cloud_agent: Mapped[CloudAgentConfig | None] = relationship(
+    cloud_scout: Mapped["CloudScoutConfig | None"] = relationship(
         back_populates="run_logs",
-        primaryjoin="and_(AgentRunLog.agent_id == CloudAgentConfig.id, AgentRunLog.agent_type == 'cloud')",
+        primaryjoin="and_(ScoutRunLog.scout_id == CloudScoutConfig.id, ScoutRunLog.scout_type == 'cloud')",
-        foreign_keys="AgentRunLog.agent_id",
+        foreign_keys="ScoutRunLog.scout_id",
-        overlaps="run_logs,local_agent",
+        overlaps="run_logs,local_scout",
     )
 
 
+class MonthlyTokenUsage(Base):
+    __tablename__ = "monthly_token_usage"
+
+    user_id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"), primary_key=True
+    )
+    year_month: Mapped[str] = mapped_column(String(7), primary_key=True)  # 'YYYY-MM'
+    feature: Mapped[str] = mapped_column(String(64), primary_key=True)
+    tokens_used: Mapped[int] = mapped_column(Integer, nullable=False, default=0, server_default="0")
+
+
 # ── Memory models ─────────────────────────────────────────────────────────────
 
 
@@ -299,8 +314,8 @@ class MemoryAssociative(Base):
         nullable=False, index=True,
     )
     content_encrypted: Mapped[str] = mapped_column(Text, nullable=False)
-    # JSON-encoded float list in SQLite tests; vector(1536) in Postgres via migration.
+    # vector(1536) via pgvector; SQLite tests use NULL embeddings so no dialect issue.
-    embedding: Mapped[list | None] = mapped_column(JSON, nullable=True)
+    embedding: Mapped[list | None] = mapped_column(Vector(1536), nullable=True)
     entity_type: Mapped[str | None] = mapped_column(String(100), nullable=True)
     entity_id: Mapped[str | None] = mapped_column(String(255), nullable=True)
     updated_at: Mapped[datetime] = mapped_column(
@@ -348,3 +363,85 @@ class MemoryProactive(Base):
     created_at: Mapped[datetime] = mapped_column(
         DateTime(timezone=True), nullable=False, server_default=func.now()
     )
+
+
+class ExtractionQueue(Base):
+    """Batch extraction queue for Free-tier users (Phase 2).
+
+    Pro/Power/Team users get realtime asyncio.create_task() extraction.
+    Free users get a queue row here; a daily cron (Phase 5) drains it.
+    """
+
+    __tablename__ = "extraction_queue"
+
+    id: Mapped[str] = mapped_column(Uuid(as_uuid=False), primary_key=True, default=_uuid)
+    user_id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"),
+        nullable=False, index=True,
+    )
+    episode_id: Mapped[str | None] = mapped_column(
+        Uuid(as_uuid=False), nullable=True,
+    )
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+
+
+class MemoryRelation(Base):
+    """Per-user entity/relation graph row (Mem0g-light, Phase 3).
+
+    subject_label/object_label are plaintext entity identifiers (not user content).
+    notes_encrypted is optional Fernet-encrypted per-user commentary.
+    confidence in [0.0, 1.0] — decays 5 % per 30 days since last_confirmed_at.
+    """
+
+    __tablename__ = "memory_relations"
+
+    id: Mapped[str] = mapped_column(Uuid(as_uuid=False), primary_key=True, default=_uuid)
+    user_id: Mapped[str] = mapped_column(
+        Uuid(as_uuid=False), ForeignKey("users.id", ondelete="CASCADE"),
+        nullable=False, index=True,
+    )
+    subject_label: Mapped[str] = mapped_column(String(128), nullable=False)
+    subject_type: Mapped[str] = mapped_column(String(32), nullable=False)
+    predicate: Mapped[str] = mapped_column(String(64), nullable=False)
+    object_label: Mapped[str] = mapped_column(String(128), nullable=False)
+    object_type: Mapped[str] = mapped_column(String(32), nullable=False)
+    confidence: Mapped[float] = mapped_column(Float, nullable=False, default=0.7)
+    source_episode_id: Mapped[str | None] = mapped_column(
+        Uuid(as_uuid=False),
+        ForeignKey("memory_episodic.id", ondelete="SET NULL"),
+        nullable=True,
+    )
+    notes_encrypted: Mapped[bytes | None] = mapped_column(LargeBinary, nullable=True)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )
+    updated_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now(), onupdate=func.now()
+    )
+    last_confirmed_at: Mapped[datetime | None] = mapped_column(
+        DateTime(timezone=True), nullable=True
+    )
+
+
+class Plugin(Base):
+    """Plugin marketplace catalog entry."""
+
+    __tablename__ = "plugins"
+
+    id: Mapped[str] = mapped_column(String(255), primary_key=True)
+    name: Mapped[str] = mapped_column(String(255), nullable=False)
+    description: Mapped[str] = mapped_column(Text, nullable=False)
+    version: Mapped[str] = mapped_column(String(50), nullable=False)
+    author_name: Mapped[str] = mapped_column(String(255), nullable=False)
+    category: Mapped[str] = mapped_column(String(100), nullable=False)
+    price_cents: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    permissions: Mapped[str] = mapped_column(Text, nullable=False, default="[]")
+    status: Mapped[str] = mapped_column(String(50), nullable=False, default="pending")
+    s3_package_key: Mapped[str | None] = mapped_column(String(500), nullable=True)
+    install_count: Mapped[int] = mapped_column(Integer, nullable=False, default=0)
+    avg_rating: Mapped[float] = mapped_column(Float, nullable=False, default=0.0)
+    created_at: Mapped[datetime] = mapped_column(
+        DateTime(timezone=True), nullable=False, server_default=func.now()
+    )

app/schemas/__init__.py

@@ -73,11 +73,9 @@ class WsFrameType(str, Enum):
     device_hello = "device_hello"
     # ── v3 frame types ─────────────────────────────────────────────────
     home_request = "home_request"
-    floating_request = "floating_request"
     stream_start = "stream_start"
     stream_text = "stream_text"
     stream_end = "stream_end"
-    floating_domain = "floating_domain"
     data_request = "data_request"
     data_response = "data_response"
     mutation = "mutation"
@@ -85,6 +83,21 @@ class WsFrameType(str, Enum):
     journey_start = "journey_start"
     journey_message = "journey_message"
     journey_reply = "journey_reply"
+    # ── v5 brief frame types ──────────────────────────────────────────
+    brief_request = "brief_request"
+    # ── v6 task brief frame types ─────────────────────────────────────
+    task_brief_request = "task_brief_request"
+    # ── v7 folder index frame types ───────────────────────────────────
+    index_session_start = "index_session_start"
+    index_file_batch = "index_file_batch"
+    index_session_cancel = "index_session_cancel"
+    index_file_result = "index_file_result"
+    index_session_progress = "index_session_progress"
+    index_session_done = "index_session_done"
+    # ── v8 contextual sidebar frame types ────────────────────────────
+    contextual_request = "contextual_request"
+    contextual_scope_update = "contextual_scope_update"
+    contextual_scope_ack = "contextual_scope_ack"
 
 
 class WsToolCall(BaseModel):
@@ -134,17 +147,20 @@ class WsDeviceHello(BaseModel):
 
     type: Literal[WsFrameType.device_hello] = WsFrameType.device_hello
     device_id: str
-    agent_ids: list[str] = Field(default_factory=list)
+    scout_ids: list[str] = Field(default_factory=list)
 
 
 
 # ── WebSocket v3 Frame Models ─────────────────────────────────────────
 
-class WsFloatingScope(BaseModel):
+class FormatPrefsModel(BaseModel):
-    """Scope for a floating request — narrows the agent to a specific entity."""
+    """User display preferences sent by Electron on each request."""
 
-    type: Literal["task", "project", "note", "timeline"]
+    timezone: str = "UTC"
-    id: str | None = None
+    date_format: str = "dd/MM/yyyy"
+    time_format: str = "24h"
+    locale: str = "en-US"
+    now_iso: str = ""
 
 
 class WsHomeRequest(BaseModel):
@@ -153,14 +169,18 @@ class WsHomeRequest(BaseModel):
     type: Literal[WsFrameType.home_request] = WsFrameType.home_request
     message: str
     conversation_history: list[dict[str, Any]] = Field(default_factory=list)
+    format_prefs: FormatPrefsModel | None = None
 
 
-class WsFloatingRequest(BaseModel):
+class WsBriefRequest(BaseModel):
-    """Client → Server: Floating chat message scoped to an entity."""
+    """Client → Server: Request a plain-text brief (home or project)."""
 
-    type: Literal[WsFrameType.floating_request] = WsFrameType.floating_request
+    type: Literal[WsFrameType.brief_request] = WsFrameType.brief_request
-    message: str
+    request_id: str | None = None
-    scope: WsFloatingScope
+    session_id: str | None = None
+    mode: Literal["home", "project"]
+    project_id: str | None = None
+    format_prefs: FormatPrefsModel | None = None
 
 
 class WsStreamStart(BaseModel):
@@ -183,28 +203,14 @@ class WsStreamEnd(BaseModel):
 
     type: Literal[WsFrameType.stream_end] = WsFrameType.stream_end
     request_id: str
+    error: str | None = None
+    mutations: list[dict[str, Any]] | None = None
 
 
-class WsDomain(BaseModel):
+# ── Scout Config V2 ───────────────────────────────────────────────────
-    """Structured floating domain payload for UI routing decisions."""
-
-    type: Literal["task", "timeline", "project", "node"]
-    id: str | None = None
-    section: Literal["task", "timeline", "note"] | None = None
 
 
-class WsFloatingDomain(BaseModel):
+class ScoutContentTypeConfig(BaseModel):
-    """Server → Client: domain determined for a floating request."""
-
-    type: Literal[WsFrameType.floating_domain] = WsFrameType.floating_domain
-    request_id: str
-    domain: WsDomain
-
-
-# ── Agent Config V2 ───────────────────────────────────────────────────
-
-
-class ContentTypeConfig(BaseModel):
     """Per-type extraction config produced by the journey chatbot."""
 
     id: str
@@ -214,34 +220,34 @@ class ContentTypeConfig(BaseModel):
     extraction_prompt: str
 
 
-class AgentConfig(BaseModel):
+class ScoutConfig(BaseModel):
-    """Structured agent configuration (replaces freeform prompt_template)."""
+    """Structured scout configuration (replaces freeform prompt_template)."""
 
-    content_types: list[ContentTypeConfig] = []
+    content_types: list[ScoutContentTypeConfig] = []
     global_rules: list[str] = []
     data_types: list[str] = []
 
 
-# ── Agent Catalog ─────────────────────────────────────────────────────
+# ── Scout Catalog ─────────────────────────────────────────────────────
 
-class AgentCatalogItem(BaseModel):
+class ScoutCatalogItem(BaseModel):
     type: str
     name: str
     description: str
 
 
-class AgentCreationCheckRequest(BaseModel):
+class ScoutCreationCheckRequest(BaseModel):
     active_agents: int = Field(ge=0, default=0)
 
 
-class AgentCreationCheckResponse(BaseModel):
+class ScoutCreationCheckResponse(BaseModel):
     allowed: bool
     tier: BillingTier
     active_agents: int
     limit: int
 
 
-class AgentTriggerRequest(BaseModel):
+class ScoutTriggerRequest(BaseModel):
     directory: str = Field(min_length=1)
     device_id: str = Field(default="")
     agent_id: str | None = None  # FE stable agent ID (electron-store UUID)
@@ -253,9 +259,9 @@ class AgentTriggerRequest(BaseModel):
     last_run_at: int | None = None  # epoch ms from FE — enables incremental scanning
 
 
-# ── Agent Run Log ─────────────────────────────────────────────────────
+# ── Scout Run Log ─────────────────────────────────────────────────────
 
-class AgentRunLogResponse(BaseModel):
+class ScoutRunLogResponse(BaseModel):
     id: str
     agent_id: str
     agent_type: Literal["local", "cloud"]

app/schemas/contextual.py

@@ -0,0 +1,73 @@
+"""Contextual sidebar scope schema and prompt block renderer.
+
+ContextualScope mirrors the TypeScript ContextualScope type sent by the
+Electron renderer when the user opens the side chat anchored to a specific
+view.  The renderer ships camelCase keys; Pydantic's alias_generator maps
+them to snake_case Python attributes automatically.
+"""
+
+from __future__ import annotations
+
+from typing import Literal, Optional
+
+from pydantic import BaseModel, ConfigDict
+from pydantic.alias_generators import to_camel
+
+
+PageType = Literal[
+    "timeline",
+    "tasks",
+    "projects-list",
+    "project",
+    "note",
+]
+
+EntityType = Literal["project", "note", "task", "timeline_event"]
+
+
+class ContextualScope(BaseModel):
+    """Scope payload sent by the Electron renderer for contextual chat.
+
+    The renderer ships camelCase keys (entityType, entityId, ...).  Pydantic's
+    alias generator maps them to snake_case Python attrs.
+    """
+
+    model_config = ConfigDict(populate_by_name=True, alias_generator=to_camel)
+
+    page: PageType
+    entity_type: Optional[EntityType] = None
+    entity_id: Optional[str] = None
+    entity_name: Optional[str] = None
+    project_id: Optional[str] = None
+    char_count: Optional[int] = None
+    counts: Optional[dict[str, int]] = None
+    filters: Optional[dict] = None
+
+
+def render_scope_block(scope: ContextualScope) -> str:
+    """Produce a single-paragraph human-readable summary of the current view
+    for injection into the contextual agent system prompt.
+
+    Never emits internal ids — only names.  The LLM is told to use names in
+    prose; ids travel through tool calls.
+    """
+    if scope.entity_type == "project":
+        c = scope.counts or {}
+        return (
+            f"User is viewing the project {scope.entity_name!r}. "
+            f"{c.get('tasks', 0)} tasks, "
+            f"{c.get('notes', 0)} notes, "
+            f"{c.get('milestones', 0)} milestones."
+        )
+    if scope.entity_type == "note":
+        return (
+            f"User is viewing the note {scope.entity_name!r} "
+            f"({scope.char_count or 0} characters)."
+        )
+    if scope.page == "tasks":
+        return "User is viewing the global Tasks list (all projects)."
+    if scope.page == "timeline":
+        return "User is viewing the global Timeline view."
+    if scope.page == "projects-list":
+        return "User is viewing the Projects list."
+    return f"User is on page {scope.page}."

requirements.txt

@@ -32,8 +32,12 @@ google-auth-oauthlib>=1.2.0
 google-auth-httplib2>=0.2.0
 msal>=1.28.0
 cryptography>=42.0.0
-langfuse>=2.0.0
+pgvector>=0.2.5
+langfuse>=3.3.1
 beautifulsoup4>=4.12.0
 lxml>=5.0.0
 PyYAML>=6.0.0
+apscheduler>=3.10.0
 ruff>=0.8.0
+pypdf>=4.0
+python-docx>=1.1

tests/conftest.py

@@ -17,6 +17,8 @@ from jose import jwt
 from sqlalchemy import StaticPool, event
 from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine
 
+from sqlalchemy import select
+
 from app.config.settings import settings
 from app.db import Base, get_session
 from app.main import app
@@ -134,6 +136,38 @@ def auth_header(tier: str = "power", user_id: str | None = None) -> dict[str, st
     return {"Authorization": f"Bearer {make_jwt(tier, user_id)}"}
 
 
+# ── Convenience aliases and per-tier user fixtures ────────────────────
+
+@pytest_asyncio.fixture
+async def db(db_session: AsyncSession) -> AsyncSession:
+    """Alias for db_session — used by folder quota tests."""
+    return db_session
+
+
+@pytest_asyncio.fixture
+async def test_user_free(db_session: AsyncSession):
+    """Return the seeded free-tier User row."""
+    result = await db_session.execute(
+        select(User).where(User.id == TEST_USER_IDS["free"])
+    )
+    return result.scalar_one()
+
+
+@pytest_asyncio.fixture
+async def test_user_power(db_session: AsyncSession):
+    """Return the seeded power-tier User row."""
+    result = await db_session.execute(
+        select(User).where(User.id == TEST_USER_IDS["power"])
+    )
+    return result.scalar_one()
+
+
+@pytest.fixture
+def auth_headers_free() -> dict[str, str]:
+    """Authorization header for the seeded free-tier user."""
+    return auth_header("free")
+
+
 # ── CLI options ───────────────────────────────────────────────────────
 
 def pytest_addoption(parser):

tests/test_agent_runner.py

@@ -1,808 +0,0 @@
-"""Tests for Step 3.4: agent_runner module.
-
-Coverage:
-  Unit:
-    - _is_overdue      — cron schedule overdue detection
-    - _extract_items_from_content — LLM extraction + JSON parsing + validation
-    - _send_insert_to_client      — tool_call frame construction + timeout
-    - run_local_agent             — end-to-end local agent happy path
-    - run_local_agent             — device offline path
-    - run_local_agent             — file-read timeout path
-    - run_local_agent             — LLM extraction error path
-    - run_cloud_agent             — stub returns error immediately
-    - trigger_pending_runs        — skipped when config is client-owned
-    - trigger_pending_runs        — non-overdue skipped
-    - trigger_pending_runs        — device_id filter for local agents
-
-    Integration:
-        - POST /agents/can-create     — billing eligibility check
-        - POST /agents/trigger        — creates run log + dispatches background task
-"""
-
-from __future__ import annotations
-
-import asyncio
-import json
-import uuid
-from datetime import datetime, timezone
-from unittest.mock import AsyncMock, MagicMock, patch
-
-import pytest
-
-from app.core.agent_runner import (
-    _extract_items_from_content,
-    _is_overdue,
-    _send_insert_to_client,
-    run_cloud_agent,
-    run_local_agent,
-    trigger_pending_runs,
-)
-from app.core.device_manager import DeviceConnectionManager
-from app.db import get_session
-from app.main import app
-from app.models import AgentRunLog, CloudAgentConfig, LocalAgentConfig
-from tests.conftest import TEST_USER_IDS, auth_header
-
-# ---------------------------------------------------------------------------
-# Helpers
-# ---------------------------------------------------------------------------
-
-_FREE_UID = TEST_USER_IDS["free"]
-_PRO_UID = TEST_USER_IDS["pro"]
-
-
-def _make_local_config(user_id: str = _FREE_UID, device_id: str = "dev-001") -> LocalAgentConfig:
-    return LocalAgentConfig(
-        id=str(uuid.uuid4()),
-        user_id=user_id,
-        device_id=device_id,
-        name="Test Local Agent",
-        directory_paths=["/home/user/emails"],
-        data_types=["tasks", "notes"],
-        prompt_template="Extract tasks and notes from this document.",
-        file_extensions=[".txt", ".eml"],
-        schedule_cron="0 */6 * * *",
-        enabled=True,
-        last_run_at=None,
-    )
-
-
-def _make_cloud_config(user_id: str = _FREE_UID) -> CloudAgentConfig:
-    return CloudAgentConfig(
-        id=str(uuid.uuid4()),
-        user_id=user_id,
-        provider="gmail",
-        name="Test Gmail Agent",
-        data_types=["tasks"],
-        prompt_template="Extract tasks from email.",
-        schedule_cron="0 */6 * * *",
-        enabled=True,
-        last_run_at=None,
-    )
-
-
-def _make_run_log(agent_id: str, agent_type: str = "local", user_id: str = _FREE_UID) -> AgentRunLog:
-    return AgentRunLog(
-        id=str(uuid.uuid4()),
-        agent_id=agent_id,
-        agent_type=agent_type,
-        user_id=user_id,
-        status="running",
-        started_at=datetime.now(timezone.utc),
-    )
-
-
-def _make_manager(user_id: str = _FREE_UID, device_id: str = "dev-001") -> DeviceConnectionManager:
-    mgr = DeviceConnectionManager()
-    ws = MagicMock()
-    ws.send_text = AsyncMock()
-    mgr.register(user_id, device_id, ws)
-    return mgr
-
-
-# ---------------------------------------------------------------------------
-# _is_overdue
-# ---------------------------------------------------------------------------
-
-def test_is_overdue_never_run():
-    """An agent that has never run is always overdue."""
-    assert _is_overdue("0 */6 * * *", None) is True
-
-
-def test_is_overdue_very_recently_run():
-    """An agent that just ran is not overdue."""
-    last = datetime.now(timezone.utc)
-    assert _is_overdue("0 */6 * * *", last) is False
-
-
-def test_is_overdue_long_ago():
-    """An agent last run 2 days ago with a 6-hour schedule is overdue."""
-    from datetime import timedelta
-    last = datetime.now(timezone.utc) - timedelta(days=2)
-    assert _is_overdue("0 */6 * * *", last) is True
-
-
-def test_is_overdue_invalid_cron_returns_false():
-    """Unparseable cron must not raise and should return False (fail-safe)."""
-    assert _is_overdue("not a cron", None) is False
-
-
-def test_is_overdue_naive_datetime():
-    """Naive datetime objects are handled without raising."""
-    from datetime import timedelta
-    last = datetime.utcnow() - timedelta(days=1)  # naive
-    # Should not raise.
-    result = _is_overdue("0 */6 * * *", last)
-    assert isinstance(result, bool)
-
-
-# ---------------------------------------------------------------------------
-# _extract_items_from_content
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_extract_items_happy_path():
-    """LLM returns valid JSON array; items with allowed tables are returned."""
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = json.dumps([
-        {"table": "tasks", "data": {"title": "Buy milk", "priority": "high"}},
-        {"table": "notes", "data": {"title": "Meeting recap", "content": "Discussed roadmap"}},
-    ])
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        items = await _extract_items_from_content(
-            "Extract tasks and notes.",
-            "Email body: Buy milk urgently. Notes from meeting: discussed roadmap.",
-            ["tasks", "notes"],
-        )
-
-    assert len(items) == 2
-    assert items[0]["table"] == "tasks"
-    assert items[0]["data"]["title"] == "Buy milk"
-    assert items[1]["table"] == "notes"
-
-
-@pytest.mark.asyncio
-async def test_extract_items_strips_forbidden_fields():
-    """Fields like id, createdAt, isAiSuggested must be stripped from extracted data."""
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = json.dumps([
-        {
-            "table": "tasks",
-            "data": {
-                "title": "Review PR",
-                "id": "should-be-removed",
-                "createdAt": 99999,
-                "isAiSuggested": 0,
-                "isApproved": 1,
-            },
-        }
-    ])
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        items = await _extract_items_from_content("Extract tasks.", "Review the PR.", ["tasks"])
-
-    assert len(items) == 1
-    data = items[0]["data"]
-    assert "id" not in data
-    assert "createdAt" not in data
-    assert "isAiSuggested" not in data
-    assert "isApproved" not in data
-    assert data["title"] == "Review PR"
-
-
-@pytest.mark.asyncio
-async def test_extract_items_invalid_json_returns_empty():
-    """LLM returning invalid JSON must return empty list without raising."""
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = "Sorry, I cannot extract anything."
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        items = await _extract_items_from_content("Extract tasks.", "content", ["tasks"])
-
-    assert items == []
-
-
-@pytest.mark.asyncio
-async def test_extract_items_disallowed_table_filtered():
-    """Items whose table is not in data_types are discarded."""
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = json.dumps([
-        {"table": "tasks", "data": {"title": "Valid task"}},
-        {"table": "projects", "data": {"name": "Should be filtered"}},
-    ])
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        # Only "tasks" is in data_types — "projects" should be filtered.
-        items = await _extract_items_from_content("Extract.", "content", ["tasks"])
-
-    assert len(items) == 1
-    assert items[0]["table"] == "tasks"
-
-
-@pytest.mark.asyncio
-async def test_extract_items_empty_data_types_returns_empty():
-    """If no allowed data_types match, skip LLM call and return immediately."""
-    mock_llm = MagicMock()
-    mock_llm.ainvoke = AsyncMock()
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        items = await _extract_items_from_content("Extract.", "content", [])
-
-    mock_llm.ainvoke.assert_not_called()
-    assert items == []
-
-
-@pytest.mark.asyncio
-async def test_extract_items_llm_error_propagates():
-    """LLM API errors propagate so the caller (run_local_agent) can record them."""
-    mock_llm = MagicMock()
-    mock_llm.ainvoke = AsyncMock(side_effect=RuntimeError("API unavailable"))
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm):
-        with pytest.raises(RuntimeError, match="API unavailable"):
-            await _extract_items_from_content("Extract tasks.", "content", ["tasks"])
-
-
-# ---------------------------------------------------------------------------
-# _send_insert_to_client
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_send_insert_to_client_happy_path():
-    """Frame is sent with isAiSuggested/isApproved added; result is returned."""
-    mgr = _make_manager()
-
-    sent_payloads: list[dict] = []
-    original_send = mgr.send_frame
-
-    async def _capture_send(uid: str, frame: dict) -> None:
-        sent_payloads.append(frame)
-        # Immediately resolve the pending call with a success result.
-        call_id = frame["id"]
-        mgr.resolve_pending_call(uid, call_id, {"row": {"id": "new-id", "title": "Buy milk"}})
-
-    mgr.send_frame = _capture_send  # type: ignore[method-assign]
-
-    result = await _send_insert_to_client(
-        _FREE_UID, "tasks", {"title": "Buy milk", "priority": "high"}, mgr
-    )
-
-    assert len(sent_payloads) == 1
-    payload = sent_payloads[0]
-    assert payload["action"] == "insert"
-    assert payload["table"] == "tasks"
-    assert payload["data"]["title"] == "Buy milk"
-    assert payload["data"]["isAiSuggested"] == 1
-    assert payload["data"]["isApproved"] == 0
-    assert result["row"]["title"] == "Buy milk"
-
-
-@pytest.mark.asyncio
-async def test_send_insert_to_client_timeout():
-    """asyncio.TimeoutError is raised when Electron does not respond."""
-    mgr = _make_manager()
-
-    async def _slow_send(uid: str, frame: dict) -> None:
-        # Never resolve the pending call.
-        pass
-
-    mgr.send_frame = _slow_send  # type: ignore[method-assign]
-
-    with patch("app.core.agent_runner._INSERT_TIMEOUT", 0.05):
-        with pytest.raises(asyncio.TimeoutError):
-            await _send_insert_to_client(_FREE_UID, "tasks", {"title": "X"}, mgr)
-
-
-# ---------------------------------------------------------------------------
-# run_local_agent
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_run_local_agent_device_offline():
-    """run_local_agent marks run as error when device is offline."""
-    config = _make_local_config()
-    run_log = _make_run_log(config.id)
-    mgr = DeviceConnectionManager()  # Empty — no device registered.
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_local_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_finalize.assert_called_once()
-    _args, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("not connected" in e for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_local_agent_happy_path():
-    """End-to-end: files received, LLM extracts one task, insert sent + ack'd."""
-    config = _make_local_config()
-    run_log = _make_run_log(config.id)
-    mgr = _make_manager()
-
-    # Build a fake agent_data frame (will be queued after send).
-    file_frame = {
-        "type": "agent_data",
-        "run_id": run_log.id,
-        "files": [{"path": "/email.eml", "content": "Urgent: fix the bug by Friday."}],
-    }
-    agent_complete_frame = None  # sentinel
-
-    sent_frames: list[dict] = []
-
-    async def _mock_send(uid: str, frame: dict) -> None:
-        sent_frames.append(frame)
-        if frame.get("type") == "agent_run":
-            # Simulate Electron responding with file data then agent_complete.
-            q = mgr.get_agent_data_queue(uid, frame["run_id"])
-            await q.put(file_frame)
-            await q.put(agent_complete_frame)
-        elif frame.get("type") == "tool_call":
-            # Resolve the pending insert immediately.
-            mgr.resolve_pending_call(uid, frame["id"], {"row": {"id": "new-task", "title": "Fix the bug"}})
-
-    mgr.send_frame = _mock_send  # type: ignore[method-assign]
-
-    mock_llm = MagicMock()
-    mock_response = MagicMock()
-    mock_response.content = json.dumps([
-        {"table": "tasks", "data": {"title": "Fix the bug", "priority": "high"}}
-    ])
-    mock_llm.ainvoke = AsyncMock(return_value=mock_response)
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm), \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_local_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_finalize.assert_called_once()
-    _args, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "success"
-    assert kwargs["items_processed"] == 1
-    assert kwargs["items_created"] == 1
-    assert kwargs["errors"] == []
-    assert kwargs["update_config_last_run"] is False
-
-    # Verify agent_run frame was sent.
-    agent_run_frames = [f for f in sent_frames if f.get("type") == "agent_run"]
-    assert len(agent_run_frames) == 1
-    assert agent_run_frames[0]["agent_id"] == config.id
-    assert "paths" in agent_run_frames[0]["config"]
-
-    # Verify insert frame was sent with AI flags.
-    insert_frames = [f for f in sent_frames if f.get("type") == "tool_call"]
-    assert len(insert_frames) == 1
-    assert insert_frames[0]["data"]["isAiSuggested"] == 1
-    assert insert_frames[0]["data"]["isApproved"] == 0
-
-
-@pytest.mark.asyncio
-async def test_run_local_agent_file_read_timeout():
-    """run_local_agent marks run as partial/error when device stops sending files."""
-    config = _make_local_config()
-    run_log = _make_run_log(config.id)
-    mgr = _make_manager()
-
-    async def _mock_send(uid: str, frame: dict) -> None:
-        # Don't put anything in the queue — simulate stalled device.
-        pass
-
-    mgr.send_frame = _mock_send  # type: ignore[method-assign]
-
-    with patch("app.core.agent_runner._FILE_READ_TIMEOUT", 0.1), \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_local_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_finalize.assert_called_once()
-    _args, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"  # No items created, so error (not partial).
-    assert any("timed out" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_local_agent_llm_extraction_error():
-    """LLM errors per-file are recorded; run continues for remaining files."""
-    config = _make_local_config()
-    run_log = _make_run_log(config.id)
-    mgr = _make_manager()
-
-    file_frame = {
-        "type": "agent_data",
-        "run_id": run_log.id,
-        "files": [
-            {"path": "/file1.eml", "content": "Email one."},
-            {"path": "/file2.eml", "content": "Email two."},
-        ],
-    }
-
-    async def _mock_send(uid: str, frame: dict) -> None:
-        if frame.get("type") == "agent_run":
-            q = mgr.get_agent_data_queue(uid, frame["run_id"])
-            await q.put(file_frame)
-            await q.put(None)  # agent_complete sentinel
-
-    mgr.send_frame = _mock_send  # type: ignore[method-assign]
-
-    mock_llm = MagicMock()
-    mock_llm.ainvoke = AsyncMock(side_effect=RuntimeError("LLM boom"))
-
-    with patch("app.core.agent_runner.get_llm", return_value=mock_llm), \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_local_agent(_FREE_UID, config, run_log, mgr)
-
-    _args, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert kwargs["items_processed"] == 2  # Both files attempted.
-    assert kwargs["items_created"] == 0
-    assert len(kwargs["errors"]) == 2  # One error per file.
-
-
-# ---------------------------------------------------------------------------
-# run_cloud_agent (stub)
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_device_offline():
-    """Cloud agent aborts immediately when no device is connected."""
-    config = _make_cloud_config()
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = DeviceConnectionManager()  # empty — no devices registered
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_finalize.assert_called_once()
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("device" in e.lower() or "connected" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_no_oauth_token():
-    """Cloud agent errors when no OAuth token is stored."""
-    config = _make_cloud_config()
-    config.oauth_token_encrypted = None
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize:
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("oauth" in e.lower() or "token" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_token_decrypt_failure():
-    """Cloud agent errors gracefully when the stored token cannot be decrypted."""
-    config = _make_cloud_config()
-    config.oauth_token_encrypted = "this-is-not-valid-fernet-ciphertext"
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    from cryptography.fernet import Fernet as _Fernet
-    valid_key = _Fernet.generate_key().decode()
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize, \
-         patch("app.integrations.settings") as mock_settings:
-        mock_settings.OAUTH_ENCRYPTION_KEY = valid_key
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("decrypt" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_happy_path_gmail():
-    """Cloud agent happy path: Gmail fetch → LLM extraction → inserts → success."""
-    from app.integrations import EmailMessage, encrypt_token
-    from cryptography.fernet import Fernet as _Fernet
-
-    fernet_key = _Fernet.generate_key().decode()
-    credentials = {
-        "token": "access_abc",
-        "refresh_token": "refresh_xyz",
-        "token_uri": "https://oauth2.googleapis.com/token",
-        "client_id": "cid",
-        "client_secret": "csec",
-    }
-
-    config = _make_cloud_config()
-    config.provider = "gmail"
-    config.prompt_template = "Extract tasks from this email."
-    config.data_types = ["tasks"]
-
-    with patch("app.integrations.settings") as ms:
-        ms.OAUTH_ENCRYPTION_KEY = fernet_key
-        config.oauth_token_encrypted = encrypt_token(credentials)
-
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    sample_email = EmailMessage(
-        id="msg001",
-        subject="Action required",
-        sender="boss@company.com",
-        body_text="Please fix the bug by Friday.",
-        date=datetime(2025, 6, 1, 10, 0, tzinfo=timezone.utc),
-    )
-
-    extracted_items = [{"table": "tasks", "data": {"title": "Fix the bug", "priority": "high"}}]
-
-    with patch("app.integrations.settings") as mock_int_settings, \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize, \
-         patch("app.core.agent_runner._extract_items_from_content", new_callable=AsyncMock, return_value=extracted_items) as mock_extract, \
-         patch("app.core.agent_runner._send_insert_to_client", new_callable=AsyncMock, return_value={"ok": True}) as mock_insert, \
-         patch("app.core.agent_runner.async_session"):
-        mock_int_settings.OAUTH_ENCRYPTION_KEY = fernet_key
-
-        mock_gmail = AsyncMock()
-        mock_gmail.fetch_messages = AsyncMock(return_value=[sample_email])
-        mock_gmail.refreshed_credentials = None
-
-        with patch("app.integrations.decrypt_token", return_value=credentials), \
-             patch("app.integrations.get_provider", return_value=mock_gmail):
-            await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    mock_extract.assert_called_once()
-    mock_insert.assert_called_once()
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "success"
-    assert kwargs["items_processed"] == 1
-    assert kwargs["items_created"] == 1
-    assert kwargs["config_type"] == "cloud"
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_provider_fetch_error():
-    """Cloud agent records error status when provider fetch raises RuntimeError."""
-    credentials = {"token": "abc"}
-    config = _make_cloud_config()
-    config.oauth_token_encrypted = "some_encrypted_value"  # non-empty so decrypt step is reached
-    config.prompt_template = "Extract tasks."
-    config.data_types = ["tasks"]
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    mock_provider = AsyncMock()
-    mock_provider.fetch_messages = AsyncMock(side_effect=RuntimeError("API quota exceeded"))
-    mock_provider.refreshed_credentials = None
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_finalize, \
-         patch("app.integrations.decrypt_token", return_value=credentials), \
-         patch("app.integrations.get_provider", return_value=mock_provider), \
-         patch("app.core.agent_runner.async_session"):
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    _, kwargs = mock_finalize.call_args
-    assert kwargs["status"] == "error"
-    assert any("quota" in e.lower() or "fetch" in e.lower() for e in kwargs["errors"])
-
-
-@pytest.mark.asyncio
-async def test_run_cloud_agent_refreshed_token_persisted():
-    """When the provider refreshes its token, the new ciphertext is written to DB."""
-    from app.integrations import encrypt_token
-    from cryptography.fernet import Fernet as _Fernet
-
-    fernet_key = _Fernet.generate_key().decode()
-    credentials = {"token": "old_token", "refresh_token": "rt_old"}
-    fresh_credentials = {"token": "new_token", "refresh_token": "rt_new"}
-
-    config = _make_cloud_config()
-    config.prompt_template = "Extract tasks."
-    config.data_types = ["tasks"]
-
-    with patch("app.integrations.settings") as ms:
-        ms.OAUTH_ENCRYPTION_KEY = fernet_key
-        config.oauth_token_encrypted = encrypt_token(credentials)
-
-    run_log = _make_run_log(config.id, agent_type="cloud")
-    mgr = _make_manager()
-
-    mock_provider = AsyncMock()
-    mock_provider.fetch_messages = AsyncMock(return_value=[])
-    mock_provider.refreshed_credentials = fresh_credentials  # token was refreshed
-
-    # Track DB writes via mock async_session.
-    mock_cfg_row = MagicMock()
-    mock_cfg_row.oauth_token_encrypted = None
-
-    mock_db = AsyncMock()
-    mock_db.__aenter__ = AsyncMock(return_value=mock_db)
-    mock_db.__aexit__ = AsyncMock(return_value=False)
-    mock_db.scalar_one_or_none = AsyncMock(return_value=mock_cfg_row)
-    cfg_result = MagicMock()
-    cfg_result.scalar_one_or_none.return_value = mock_cfg_row
-    mock_db.execute = AsyncMock(return_value=cfg_result)
-    mock_db.commit = AsyncMock()
-
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock), \
-         patch("app.integrations.decrypt_token", return_value=credentials), \
-         patch("app.integrations.get_provider", return_value=mock_provider), \
-         patch("app.integrations.encrypt_token", return_value="new_encrypted") as mock_encrypt, \
-         patch("app.core.agent_runner.async_session", return_value=mock_db), \
-         patch("app.integrations.settings") as mock_int_settings:
-        mock_int_settings.OAUTH_ENCRYPTION_KEY = fernet_key
-        await run_cloud_agent(_FREE_UID, config, run_log, mgr)
-
-    # The new encrypted token should have been written to the config row.
-    mock_encrypt.assert_called_once_with(fresh_credentials)
-    assert mock_cfg_row.oauth_token_encrypted == "new_encrypted"
-
-
-@pytest.mark.asyncio
-async def test_finalize_run_updates_cloud_config_last_run_at():
-    """_finalize_run with config_type='cloud' updates CloudAgentConfig.last_run_at."""
-    from app.core.agent_runner import _finalize_run
-
-    run_log = _make_run_log(str(uuid.uuid4()), agent_type="cloud")
-    run_log.id = str(uuid.uuid4())
-
-    mock_cfg = MagicMock()
-    mock_cfg.last_run_at = None
-
-    cfg_result = MagicMock()
-    cfg_result.scalar_one_or_none.return_value = mock_cfg
-
-    mock_db = AsyncMock()
-    mock_db.__aenter__ = AsyncMock(return_value=mock_db)
-    mock_db.__aexit__ = AsyncMock(return_value=False)
-    mock_db.merge = AsyncMock(return_value=run_log)
-    mock_db.execute = AsyncMock(return_value=cfg_result)
-    mock_db.commit = AsyncMock()
-
-    config_id = str(uuid.uuid4())
-
-    with patch("app.core.agent_runner.async_session", return_value=mock_db):
-        await _finalize_run(
-            run_log,
-            status="success",
-            update_config_last_run=True,
-            config_id=config_id,
-            config_type="cloud",
-        )
-
-    # CloudAgentConfig.last_run_at should have been set.
-    assert mock_cfg.last_run_at is not None
-    mock_db.commit.assert_called()
-
-
-# ---------------------------------------------------------------------------
-# trigger_pending_runs
-# ---------------------------------------------------------------------------
-
-
-@pytest.mark.asyncio
-async def test_trigger_pending_runs_no_overdue():
-    """Pending-run scan is skipped because agent config is client-owned."""
-
-    mgr = _make_manager()
-
-    with patch("app.core.agent_runner.run_local_agent", new_callable=AsyncMock) as mock_run:
-        await trigger_pending_runs(_FREE_UID, "dev-001", mgr)
-
-    mock_run.assert_not_called()
-
-
-@pytest.mark.asyncio
-async def test_trigger_pending_runs_device_id_filter():
-    """Device filtering is no longer backend-managed in pending runs."""
-
-    mgr = _make_manager(device_id="dev-001")
-
-    with patch("app.core.agent_runner.run_local_agent", new_callable=AsyncMock) as mock_run:
-        await trigger_pending_runs(_FREE_UID, "dev-001", mgr)
-
-    mock_run.assert_not_called()
-
-
-@pytest.mark.asyncio
-async def test_trigger_pending_runs_dispatches_overdue():
-    """No pending runs are dispatched by backend after config deprecation."""
-
-    mgr = _make_manager()
-
-    with patch("app.core.agent_runner.run_local_agent", new_callable=AsyncMock) as mock_run:
-        await trigger_pending_runs(_FREE_UID, "dev-001", mgr)
-
-    mock_run.assert_not_called()
-
-
-# ---------------------------------------------------------------------------
-# Integration: POST /agents/can-create and /agents/trigger
-# ---------------------------------------------------------------------------
-
-
-@pytest.fixture(autouse=True)
-def _override_db(db_session):
-    """Route all get_session calls to the test SQLite session."""
-
-    async def _gen():
-        yield db_session
-
-    app.dependency_overrides[get_session] = _gen
-    yield
-    app.dependency_overrides.pop(get_session, None)
-
-
-@pytest.mark.asyncio
-async def test_can_create_agent_allows_when_under_limit(client):
-    """POST /agents/can-create returns allowed=True when under tier limit."""
-    resp = client.post(
-        "/api/v1/agents/can-create",
-        json={"active_agents": 0},
-        headers=auth_header("free"),
-    )
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["allowed"] is True
-    assert body["tier"] == "free"
-    assert body["active_agents"] == 0
-    assert body["limit"] == 2
-
-
-@pytest.mark.asyncio
-async def test_can_create_agent_denies_when_at_limit(client):
-    """POST /agents/can-create returns allowed=False at free-tier limit."""
-    resp = client.post(
-        "/api/v1/agents/can-create",
-        json={"active_agents": 2},
-        headers=auth_header("free"),
-    )
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["allowed"] is False
-    assert body["limit"] == 2
-
-
-@pytest.mark.asyncio
-async def test_trigger_run_local_agent_creates_run_log(client, db_session):
-    """POST /agents/trigger creates a local run log and dispatches background task."""
-    dispatched: list[tuple[str, str]] = []
-
-    async def _fake_run(user_id, cfg, run_log, device_mgr):
-        dispatched.append((user_id, cfg.id))
-
-    def _fake_create_task(coro):
-        coro.close()
-        return MagicMock()
-
-    with patch("app.api.routes.agents.run_local_agent", new_callable=AsyncMock, side_effect=_fake_run), \
-         patch("asyncio.create_task") as mock_create_task:
-        mock_create_task.side_effect = _fake_create_task
-        resp = client.post(
-            "/api/v1/agents/trigger",
-            json={
-                "directory": "/home/user/docs",
-                "what_to_extract": ["task", "note"],
-                "batch_interval": "0 */6 * * *",
-                "custom_agent_prompt": "Extract tasks and notes.",
-                "active_agents": 0,
-            },
-            headers=auth_header("power"),
-        )
-
-    assert resp.status_code == 202
-    data = resp.json()
-    assert isinstance(data["agent_id"], str)
-    assert data["agent_id"]
-    assert data["status"] == "running"
-    assert data["agent_type"] == "local"
-
-    # Verify create_task was called (dispatching background run).
-    mock_create_task.assert_called_once()

tests/test_agent_runner_v2.py

@@ -35,7 +35,7 @@ from unittest.mock import AsyncMock, MagicMock, patch
 import pytest
 import yaml
 
-from app.core.agent_runner import (
+from app.core.scout_runner import (
     _format_metadata,
     _format_projects,
     _get_extraction_rules,
@@ -44,7 +44,7 @@ from app.core.agent_runner import (
 )
 from app.core.device_manager import DeviceConnectionManager
 from app.core.langfuse_client import get_langfuse
-from app.models import AgentRunLog, LocalAgentConfig
+from app.models import ScoutRunLog, LocalScoutConfig
 from tests.conftest import TEST_USER_IDS
 
 # ── Constants ─────────────────────────────────────────────────────────────
@@ -127,8 +127,8 @@ def _make_config(
     agent_config: dict | None = None,
     directory: str = "/emails",
     device_id: str = "dev-001",
-) -> LocalAgentConfig:
+) -> LocalScoutConfig:
-    return LocalAgentConfig(
+    return LocalScoutConfig(
         id=str(uuid.uuid4()),
         user_id=_USER_ID,
         device_id=device_id,
@@ -136,7 +136,7 @@ def _make_config(
         directory_paths=[directory],
         data_types=["tasks", "notes", "timelines"],
         prompt_template="",
-        agent_config=agent_config or _AGENT_CONFIG,
+        scout_config=agent_config or _AGENT_CONFIG,
         file_extensions=[".html", ".eml"],
         schedule_cron="0 */6 * * *",
         enabled=True,
@@ -144,11 +144,11 @@ def _make_config(
     )
 
 
-def _make_run_log(agent_id: str) -> AgentRunLog:
+def _make_run_log(agent_id: str) -> ScoutRunLog:
-    return AgentRunLog(
+    return ScoutRunLog(
         id=str(uuid.uuid4()),
-        agent_id=agent_id,
+        scout_id=agent_id,
-        agent_type="local",
+        scout_type="local",
         user_id=_USER_ID,
         status="running",
         started_at=datetime.now(timezone.utc),
@@ -271,7 +271,7 @@ async def test_2_9_device_offline():
     run_log = _make_run_log(config.id)
     mgr = _make_manager(online=False)
 
-    with patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
+    with patch("app.core.scout_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
         await run_local_agent(_USER_ID, config, run_log, mgr)
 
     _, kwargs = mock_fin.call_args
@@ -295,8 +295,8 @@ async def test_2_10_empty_file():
         projects=[_PROJECTS["alpha"]],
     )
 
-    with patch("app.core.agent_runner._make_agent_executor", return_value=executor), \
+    with patch("app.core.scout_runner._make_agent_executor", return_value=executor), \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
+         patch("app.core.scout_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
         await run_local_agent(_USER_ID, config, run_log, mgr)
 
     _, kwargs = mock_fin.call_args
@@ -326,9 +326,9 @@ async def test_2_8_items_created_count():
             _tool_calls_out.extend(["create_task", "create_note", "update_task"])
         return "Done."
 
-    with patch("app.core.agent_runner._make_agent_executor", return_value=executor), \
+    with patch("app.core.scout_runner._make_agent_executor", return_value=executor), \
-         patch("app.core.agent_runner._run_agent_with_tools", side_effect=mock_run_agent), \
+         patch("app.core.scout_runner._run_agent_with_tools", side_effect=mock_run_agent), \
-         patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
+         patch("app.core.scout_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
         await run_local_agent(_USER_ID, config, run_log, mgr)
 
     _, kwargs = mock_fin.call_args
@@ -377,12 +377,11 @@ async def test_eval_runner(runner_case, pytestconfig):
     ) if lf else nullcontext()
 
     with obs_ctx as obs:
-        with patch("app.core.agent_runner._make_agent_executor", return_value=executor), \
+        with patch("app.core.scout_runner._make_agent_executor", return_value=executor), \
-             patch("app.core.agent_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
+             patch("app.core.scout_runner._finalize_run", new_callable=AsyncMock) as mock_fin:
             await run_local_agent(_USER_ID, config, run_log, mgr)
 
         _, kwargs = mock_fin.call_args
-        inserts = [c for c in calls if c["action"] == "insert"]
         score, comment = _evaluate_case(case, calls, kwargs)
 
         if obs is not None:

tests/test_agent_setup.py

@@ -1,242 +0,0 @@
-"""Tests for the Chatbot Journey endpoints.
-
-Covers:
-  1. Start journey for local agent → session_id + first question, done=False
-  2. Start journey for cloud agent → contextual email-focused question
-  3. Start journey with existing agent_id → session seeded, first question returned
-  4. Start journey with non-existent agent_id → still succeeds (graceful fallback)
-  5. Message: continue conversation → done=False, follow-up question returned
-  6. Message: LLM wraps up → done=True + prompt_template extracted correctly
-  7. Message with max-turns nudge → no crash, returns response
-  8. Invalid session_id → 404
-  9. Expired session → 404
-  10. Session ownership: user B cannot access user A's session
-  11. No JWT on /start → 401
-  12. No JWT on /message → 401
-"""
-
-from __future__ import annotations
-
-import time
-import uuid
-from unittest.mock import AsyncMock, patch
-
-from fastapi.testclient import TestClient
-from sqlalchemy.ext.asyncio import AsyncSession
-
-from app.api.routes.agent_setup import (
-    _SESSION_TTL_SECONDS,
-    _TEMPLATE_END,
-    _TEMPLATE_START,
-    _extract_template,
-    _sessions,
-)
-from app.models import LocalAgentConfig
-from tests.conftest import TEST_USER_IDS, auth_header
-
-# ── Helpers ──────────────────────────────────────────────────────────────
-
-
-def _start(client: TestClient, agent_type: str = "local", agent_id: str | None = None, tier: str = "power") -> dict:
-    body: dict = {"agent_type": agent_type}
-    if agent_id:
-        body["agent_id"] = agent_id
-    resp = client.post("/api/v1/agents/journey/start", json=body, headers=auth_header(tier))
-    return resp
-
-
-def _message(client: TestClient, session_id: str, message: str, tier: str = "power") -> dict:
-    return client.post(
-        "/api/v1/agents/journey/message",
-        json={"session_id": session_id, "message": message},
-        headers=auth_header(tier),
-    )
-
-
-# ── Unit: _extract_template ───────────────────────────────────────────────
-
-
-def test_extract_template_present():
-    text = f"Some preamble.\n{_TEMPLATE_START}\nExtract tasks from emails.\n{_TEMPLATE_END}\nTrailing text."
-    result = _extract_template(text)
-    assert result == "Extract tasks from emails."
-
-
-def test_extract_template_absent():
-    assert _extract_template("No markers here.") is None
-
-
-def test_extract_template_empty_content():
-    text = f"{_TEMPLATE_START}\n{_TEMPLATE_END}"
-    assert _extract_template(text) is None
-
-
-# ── Start journey ─────────────────────────────────────────────────────────
-
-
-def test_start_journey_local(client: TestClient):
-    resp = _start(client, agent_type="local")
-    assert resp.status_code == 200
-    body = resp.json()
-    assert "session_id" in body
-    assert body["done"] is False
-    assert body["prompt_template"] is None
-    assert len(body["message"]) > 0
-    # Local question should be about files/directories
-    assert any(w in body["message"].lower() for w in ("file", "director", "document", "monitor"))
-
-
-def test_start_journey_cloud(client: TestClient):
-    resp = _start(client, agent_type="cloud")
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["done"] is False
-    # Cloud question should mention emails or messages
-    assert any(w in body["message"].lower() for w in ("email", "message", "communication"))
-
-
-def test_start_journey_with_agent_id(client: TestClient, db_session: AsyncSession):
-    """When agent_id is provided, session should be created even if agent doesn't exist."""
-    fake_agent_id = str(uuid.uuid4())
-    resp = _start(client, agent_type="local", agent_id=fake_agent_id)
-    # Should succeed gracefully even if the agent_id doesn't exist
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["done"] is False
-
-
-def test_start_journey_with_existing_agent(client: TestClient, db_session: AsyncSession):
-    """When a real local agent is provided, session is seeded with its prompt_template."""
-    import asyncio
-
-    user_id = TEST_USER_IDS["power"]
-    agent = LocalAgentConfig(
-        id=str(uuid.uuid4()),
-        user_id=user_id,
-        name="Test Agent",
-        device_id="device-1",
-        directory_paths=["/home/user/emails"],
-        data_types=["tasks"],
-        prompt_template="Extract tasks from .eml files.",
-        file_extensions=[".eml"],
-        schedule_cron="0 */6 * * *",
-        enabled=True,
-    )
-
-    async def _seed():
-        db_session.add(agent)
-        await db_session.commit()
-
-    asyncio.get_event_loop().run_until_complete(_seed())
-
-    resp = _start(client, agent_type="local", agent_id=agent.id)
-    assert resp.status_code == 200
-    body = resp.json()
-    assert body["done"] is False
-    # The session should be stored
-    assert body["session_id"] in _sessions
-
-
-def test_start_journey_requires_auth(client: TestClient):
-    resp = client.post("/api/v1/agents/journey/start", json={"agent_type": "local"})
-    assert resp.status_code == 401
-
-
-# ── Message ───────────────────────────────────────────────────────────────
-
-
-def test_message_continues_conversation(client: TestClient):
-    """A mid-journey reply (no template markers) returns done=False."""
-    follow_up = "That looks good. Can you tell me more about priority rules?"
-
-    with patch("app.api.routes.agent_setup._call_llm", new=AsyncMock(return_value=follow_up)):
-        start_resp = _start(client, agent_type="local")
-        assert start_resp.status_code == 200
-        session_id = start_resp.json()["session_id"]
-
-        msg_resp = _message(client, session_id, "I have .eml and .txt files")
-        assert msg_resp.status_code == 200
-        body = msg_resp.json()
-        assert body["done"] is False
-        assert body["prompt_template"] is None
-        assert body["message"] == follow_up
-        assert body["session_id"] == session_id
-
-
-def test_message_produces_template(client: TestClient):
-    """When the LLM includes PROMPT_TEMPLATE markers, done=True and prompt_template is set."""
-    final_template = "Extract tasks from email. Subject → title. 'urgent' → high priority."
-    llm_response = (
-        "Great, I have all the information I need.\n"
-        f"{_TEMPLATE_START}\n{final_template}\n{_TEMPLATE_END}\n"
-    )
-
-    with patch("app.api.routes.agent_setup._call_llm", new=AsyncMock(return_value=llm_response)):
-        start_resp = _start(client, agent_type="cloud")
-        assert start_resp.status_code == 200
-        session_id = start_resp.json()["session_id"]
-
-        msg_resp = _message(client, session_id, "Only invoices from clients")
-        assert msg_resp.status_code == 200
-        body = msg_resp.json()
-        assert body["done"] is True
-        assert body["prompt_template"] == final_template
-        # Session should be cleaned up
-        assert session_id not in _sessions
-
-
-def test_message_invalid_session(client: TestClient):
-    resp = _message(client, "nonexistent-session-id", "hello")
-    assert resp.status_code == 404
-
-
-def test_message_wrong_owner(client: TestClient):
-    """User B cannot access user A's session."""
-    start_resp = _start(client, agent_type="local", tier="power")
-    session_id = start_resp.json()["session_id"]
-
-    # user with "pro" tier (different user_id) tries to send a message
-    resp = client.post(
-        "/api/v1/agents/journey/message",
-        json={"session_id": session_id, "message": "hello"},
-        headers=auth_header("pro"),  # different user
-    )
-    assert resp.status_code == 404
-
-
-def test_message_expired_session(client: TestClient):
-    """Expired sessions return 404."""
-    start_resp = _start(client, agent_type="local")
-    session_id = start_resp.json()["session_id"]
-
-    # Manually expire the session
-    _sessions[session_id].created_at = time.monotonic() - _SESSION_TTL_SECONDS - 1
-
-    resp = _message(client, session_id, "hello")
-    assert resp.status_code == 404
-
-
-def test_message_requires_auth(client: TestClient):
-    resp = client.post(
-        "/api/v1/agents/journey/message",
-        json={"session_id": "any", "message": "hello"},
-    )
-    assert resp.status_code == 401
-
-
-def test_message_max_turns_nudge(client: TestClient):
-    """After _MAX_TURNS user messages, a system nudge is appended but no crash occurs."""
-    from app.api.routes.agent_setup import _MAX_TURNS
-
-    follow_up = "Tell me more about priority rules."
-
-    with patch("app.api.routes.agent_setup._call_llm", new=AsyncMock(return_value=follow_up)):
-        start_resp = _start(client, agent_type="local")
-        session_id = start_resp.json()["session_id"]
-
-        for i in range(_MAX_TURNS):
-            resp = _message(client, session_id, f"Answer {i + 1}")
-            assert resp.status_code == 200
-            # While no template produced, session must still exist
-            if resp.json()["done"]:
-                break  # LLM decided to wrap up early — also fine

tests/test_brief_agent.py

@@ -0,0 +1,163 @@
+"""Tests for Phase 3: brief agent WS frame + REST fallback.
+
+Coverage:
+  - run_home_brief streams non-empty text (mocked _run_single_agent_stream)
+  - run_project_brief with bogus UUID → WS returns stream_end with error, no crash
+  - _build_read_tools uses read-only subset only (no mutating tools)
+  - POST /chat/brief home mode returns {response: "..."}
+  - POST /chat/brief project mode with invalid UUID → 422
+"""
+
+from __future__ import annotations
+
+import uuid
+from collections.abc import AsyncGenerator
+from typing import Any
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from tests.conftest import TEST_USER_IDS, auth_header
+
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+_USER_ID = TEST_USER_IDS["pro"]
+_EMPTY_CONTEXT: dict[str, Any] = {"core_memory": {}}
+
+
+async def _fake_token_stream(*_args, **_kwargs) -> AsyncGenerator[tuple[str, Any], None]:
+    """Fake _run_single_agent_stream that yields two token events."""
+    yield ("token", "Hello")
+    yield ("token", " world")
+
+
+# ---------------------------------------------------------------------------
+# Unit: run_home_brief streams non-empty text
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_run_home_brief_streams_text():
+    with patch(
+        "app.core.brief_agent._run_single_agent_stream",
+        side_effect=_fake_token_stream,
+    ):
+        from app.core.brief_agent import run_home_brief
+
+        chunks: list[str] = []
+        async for event_type, data in run_home_brief(_USER_ID, _EMPTY_CONTEXT):
+            if event_type == "token":
+                chunks.append(str(data))
+
+    assert "".join(chunks) == "Hello world"
+
+
+# ---------------------------------------------------------------------------
+# Unit: run_project_brief streams text with valid UUID
+# ---------------------------------------------------------------------------
+
+@pytest.mark.asyncio
+async def test_run_project_brief_streams_text():
+    project_id = str(uuid.uuid4())
+    with patch(
+        "app.core.brief_agent._run_single_agent_stream",
+        side_effect=_fake_token_stream,
+    ):
+        from app.core.brief_agent import run_project_brief
+
+        chunks: list[str] = []
+        async for event_type, data in run_project_brief(_USER_ID, project_id, _EMPTY_CONTEXT):
+            if event_type == "token":
+                chunks.append(str(data))
+
+    assert "".join(chunks) == "Hello world"
+
+
+# ---------------------------------------------------------------------------
+# Unit: _build_read_tools uses read-only subset (no write tools)
+# ---------------------------------------------------------------------------
+
+def test_build_read_tools_read_only_subset():
+    from app.agents.note_agent import NOTE_READ_TOOLS
+    from app.agents.project_agent import PROJECT_READ_TOOLS
+    from app.agents.task_agent import TASK_READ_TOOLS
+    from app.agents.timeline_agent import TIMELINE_READ_TOOLS
+    from app.core.brief_agent import _build_read_tools
+
+    tools = _build_read_tools(_USER_ID, None)
+    tool_names = {getattr(t, "name", None) or getattr(t, "__name__", str(t)) for t in tools}
+
+    # Read-only exports must be present.
+    for read_list in (TASK_READ_TOOLS, PROJECT_READ_TOOLS, TIMELINE_READ_TOOLS, NOTE_READ_TOOLS):
+        for t in read_list:
+            name = getattr(t, "name", None) or getattr(t, "__name__", str(t))
+            assert name in tool_names, f"Read tool {name!r} missing from _build_read_tools"
+
+    # No mutating tools (e.g. create_task, update_task, delete_task).
+    mutating = {"create_task", "update_task", "delete_task", "create_project",
+                "update_project", "delete_project", "create_note", "update_note",
+                "delete_note", "memory_add", "memory_update", "memory_delete"}
+    overlap = tool_names & mutating
+    assert not overlap, f"Mutating tools in brief read-only subset: {overlap}"
+
+
+# ---------------------------------------------------------------------------
+# Integration: POST /chat/brief — home mode
+# ---------------------------------------------------------------------------
+
+@pytest.fixture(autouse=True)
+def _override_db(db_session):
+    from app.db import get_session
+    from app.main import app
+
+    async def _gen():
+        yield db_session
+
+    app.dependency_overrides[get_session] = _gen
+    yield
+    app.dependency_overrides.pop(get_session, None)
+
+
+@pytest.mark.asyncio
+async def test_rest_brief_home_returns_response(client):
+    async def _fake_home_brief(user_id, context):
+        yield ("token", "Today looks light.")
+
+    with (
+        patch("app.api.routes.chat.run_home_brief", side_effect=_fake_home_brief),
+        patch(
+            "app.api.routes.chat.MemoryMiddleware.enrich_context",
+            new=AsyncMock(return_value={}),
+        ),
+    ):
+        res = client.post(
+            "/api/v1/chat/brief",
+            json={"mode": "home"},
+            headers=auth_header("pro"),
+        )
+
+    assert res.status_code == 200
+    data = res.json()
+    assert data["response"] == "Today looks light."
+
+
+@pytest.mark.asyncio
+async def test_rest_brief_project_invalid_uuid_returns_422(client):
+    res = client.post(
+        "/api/v1/chat/brief",
+        json={"mode": "project", "project_id": "not-a-uuid"},
+        headers=auth_header("pro"),
+    )
+    assert res.status_code == 422
+
+
+@pytest.mark.asyncio
+async def test_rest_brief_project_missing_uuid_returns_422(client):
+    res = client.post(
+        "/api/v1/chat/brief",
+        json={"mode": "project"},
+        headers=auth_header("pro"),
+    )
+    assert res.status_code == 422

tests/test_classify_file.py

@@ -1,184 +0,0 @@
-"""Unit tests for Step 1 file classification (_classify_file).
-
-These tests call the real LLM so they require OPENAI_API_KEY / LLM env vars.
-Run with: pytest tests/test_classify_file.py -v
-
-To run a quick manual check against a real file without the full UI:
-    python -m tests.test_classify_file <path/to/file.txt> [project_name...]
-"""
-
-from __future__ import annotations
-
-import asyncio
-import sys
-
-import pytest
-
-from app.core.agent_runner import _classify_file
-
-
-# ── Fixtures ──────────────────────────────────────────────────────────────
-
-PROJECTS_SAMPLE = [
-    {
-        "id": "aaaa-0001-0000-0000-000000000001",
-        "name": "ARPA Sicilia POC",
-        "status": "active",
-        "aiSummary": "Proof of concept for AI features targeting ARPA Sicilia agency.",
-    },
-    {
-        "id": "bbbb-0002-0000-0000-000000000002",
-        "name": "SNAM AI Meeting Prep",
-        "status": "active",
-        "aiSummary": "AI-assisted preparation of meeting materials for SNAM.",
-    },
-    {
-        "id": "cccc-0003-0000-0000-000000000003",
-        "name": "SFERA+ Wave 2",
-        "status": "active",
-        "aiSummary": "Second wave of the SFERA+ whitelist project.",
-    },
-]
-
-ARPA_EMAIL = """\
-to: roberto.musso@hpe.com; luca.tondin@hpecds.com
-isImportance: normal
-hasAttachment: True
----
-## Body
-Buongiorno,
-
-In riferimento alla riunione di ieri sul POC ARPA Sicilia, vi invio il riassunto
-dei deliverable concordati:
-- Preparare demo entro il 30 marzo
-- Condividere documentazione tecnica con il team ARPA
-- Fissare call di follow-up la prossima settimana
-
-Cordiali saluti
-Roberto Marchetti
-"""
-
-SNAM_EMAIL = """\
-to: roberto.musso@hpe.com
-isImportance: high
-hasAttachment: False
----
-## Body
-Ciao,
-ti invio l'agenda per la riunione SNAM di domani.
-Per favore conferma la tua presenza.
-"""
-
-UNRELATED_EMAIL = """\
-to: roberto.musso@hpe.com
-isImportance: normal
----
-## Body
-Benvenuto nel programma HPE Employee Learning Series.
-Completa la formazione richiesta entro la fine del trimestre.
-"""
-
-
-# ── Tests ─────────────────────────────────────────────────────────────────
-
-
-@pytest.mark.asyncio
-async def test_classify_arpa_matches_existing():
-    project_id, domains, new_name = await _classify_file(
-        file_path="arpa_email.txt",
-        file_content=ARPA_EMAIL,
-        projects=PROJECTS_SAMPLE,
-        config_data_types=["tasks", "notes", "timelines"],
-    )
-    assert project_id == "aaaa-0001-0000-0000-000000000001", (
-        f"Expected ARPA project, got project_id={project_id!r} new_name={new_name!r}"
-    )
-    assert new_name is None
-
-
-@pytest.mark.asyncio
-async def test_classify_snam_matches_existing():
-    project_id, domains, new_name = await _classify_file(
-        file_path="snam_email.txt",
-        file_content=SNAM_EMAIL,
-        projects=PROJECTS_SAMPLE,
-        config_data_types=["tasks", "notes"],
-    )
-    assert project_id == "bbbb-0002-0000-0000-000000000002", (
-        f"Expected SNAM project, got project_id={project_id!r} new_name={new_name!r}"
-    )
-
-
-@pytest.mark.asyncio
-async def test_classify_unrelated_returns_new():
-    project_id, domains, new_name = await _classify_file(
-        file_path="learning_email.txt",
-        file_content=UNRELATED_EMAIL,
-        projects=PROJECTS_SAMPLE,
-        config_data_types=["tasks", "notes"],
-    )
-    assert project_id == "new"
-    assert new_name is not None  # LLM should suggest a name
-
-
-@pytest.mark.asyncio
-async def test_classify_empty_file_returns_new():
-    project_id, domains, new_name = await _classify_file(
-        file_path="empty.txt",
-        file_content="   ",
-        projects=PROJECTS_SAMPLE,
-        config_data_types=["tasks"],
-    )
-    assert project_id == "new"
-
-
-@pytest.mark.asyncio
-async def test_classify_no_projects_returns_new():
-    project_id, domains, new_name = await _classify_file(
-        file_path="arpa_email.txt",
-        file_content=ARPA_EMAIL,
-        projects=[],
-        config_data_types=["tasks", "notes"],
-    )
-    assert project_id == "new"
-    assert new_name is not None
-
-
-# ── CLI quick-test runner ─────────────────────────────────────────────────
-
-
-async def _cli_test(file_path: str, project_names: list[str]) -> None:
-    """Run Step 1 classification against a real file from the CLI."""
-    import json
-    from pathlib import Path
-
-    content = Path(file_path).read_text(encoding="utf-8", errors="replace")
-    projects = [
-        {"id": f"test-id-{i:04d}", "name": name, "status": "active", "aiSummary": ""}
-        for i, name in enumerate(project_names)
-    ]
-
-    print(f"\nClassifying: {file_path}")
-    print(f"Projects in context: {[p['name'] for p in projects]}\n")
-
-    project_id, domains, new_name = await _classify_file(
-        file_path=file_path,
-        file_content=content,
-        projects=projects,
-        config_data_types=["tasks", "notes", "timelines"],
-    )
-
-    result = {
-        "project_id": project_id,
-        "matched_name": next((p["name"] for p in projects if p["id"] == project_id), None),
-        "new_project_name": new_name,
-        "domains": domains,
-    }
-    print(json.dumps(result, indent=2, ensure_ascii=False))
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        print("Usage: python -m tests.test_classify_file <file_path> [project_name ...]")
-        sys.exit(1)
-    asyncio.run(_cli_test(sys.argv[1], sys.argv[2:]))

tests/test_contextual_scope.py

@@ -0,0 +1,52 @@
+import pytest
+from app.schemas.contextual import ContextualScope, render_scope_block
+
+
+def test_render_project_scope():
+    scope = ContextualScope(
+        page="project",
+        entity_type="project",
+        entity_id="p1",
+        entity_name="Acme Q3 launch",
+        counts={"tasks": 12, "notes": 4, "milestones": 3},
+    )
+    block = render_scope_block(scope)
+    assert "Acme Q3 launch" in block
+    assert "12 tasks" in block
+    assert "4 notes" in block
+    assert "3 milestones" in block
+    assert "p1" not in block
+
+
+def test_render_list_scope_no_entity():
+    scope = ContextualScope(page="tasks", entity_type=None)
+    block = render_scope_block(scope)
+    assert "tasks" in block.lower()
+    assert "None" not in block
+
+
+def test_render_note_scope_includes_char_count():
+    scope = ContextualScope(
+        page="note",
+        entity_type="note",
+        entity_id="n1",
+        entity_name="Meeting 14 May",
+        project_id="p1",
+        char_count=4280,
+    )
+    block = render_scope_block(scope)
+    assert "Meeting 14 May" in block
+    assert "4280" in block or "4,280" in block
+
+
+def test_parses_camelcase_payload_from_renderer():
+    payload = {
+        "page": "project",
+        "entityType": "project",
+        "entityId": "p1",
+        "entityName": "Acme",
+        "counts": {"tasks": 5, "notes": 1, "milestones": 2},
+    }
+    scope = ContextualScope.model_validate(payload)
+    assert scope.entity_id == "p1"
+    assert scope.entity_name == "Acme"

tests/test_contextual_ws.py

@@ -0,0 +1,44 @@
+"""Tests for contextual WS frame handlers.
+
+These tests only exercise the new handler functions in device_ws.py and do
+not depend on litellm or the full deep_agent import chain.  They monkeypatch
+run_contextual_stream so no LLM call is made.
+"""
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+
+
+@pytest.mark.asyncio
+async def test_handle_contextual_scope_update_appends_system_message_no_llm(monkeypatch):
+    """_handle_contextual_scope_update must:
+    - call append_system_message on the session buffer
+    - send a contextual_scope_ack back on the socket
+    - make no LLM call
+    """
+    from app.api.routes import device_ws
+
+    ws = AsyncMock()
+    buffer = MagicMock()
+    buffer.append_system_message = MagicMock()
+
+    payload = {
+        "type": "contextual_scope_update",
+        "session_id": "s1",
+        "scope": {
+            "page": "project",
+            "entityType": "project",
+            "entityId": "p1",
+            "entityName": "Acme",
+            "counts": {"tasks": 1, "notes": 0, "milestones": 0},
+        },
+    }
+
+    monkeypatch.setattr(device_ws, "get_session_buffer", lambda *a, **kw: buffer)
+    await device_ws._handle_contextual_scope_update(ws, "user1", payload)
+
+    ws.send_text.assert_awaited_once()
+    import json
+    sent = json.loads(ws.send_text.await_args.args[0])
+    assert sent["type"] == "contextual_scope_ack"
+    assert sent["session_id"] == "s1"
+    buffer.append_system_message.assert_called_once()

tests/test_deep_agent.py

@@ -10,10 +10,10 @@ import pytest
 from langchain_core.messages import AIMessage, ToolMessage
 
 from app.core.deep_agent import (
-    _infer_floating_domain,
+    _build_system_prompt,
+    _datetime_context_injection,
     _normalize_tagged_list_lines,
-    run_floating,
+    _request_context_block,
-    run_floating_stream,
     run_home,
 )
 
@@ -63,7 +63,7 @@ class _FakeLLM:
 async def test_run_home_uses_mocked_tool_result():
     fake_llm = _FakeLLM()
 
-    with patch("app.core.deep_agent.get_llm", return_value=fake_llm), patch(
+    with patch("app.core.deep_agent.get_agent_llm", return_value=fake_llm), patch(
         "app.core.deep_agent._all_tools", return_value=[_FakeTool()]
     ):
         out = await run_home("user-1", "list my tasks", {})
@@ -72,53 +72,6 @@ async def test_run_home_uses_mocked_tool_result():
     assert "Mock Task" in out
 
 
-@pytest.mark.asyncio
-async def test_run_floating_stream_emits_domain_then_tokens_with_mocked_tool_result():
-    fake_llm = _FakeLLM()
-
-    with patch("app.core.deep_agent.get_llm", return_value=fake_llm), patch(
-        "app.core.deep_agent._all_tools", return_value=[_FakeTool()]
-    ):
-        events = []
-        async for event in run_floating_stream(
-            "user-1",
-            "show me timeline updates",
-            {"scope": {"type": "timeline", "id": "tl-1"}},
-        ):
-            events.append(event)
-
-    assert events[0] == (
-        "floating_domain",
-        {"type": "timeline", "id": "tl-1", "section": None},
-    )
-    assert ("token", "stream-") in events
-    assert ("token", "ok") in events
-
-
-@pytest.mark.asyncio
-async def test_infer_floating_domain_prefers_message_intent_over_scope_type():
-    class _ClassifierOnlyLLM:
-        async def ainvoke(self, _messages):
-            return AIMessage(
-                content='{"type":"project","id":"213213-312321-312312-421321","section":"task"}'
-            )
-
-    with patch("app.core.deep_agent.get_llm", return_value=_ClassifierOnlyLLM()):
-        domain = await _infer_floating_domain(
-            "Quali sono i miei task per il progetto X",
-            {
-                "scope": {"type": "timeline"},
-                "resolved_project_id": "213213-312321-312312-421321",
-            },
-        )
-
-    assert domain == {
-        "type": "project",
-        "id": "213213-312321-312312-421321",
-        "section": "task",
-    }
-
-
 def test_normalize_tagged_list_lines_rewrites_mixed_task_lines_to_tag_only_lines():
     raw = (
         "Certo!\n\n"
@@ -155,134 +108,211 @@ def test_normalize_tagged_list_lines_filters_upcoming_timeline_query_to_current_
     assert "<timeline>[tl-future]</timeline>" not in out
 
 
-@pytest.mark.asyncio
+# ── _datetime_context_injection ────────────────────────────────────────────────
-async def test_run_floating_strips_xml_like_tags_from_final_text():
-    fake_llm = _FakeLLM()
 
-    async def _fake_run_single_agent(**_kwargs):
+def _fp(tz: str, now_iso: str) -> dict:
-        return (
+    return {"timezone": tz, "now_iso": now_iso, "date_format": "dd/MM/yyyy", "time_format": "24h"}
-            "Hai 1 task:\\n"
-            "Mail barra in prod <task>[180faff3-507d-4d88-aba8-66f204eb59ef]</task>"
-        )
-
-    with patch("app.core.deep_agent.get_llm", return_value=fake_llm), patch(
-        "app.core.deep_agent._run_single_agent", side_effect=_fake_run_single_agent
-    ):
-        text, _domain = await run_floating(
-            "user-1",
-            "quali task ho?",
-            {"scope": {"type": "task"}},
-        )
-
-    assert "<task>" not in text
-    assert "</task>" not in text
-    assert "[180faff3-507d-4d88-aba8-66f204eb59ef]" not in text
 
 
-@pytest.mark.asyncio
+def _parse_ms(block: str, key: str) -> tuple[int, int]:
-async def test_run_floating_stream_strips_xml_like_tags_from_streamed_text():
+    """Extract [start, end] from a 'key  [start, end]' line in the DATE CONTEXT block."""
-    fake_llm = _FakeLLM()
+    import re
-
+    m = re.search(rf"^{key}\s+\[(\d+),\s*(\d+)\]", block, re.MULTILINE)
-    async def _fake_stream(**_kwargs):
+    assert m, f"Key '{key}' not found in block:\n{block}"
-        yield "token", "Hai 1 task:\\n"
+    return int(m.group(1)), int(m.group(2))
-        yield "token", "Mail barra in prod <task>[180faff3-507d-4d88-aba8-66f204eb59ef]</task>"
-
-    with patch("app.core.deep_agent.get_llm", return_value=fake_llm), patch(
-        "app.core.deep_agent._run_single_agent_stream", side_effect=_fake_stream
-    ):
-        events = []
-        async for event in run_floating_stream(
-            "user-1",
-            "quali task ho?",
-            {"scope": {"type": "task"}},
-        ):
-            events.append(event)
-
-    token_events = [str(data) for event_type, data in events if event_type == "token"]
-    combined = "".join(token_events)
-    assert "<task>" not in combined
-    assert "</task>" not in combined
-    assert "[180faff3-507d-4d88-aba8-66f204eb59ef]" not in combined
 
 
-@pytest.mark.asyncio
+def test_datetime_context_injection_europe_rome_late_evening():
-async def test_run_floating_stream_falls_back_to_final_response_content_when_astream_is_empty():
+    """22:16 CEST on 2026-04-26 — 'tomorrow' must be 2026-04-27 00:00→23:59:59.999 CEST."""
-    class _NoChunkLLM:
+    from zoneinfo import ZoneInfo
-        def __init__(self) -> None:
+    from datetime import datetime, timezone
-            self.calls = 0
 
-        def bind_tools(self, _tools):
+    block = _datetime_context_injection({"format_prefs": _fp("Europe/Rome", "2026-04-26T20:16:02.155Z")})
+    assert "DATE CONTEXT" in block
+    assert "Europe/Rome" in block
+
+    tz = ZoneInfo("Europe/Rome")
+    today_start = int(datetime(2026, 4, 26, tzinfo=tz).timestamp() * 1000)
+    today_end = int(datetime(2026, 4, 27, tzinfo=tz).timestamp() * 1000) - 1
+    tomorrow_start = today_end + 1
+    tomorrow_end = int(datetime(2026, 4, 28, tzinfo=tz).timestamp() * 1000) - 1
+
+    t_s, t_e = _parse_ms(block, "today")
+    assert t_s == today_start
+    assert t_e == today_end
+
+    tm_s, tm_e = _parse_ms(block, "tomorrow")
+    assert tm_s == tomorrow_start
+    assert tm_e == tomorrow_end
+
+    # Sanity: window is exactly 86 400 000 ms (1 day, CEST has no DST jump on this date)
+    assert today_end - today_start + 1 == 86_400_000
+    assert tomorrow_end - tomorrow_start + 1 == 86_400_000
+
+
+def test_datetime_context_injection_utc():
+    """UTC timezone: boundaries are clean UTC midnights."""
+    from datetime import datetime, timezone
+
+    block = _datetime_context_injection({"format_prefs": _fp("UTC", "2026-01-15T10:00:00Z")})
+    t_s, t_e = _parse_ms(block, "today")
+    expected_start = int(datetime(2026, 1, 15, tzinfo=timezone.utc).timestamp() * 1000)
+    assert t_s == expected_start
+    assert t_e == expected_start + 86_400_000 - 1
+
+
+def test_datetime_context_injection_dst_spring_forward():
+    """Europe/Rome DST spring-forward 2026-03-29: that day is 23h, not 24h."""
+    from zoneinfo import ZoneInfo
+    from datetime import datetime
+
+    block = _datetime_context_injection({"format_prefs": _fp("Europe/Rome", "2026-03-29T08:00:00Z")})
+    tz = ZoneInfo("Europe/Rome")
+    day_start = int(datetime(2026, 3, 29, tzinfo=tz).timestamp() * 1000)
+    day_end = int(datetime(2026, 3, 30, tzinfo=tz).timestamp() * 1000) - 1
+
+    t_s, t_e = _parse_ms(block, "today")
+    assert t_s == day_start
+    assert t_e == day_end
+    assert t_e - t_s + 1 == 23 * 3_600_000  # 23-hour day
+
+
+def test_datetime_context_injection_dst_fall_back():
+    """Europe/Rome DST fall-back 2026-10-25: that day is 25h."""
+    from zoneinfo import ZoneInfo
+    from datetime import datetime
+
+    block = _datetime_context_injection({"format_prefs": _fp("Europe/Rome", "2026-10-25T08:00:00Z")})
+    tz = ZoneInfo("Europe/Rome")
+    day_start = int(datetime(2026, 10, 25, tzinfo=tz).timestamp() * 1000)
+    day_end = int(datetime(2026, 10, 26, tzinfo=tz).timestamp() * 1000) - 1
+
+    t_s, t_e = _parse_ms(block, "today")
+    assert t_s == day_start
+    assert t_e == day_end
+    assert t_e - t_s + 1 == 25 * 3_600_000  # 25-hour day
+
+
+def test_datetime_context_injection_year_boundary():
+    """Dec 31 → Jan 1: last_year, this_year, next_month cross year boundary correctly."""
+    from zoneinfo import ZoneInfo
+    from datetime import datetime
+
+    block = _datetime_context_injection({"format_prefs": _fp("UTC", "2026-12-31T23:00:00Z")})
+    tz = ZoneInfo("UTC")
+
+    yr_s, yr_e = _parse_ms(block, "this_year")
+    assert yr_s == int(datetime(2026, 1, 1, tzinfo=tz).timestamp() * 1000)
+    assert yr_e == int(datetime(2027, 1, 1, tzinfo=tz).timestamp() * 1000) - 1
+
+    ly_s, ly_e = _parse_ms(block, "last_year")
+    assert ly_s == int(datetime(2025, 1, 1, tzinfo=tz).timestamp() * 1000)
+    assert ly_e == yr_s - 1
+
+    nm_s, _ = _parse_ms(block, "next_month")
+    assert nm_s == int(datetime(2027, 1, 1, tzinfo=tz).timestamp() * 1000)
+
+
+def test_datetime_context_injection_missing_format_prefs():
+    assert _datetime_context_injection({}) == ""
+    assert _datetime_context_injection({"format_prefs": None}) == ""
+    assert _datetime_context_injection({"format_prefs": "bad"}) == ""
+
+
+# ── _request_context_block ─────────────────────────────────────────────────────
+
+def test_request_context_block_scope_and_project():
+    ctx = {"scope": {"type": "task", "id": "t-1"}, "resolved_project_id": "proj-uuid"}
+    block = _request_context_block(ctx)
+    assert "scope" in block
+    assert "resolved_project_id: proj-uuid" in block
+
+
+def test_request_context_block_empty():
+    assert _request_context_block({}) == ""
+    assert _request_context_block({"scope": None}) == ""
+
+
+# ── _build_system_prompt ───────────────────────────────────────────────────────
+
+def test_build_system_prompt_substitutes_all_slots(monkeypatch):
+    """All five slots must appear in the compiled output; no raw placeholder remains."""
+    # Patch get_prompt_or_fallback to return None prompt_obj so we use fallback .format() path
+    import app.core.deep_agent as da
+    monkeypatch.setattr(da, "get_prompt_or_fallback", lambda name, fallback: (fallback, None))
+
+    ctx = {
+        "format_prefs": _fp("Europe/Rome", "2026-04-26T20:16:02.155Z"),
+        "core_memory": {"language": "it"},
+        "relational_memory": ["Alice — client"],
+        "proactive_hints": ["User prefers morning meetings"],
+        "scope": {"type": "task"},
+        "resolved_project_id": "proj-1",
+    }
+    from app.core.deep_agent import _HOME_SYSTEM_PROMPT
+    text, _ = _build_system_prompt("home_system", _HOME_SYSTEM_PROMPT, ctx)
+
+    # No unresolved placeholders
+    assert "{date_context}" not in text
+    assert "{language_instruction}" not in text
+    assert "{relational_memory}" not in text
+    assert "{proactive_hints}" not in text
+    assert "{request_context}" not in text
+
+    # Content was injected
+    assert "DATE CONTEXT" in text
+    assert "Italian" in text
+    assert "Alice" in text
+    assert "morning meetings" in text
+    assert "proj-1" in text
+
+
+def test_build_system_prompt_empty_format_prefs(monkeypatch):
+    """Missing format_prefs must not raise — date_context slot renders empty string."""
+    import app.core.deep_agent as da
+    monkeypatch.setattr(da, "get_prompt_or_fallback", lambda name, fallback: (fallback, None))
+
+    from app.core.deep_agent import _HOME_SYSTEM_PROMPT
+    text, _ = _build_system_prompt("home_system", _HOME_SYSTEM_PROMPT, {})
+    # Prompt renders without error; date section is empty but structure holds
+    assert "# Date filtering" in text
+    assert "{date_context}" not in text
+
+
+def test_human_message_is_bare_message(monkeypatch):
+    """After the refactor HumanMessage content must equal the raw user message exactly."""
+    import app.core.deep_agent as da
+    from langchain_core.messages import HumanMessage as LCHumanMessage
+
+    captured: list[list] = []
+
+    class _CaptureLLM:
+        def bind_tools(self, _):
             return self
 
-        async def ainvoke(self, _messages):
+        async def ainvoke(self, messages):
-            self.calls += 1
+            captured.append(list(messages))
-            if self.calls == 1:
+            return AIMessage(content="risposta")
-                return AIMessage(
-                    content="",
-                    tool_calls=[
-                        {
-                            "id": "call-1",
-                            "name": "list_tasks",
-                            "args": {},
-                        }
-                    ],
-                )
-            return AIMessage(content="No notes found.")
 
-        async def astream(self, _messages):
+    monkeypatch.setattr(da, "get_prompt_or_fallback", lambda n, f: (f, None))
-            if False:
+    monkeypatch.setattr(da, "get_agent_llm", lambda _: _CaptureLLM())
-                yield None
+    monkeypatch.setattr(da, "_all_tools_for_user", lambda *_: [])
+    monkeypatch.setattr(da, "get_langfuse", lambda: None)
+    monkeypatch.setattr(da, "set_tool_result_collector", lambda _: None)
+    monkeypatch.setattr(da, "clear_tool_result_collector", lambda: None)
 
-    with patch("app.core.deep_agent.get_llm", return_value=_NoChunkLLM()), patch(
+    import asyncio
-        "app.core.deep_agent._all_tools", return_value=[_FakeTool()]
-    ):
-        events = []
-        async for event in run_floating_stream(
-            "user-1",
-            "quali sono le note?",
-            {"scope": {"type": "note"}},
-        ):
-            events.append(event)
 
-    assert events[0][0] == "floating_domain"
+    async def _run():
-    assert ("token", "No notes found.") in events
+        chunks = []
+        ctx = {"format_prefs": _fp("UTC", "2026-04-27T10:00:00Z")}
+        async for ev in da.run_home_stream("u1", "Cosa devo fare domani?", ctx):
+            chunks.append(ev)
 
+    asyncio.get_event_loop().run_until_complete(_run())
 
-@pytest.mark.asyncio
+    assert captured, "LLM was never called"
-async def test_run_floating_returns_fallback_when_sanitization_would_empty_text():
+    messages = captured[0]
-    fake_llm = _FakeLLM()
+    human = next(m for m in messages if isinstance(m, LCHumanMessage))
-
+    assert human.content == "Cosa devo fare domani?"
-    async def _fake_run_single_agent(**_kwargs):
+    assert "Context:" not in human.content
-        return "<task>[180faff3-507d-4d88-aba8-66f204eb59ef]</task>"
-
-    with patch("app.core.deep_agent.get_llm", return_value=fake_llm), patch(
-        "app.core.deep_agent._run_single_agent", side_effect=_fake_run_single_agent
-    ):
-        text, _domain = await run_floating(
-            "user-1",
-            "quali task ho?",
-            {"scope": {"type": "task"}},
-        )
-
-    assert text == "No results found."
-
-
-@pytest.mark.asyncio
-async def test_run_floating_stream_returns_fallback_when_sanitization_would_empty_text():
-    fake_llm = _FakeLLM()
-
-    async def _fake_stream(**_kwargs):
-        yield "token", "<task>[180faff3-507d-4d88-aba8-66f204eb59ef]</task>"
-
-    with patch("app.core.deep_agent.get_llm", return_value=fake_llm), patch(
-        "app.core.deep_agent._run_single_agent_stream", side_effect=_fake_stream
-    ):
-        events = []
-        async for event in run_floating_stream(
-            "user-1",
-            "quali task ho?",
-            {"scope": {"type": "task"}},
-        ):
-            events.append(event)
-
-    assert ("token", "No results found.") in events

tests/test_device_ws.py

@@ -22,7 +22,7 @@ import pytest
 from app.core.device_manager import DeviceConnectionManager
 from app.db import get_session
 from app.main import app
-from app.models import AgentRunLog
+from app.models import ScoutRunLog
 from tests.conftest import TEST_USER_IDS, make_jwt
 
 # ---------------------------------------------------------------------------
@@ -33,9 +33,9 @@ _FREE_UID = TEST_USER_IDS["free"]
 _PRO_UID = TEST_USER_IDS["pro"]
 
 
-def _device_hello(device_id: str = "dev-001", agent_ids: list[str] | None = None) -> str:
+def _device_hello(device_id: str = "dev-001", scout_ids: list[str] | None = None) -> str:
     return json.dumps(
-        {"type": "device_hello", "device_id": device_id, "agent_ids": agent_ids or []}
+        {"type": "device_hello", "device_id": device_id, "scout_ids": scout_ids or []}
     )
 
 
@@ -156,40 +156,6 @@ async def test_manager_unregister_cancels_pending_calls(manager, mock_ws):
     assert fut.cancelled()
 
 
-@pytest.mark.asyncio
-async def test_manager_agent_data_queue(manager, mock_ws):
-    manager.register("user1", "dev-A", mock_ws)
-    q = manager.get_agent_data_queue("user1", "run-xyz")
-    # Put a frame and get it back.
-    frame = {"type": "agent_data", "run_id": "run-xyz", "files": []}
-    await q.put(frame)
-    assert await q.get() == frame
-
-
-@pytest.mark.asyncio
-async def test_manager_agent_data_queue_creates_once(manager, mock_ws):
-    manager.register("user1", "dev-A", mock_ws)
-    q1 = manager.get_agent_data_queue("user1", "run-1")
-    q2 = manager.get_agent_data_queue("user1", "run-1")
-    assert q1 is q2
-
-
-@pytest.mark.asyncio
-async def test_manager_agent_data_queue_raises_when_offline(manager):
-    with pytest.raises(RuntimeError, match="not connected"):
-        manager.get_agent_data_queue("ghost", "run-1")
-
-
-@pytest.mark.asyncio
-async def test_manager_cleanup_agent_data_queue(manager, mock_ws):
-    manager.register("user1", "dev-A", mock_ws)
-    manager.get_agent_data_queue("user1", "run-1")
-    manager.cleanup_agent_data_queue("user1", "run-1")
-    # After cleanup a new queue is created (not the same object).
-    q_new = manager.get_agent_data_queue("user1", "run-1")
-    assert q_new is not None
-
-
 # ---------------------------------------------------------------------------
 # Integration tests — /api/v1/ws/device endpoint
 # ---------------------------------------------------------------------------
@@ -235,7 +201,6 @@ def test_ws_device_invalid_first_frame_closes(client):
 def test_ws_device_tool_result_dispatched(client):
     """tool_result frame is routed to the DeviceConnectionManager."""
     token = make_jwt(tier="free")
-    user_id = TEST_USER_IDS["free"]
 
     from app.core.device_manager import device_manager as dm
 
@@ -266,43 +231,6 @@ def test_ws_device_tool_result_dispatched(client):
     assert any(c["call_id"] == "call-123" for c in captured)
 
 
-def test_ws_device_agent_data_enqueued(client):
-    """agent_data frame is placed in the per-run queue by the message loop."""
-    from app.core.device_manager import device_manager as dm
-
-    token = make_jwt(tier="free")
-    user_id = TEST_USER_IDS["free"]
-
-    # Capture the queue object the message loop accesses.
-    captured_queue: list[asyncio.Queue] = []
-    original_get_queue = dm.get_agent_data_queue
-
-    def _spy_get_queue(uid, run_id):
-        q = original_get_queue(uid, run_id)
-        if not captured_queue:
-            captured_queue.append(q)
-        return q
-
-    with patch.object(dm, "get_agent_data_queue", side_effect=_spy_get_queue):
-        with patch("app.api.routes.device_ws._HEARTBEAT_INTERVAL", 9999):
-            with client.websocket_connect(f"/api/v1/ws/device?token={token}") as ws:
-                ws.send_text(_device_hello("dev-001"))
-                ws.send_text(
-                    json.dumps(
-                        {
-                            "type": "agent_data",
-                            "run_id": "run-XYZ",
-                            "files": [{"path": "/tmp/file.txt", "content": "hello"}],
-                        }
-                    )
-                )
-                ws.close()
-
-    # The queue should have received exactly one frame.
-    assert captured_queue, "queue was never accessed"
-    assert not captured_queue[0].empty()
-
-
 def test_ws_device_disconnect_marks_run_logs_as_error(client, db_session):
     """On disconnect, _mark_runs_disconnected is called with the correct user_id."""
     from app.api.routes import device_ws as _dws
@@ -334,10 +262,10 @@ async def test_mark_runs_disconnected_updates_db(db_session):
 
     user_id = TEST_USER_IDS["free"]
 
-    run_log = AgentRunLog(
+    run_log = ScoutRunLog(
         id=str(uuid.uuid4()),
-        agent_id=str(uuid.uuid4()),
+        scout_id=str(uuid.uuid4()),
-        agent_type="local",
+        scout_type="local",
         user_id=user_id,
         status="running",
         started_at=datetime.now(timezone.utc),
@@ -352,7 +280,7 @@ async def test_mark_runs_disconnected_updates_db(db_session):
     # Verify through the same session factory.
     async with _TestSessionLocal() as s:
         result = await s.execute(
-            select(AgentRunLog).where(AgentRunLog.id == run_log.id)
+            select(ScoutRunLog).where(ScoutRunLog.id == run_log.id)
         )
         updated = result.scalar_one_or_none()
 

tests/test_folder_agent_tool.py

@@ -0,0 +1,139 @@
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from app.agents.folder_agent import (
+    read_project_folder_file,
+    search_project_folder_file,
+)
+
+pytestmark = pytest.mark.asyncio
+
+
+async def test_happy_path():
+    with patch(
+        "app.agents.folder_agent.execute_on_client",
+        new=AsyncMock(return_value={"content": "file body", "kind": "text", "totalSize": 9}),
+    ):
+        out = await read_project_folder_file.ainvoke({"project_id": "p1", "relative_path": "docs/x.md"})
+    assert "file body" in out
+    assert "kind=text" in out
+
+
+async def test_traversal_rejected():
+    out = await read_project_folder_file.ainvoke({"project_id": "p1", "relative_path": "../../etc/passwd"})
+    assert out == "Access denied"
+
+
+async def test_absolute_path_rejected():
+    out = await read_project_folder_file.ainvoke({"project_id": "p1", "relative_path": "C:\\Windows\\foo"})
+    assert out == "Access denied"
+
+
+async def test_missing_file():
+    with patch(
+        "app.agents.folder_agent.execute_on_client",
+        new=AsyncMock(return_value={"content": "", "kind": "missing", "totalSize": 0}),
+    ):
+        out = await read_project_folder_file.ainvoke({"project_id": "p1", "relative_path": "ghost.md"})
+    assert "not found" in out.lower()
+
+
+async def test_pagination_signals_more_available():
+    # Electron returned the first slice, totalSize larger than slice length.
+    with patch(
+        "app.agents.folder_agent.execute_on_client",
+        new=AsyncMock(return_value={"content": "first chunk", "kind": "text", "totalSize": 1000}),
+    ):
+        out = await read_project_folder_file.ainvoke({
+            "project_id": "p1",
+            "relative_path": "big.txt",
+            "offset": 0,
+            "length": 11,
+        })
+    assert "first chunk" in out
+    assert "More content available" in out
+    assert "offset=11" in out
+
+
+async def test_pdf_extracted_then_sliced(monkeypatch):
+    from app.agents import folder_agent
+    monkeypatch.setattr(folder_agent, "_extract_pdf_text", lambda b: "ABC " * 100)
+    with patch(
+        "app.agents.folder_agent.execute_on_client",
+        new=AsyncMock(return_value={"content": "JVBERi0xLg==", "kind": "pdf", "totalSize": 12}),
+    ):
+        out = await read_project_folder_file.ainvoke({
+            "project_id": "p1",
+            "relative_path": "doc.pdf",
+            "offset": 0,
+            "length": 8,
+        })
+    assert "kind=pdf" in out
+    assert "ABC ABC " in out
+    assert "More content available" in out
+
+
+async def test_image_returns_placeholder():
+    with patch(
+        "app.agents.folder_agent.execute_on_client",
+        new=AsyncMock(return_value={"content": "iVBORw0K", "kind": "image", "totalSize": 1024}),
+    ):
+        out = await read_project_folder_file.ainvoke({"project_id": "p1", "relative_path": "logo.png"})
+    assert "image" in out.lower()
+
+
+async def test_search_finds_match_with_context():
+    body = "alpha\nbeta\nthe needle is here\ngamma\ndelta"
+    with patch(
+        "app.agents.folder_agent.execute_on_client",
+        new=AsyncMock(return_value={"content": body, "kind": "text", "totalSize": len(body)}),
+    ):
+        out = await search_project_folder_file.ainvoke({
+            "project_id": "p1",
+            "relative_path": "log.txt",
+            "query": "needle",
+            "context_lines": 1,
+        })
+    assert "needle" in out
+    assert "matches=1" in out
+    # Context lines included
+    assert "beta" in out
+    assert "gamma" in out
+
+
+async def test_search_no_match():
+    with patch(
+        "app.agents.folder_agent.execute_on_client",
+        new=AsyncMock(return_value={"content": "nothing here", "kind": "text", "totalSize": 12}),
+    ):
+        out = await search_project_folder_file.ainvoke({
+            "project_id": "p1",
+            "relative_path": "x.txt",
+            "query": "zzz",
+        })
+    assert "No matches" in out
+
+
+async def test_search_rejects_traversal():
+    out = await search_project_folder_file.ainvoke({
+        "project_id": "p1",
+        "relative_path": "../etc/passwd",
+        "query": "root",
+    })
+    assert out == "Access denied"
+
+
+async def test_search_image_rejected():
+    with patch(
+        "app.agents.folder_agent.execute_on_client",
+        new=AsyncMock(return_value={"content": "b64data", "kind": "image", "totalSize": 100}),
+    ):
+        out = await search_project_folder_file.ainvoke({
+            "project_id": "p1",
+            "relative_path": "logo.png",
+            "query": "anything",
+        })
+    assert "Cannot search" in out

tests/test_folder_indexer.py

@@ -0,0 +1,83 @@
+"""Folder indexer LLM helpers."""
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from app.core.folder_indexer import summarize_text, summarize_image, IndexResult
+
+pytestmark = pytest.mark.asyncio
+
+
+async def test_summarize_text_returns_summary_and_tokens():
+    mock_resp = AsyncMock()
+    mock_resp.content = "Kickoff notes covering scope and deadlines."
+    mock_resp.usage_metadata = {"input_tokens": 320, "output_tokens": 18, "total_tokens": 338}
+    with patch("app.core.folder_indexer._llm_text", new=AsyncMock(return_value=mock_resp)):
+        result = await summarize_text(content="hello world", ext=".md", name="kickoff.md")
+    assert isinstance(result, IndexResult)
+    assert result.summary == "Kickoff notes covering scope and deadlines."
+    assert result.tokens_used == 338
+
+
+async def test_summarize_text_truncates_summary_at_500_chars():
+    mock_resp = AsyncMock()
+    mock_resp.content = "x" * 1000
+    mock_resp.usage_metadata = {"total_tokens": 100}
+    with patch("app.core.folder_indexer._llm_text", new=AsyncMock(return_value=mock_resp)):
+        result = await summarize_text(content="x", ext=".md", name="x.md")
+    assert len(result.summary) <= 500
+
+
+async def test_summarize_image_uses_vision_content_blocks():
+    mock_resp = AsyncMock()
+    mock_resp.content = "Final logo on white background."
+    mock_resp.usage_metadata = {"total_tokens": 500}
+    captured = {}
+
+    async def fake_llm_vision(messages):
+        captured["messages"] = messages
+        return mock_resp
+
+    with patch("app.core.folder_indexer._llm_vision", new=fake_llm_vision):
+        result = await summarize_image(image_b64="iVBORw0KG", mime="image/png")
+
+    assert "Final logo" in result.summary
+    assert result.tokens_used == 500
+    # last message contains an image content block
+    last = captured["messages"][-1]
+    assert any(
+        isinstance(p, dict) and p.get("type") == "image_url"
+        for p in (last.content if isinstance(last.content, list) else [])
+    )
+
+
+async def test_summarize_pdf_extracts_then_summarizes(monkeypatch):
+    # pypdf.PdfReader returns text from pages
+    from app.core import folder_indexer
+    class FakePage:
+        def extract_text(self): return "PDF page content with project info."
+    class FakeReader:
+        pages = [FakePage(), FakePage()]
+    monkeypatch.setattr(folder_indexer, "PdfReader", lambda buf: FakeReader())
+    mock_resp = AsyncMock(); mock_resp.content = "Project info doc."; mock_resp.usage_metadata = {"total_tokens": 50}
+    async def fake_llm(messages): return mock_resp
+    with patch("app.core.folder_indexer._llm_text", new=fake_llm):
+        result = await folder_indexer.summarize_pdf(pdf_b64="SGVsbG8=", name="doc.pdf")
+    assert "Project info" in result.summary
+    assert result.tokens_used == 50
+
+
+async def test_summarize_docx_extracts_then_summarizes(monkeypatch):
+    from app.core import folder_indexer
+    class FakePara:
+        def __init__(self, t): self.text = t
+    class FakeDoc:
+        paragraphs = [FakePara("Heading"), FakePara("Body paragraph one.")]
+    monkeypatch.setattr(folder_indexer, "DocxDocument", lambda buf: FakeDoc())
+    mock_resp = AsyncMock(); mock_resp.content = "Heading and body."; mock_resp.usage_metadata = {"total_tokens": 30}
+    async def fake_llm(messages): return mock_resp
+    with patch("app.core.folder_indexer._llm_text", new=fake_llm):
+        result = await folder_indexer.summarize_docx(docx_b64="UEsDBBQ=", name="doc.docx")
+    assert result.summary == "Heading and body."

tests/test_folder_quota.py

@@ -0,0 +1,94 @@
+"""Folder quota helpers."""
+from __future__ import annotations
+
+from datetime import datetime, timezone
+
+import pytest
+from sqlalchemy import select
+
+from app.billing.quota import (
+    check_folder_quota,
+    add_token_usage,
+    QuotaExceeded,
+)
+from app.models import MonthlyTokenUsage
+
+
+pytestmark = pytest.mark.asyncio
+
+
+async def test_check_folder_quota_free_rejects_above_file_cap(db, test_user_free):
+    with pytest.raises(QuotaExceeded) as exc:
+        await check_folder_quota(
+            user_id=test_user_free.id, tier="free", estimated_files=500, db=db
+        )
+    assert exc.value.reason == "max_files"
+
+
+async def test_check_folder_quota_free_passes_under_cap(db, test_user_free):
+    # No raise
+    await check_folder_quota(
+        user_id=test_user_free.id, tier="free", estimated_files=50, db=db
+    )
+
+
+async def test_check_folder_quota_rejects_when_monthly_exhausted(db, test_user_free):
+    ym = datetime.now(timezone.utc).strftime("%Y-%m")
+    db.add(MonthlyTokenUsage(
+        user_id=test_user_free.id, year_month=ym, feature="folder_index", tokens_used=100_000
+    ))
+    await db.commit()
+    with pytest.raises(QuotaExceeded) as exc:
+        await check_folder_quota(
+            user_id=test_user_free.id, tier="free", estimated_files=10, db=db
+        )
+    assert exc.value.reason == "monthly_tokens"
+
+
+async def test_check_folder_quota_power_unlimited(db, test_user_power):
+    await check_folder_quota(
+        user_id=test_user_power.id, tier="power", estimated_files=999_999, db=db
+    )
+
+
+async def test_add_token_usage_atomic_increment(db, test_user_free):
+    await add_token_usage(user_id=test_user_free.id, feature="folder_index", tokens=1500, db=db)
+    await add_token_usage(user_id=test_user_free.id, feature="folder_index", tokens=2500, db=db)
+    ym = datetime.now(timezone.utc).strftime("%Y-%m")
+    row = (await db.execute(
+        select(MonthlyTokenUsage).where(
+            MonthlyTokenUsage.user_id == test_user_free.id,
+            MonthlyTokenUsage.year_month == ym,
+            MonthlyTokenUsage.feature == "folder_index",
+        )
+    )).scalar_one()
+    assert row.tokens_used == 4000
+
+
+async def test_add_token_usage_returns_exhausted_when_over_cap(db, test_user_free):
+    result = await add_token_usage(
+        user_id=test_user_free.id, feature="folder_index", tokens=150_000, db=db, cap=100_000
+    )
+    assert result.exhausted is True
+    assert result.tokens_used == 150_000
+
+
+def test_quota_check_endpoint_rejects(client, auth_headers_free):
+    res = client.post(
+        "/api/v1/billing/quota/check",
+        json={"feature": "folder_index", "estimated_files": 500},
+        headers=auth_headers_free,
+    )
+    assert res.status_code == 402
+    body = res.json()
+    assert body["detail"]["reason"] == "max_files"
+
+
+def test_quota_check_endpoint_passes(client, auth_headers_free):
+    res = client.post(
+        "/api/v1/billing/quota/check",
+        json={"feature": "folder_index", "estimated_files": 50},
+        headers=auth_headers_free,
+    )
+    assert res.status_code == 200
+    assert res.json() == {"ok": True}

tests/test_integrations.py

@@ -328,7 +328,7 @@ def _make_gmail_message(
 class TestGmailClientFetchMessages:
     """GmailClient.fetch_messages tests with mocked Google API."""
 
-    def _make_client(self) -> "GmailClient":
+    def _make_client(self):
         from app.integrations.gmail import GmailClient
         return GmailClient(_TOKEN_DICT)
 
@@ -509,7 +509,7 @@ def _make_graph_teams_message(
 class TestMSGraphClientFetchEmails:
     """MSGraphClient.fetch_emails tests with mocked httpx."""
 
-    def _make_client(self) -> "MSGraphClient":
+    def _make_client(self):
         from app.integrations.ms_graph import MSGraphClient
         return MSGraphClient(_MS_TOKEN_DICT)
 
@@ -608,7 +608,7 @@ class TestMSGraphClientFetchEmails:
 class TestMSGraphClientFetchMessages:
     """MSGraphClient.fetch_messages (Teams) tests."""
 
-    def _make_client(self) -> "MSGraphClient":
+    def _make_client(self):
         from app.integrations.ms_graph import MSGraphClient
         return MSGraphClient(_MS_TOKEN_DICT)
 

tests/test_journey_v2.py

@@ -1,6 +1,6 @@
 """Tests for Local Agent V2 journey setup (Step 4).
 
-Covers the chatbot journey that produces a structured AgentConfig JSON
+Covers the chatbot journey that produces a structured ScoutConfig JSON
 instead of a freeform prompt_template string.
 
 Unit tests (no LLM)
@@ -16,7 +16,7 @@ Eval test (real LLM + Langfuse scoring)
 ----------------------------------------
   4.1   Journey start explores directory → first reply contains a question
 
-Cases 4.2–4.5 (multi-turn conversations producing a full AgentConfig) are
+Cases 4.2–4.5 (multi-turn conversations producing a full ScoutConfig) are
 non-deterministic and tested manually — results tracked in Langfuse.
 
 Run:
@@ -37,7 +37,7 @@ from unittest.mock import patch
 import pytest
 import yaml
 
-from app.api.routes.agent_setup import (
+from app.api.routes.scout_setup import (
     _CONFIG_END,
     _CONFIG_START,
     _MAX_TURNS,
@@ -48,7 +48,7 @@ from app.api.routes.agent_setup import (
 )
 from app.core.langfuse_client import get_langfuse
 from app.core.ws_context import clear_client_executor, set_client_executor
-from app.schemas import AgentConfig
+from app.schemas import ScoutConfig
 from tests.conftest import TEST_USER_IDS
 
 # ── Constants ─────────────────────────────────────────────────────────────
@@ -179,7 +179,7 @@ def _evaluate_case(case: dict, reply: dict) -> tuple[float, str]:
 
 def test_4_6a_extract_valid_json():
     """_extract_agent_config: valid JSON between markers → returns serialised config."""
-    config = AgentConfig(
+    config = ScoutConfig(
         content_types=[],
         global_rules=["No project = no entity"],
         data_types=["tasks"],
@@ -187,7 +187,7 @@ def test_4_6a_extract_valid_json():
     text = f"Some preamble\n{_CONFIG_START}\n{config.model_dump_json()}\n{_CONFIG_END}\nTrailing"
     result = _extract_agent_config(text)
     assert result is not None
-    parsed = AgentConfig.model_validate_json(result)
+    parsed = ScoutConfig.model_validate_json(result)
     assert parsed.global_rules == ["No project = no entity"]
 
 
@@ -230,7 +230,7 @@ async def test_4_6f_nudge_uses_new_markers():
         # Return plain text — no markers — to trigger the nudge path.
         return "I still need more information from you."
 
-    from app.api.routes.agent_setup import JourneySession
+    from app.api.routes.scout_setup import JourneySession
 
     fake_session = JourneySession(
         session_id=session_id,
@@ -248,7 +248,7 @@ async def test_4_6f_nudge_uses_new_markers():
     _sessions[session_id] = fake_session
 
     try:
-        with patch("app.api.routes.agent_setup._call_llm_with_tools", side_effect=_mock_llm):
+        with patch("app.api.routes.scout_setup._call_llm_with_tools", side_effect=_mock_llm):
             await handle_journey_message(_USER_ID, {
                 "session_id": session_id,
                 "message": "one more message to trigger nudge",

tests/test_manifest_injection.py

@@ -0,0 +1,69 @@
+from __future__ import annotations
+
+from unittest.mock import AsyncMock, patch
+
+import pytest
+
+from app.core.deep_agent import format_folder_manifest, MANIFEST_TOKEN_BUDGET
+
+pytestmark = pytest.mark.asyncio
+
+
+def test_format_folder_manifest_basic():
+    manifest = {
+        "folderPath": "D:\\Acme",
+        "lastScannedAt": "2h ago",
+        "files": [
+            {"relPath": "briefs/kickoff.md", "kind": "text", "summary": "Kickoff notes; scope and deadlines."},
+            {"relPath": "logos/logo-v3.png", "kind": "image", "summary": "Final logo on white."},
+        ],
+    }
+    out = format_folder_manifest(manifest)
+    assert "<linked_folder>" in out
+    assert "/briefs/kickoff.md" in out or "briefs/kickoff.md" in out
+    assert "[text]" in out
+    assert "[image]" in out
+
+
+def test_format_folder_manifest_truncates_past_budget():
+    files = [
+        {"relPath": f"f{i}.md", "kind": "text", "summary": "x" * 100, "mtimeMs": i}
+        for i in range(2000)
+    ]
+    out = format_folder_manifest({"folderPath": "p", "lastScannedAt": "now", "files": files})
+    assert "more files omitted" in out
+    # Rough token check
+    assert len(out) // 4 < MANIFEST_TOKEN_BUDGET + 200
+
+
+def test_format_folder_manifest_null_returns_empty():
+    assert format_folder_manifest(None) == ""
+    assert format_folder_manifest({"files": []}) == ""
+
+
+async def test_brief_multi_project_manifest_top_5_per_project():
+    fake_response = [
+        {
+            "projectId": "p1", "projectName": "Acme", "folderPath": "/a",
+            "lastScannedAt": "now",
+            "files": [
+                {"relPath": f"f{i}.md", "kind": "text", "summary": "s", "mtimeMs": i}
+                for i in range(10)
+            ],
+        },
+        {
+            "projectId": "p2", "projectName": "Beta", "folderPath": "/b",
+            "lastScannedAt": "now",
+            "files": [{"relPath": "x.md", "kind": "text", "summary": "s", "mtimeMs": 1}],
+        },
+    ]
+    with patch(
+        "app.core.deep_agent.execute_on_client",
+        new=AsyncMock(return_value={"projects": fake_response}),
+    ):
+        from app.core.deep_agent import build_brief_multi_project_manifest
+        out = await build_brief_multi_project_manifest()
+    # Project 1 has 10 files, only top 5 by mtimeMs should appear
+    assert out.count("[p1]") <= 5
+    # Project 2 has 1 file, must appear
+    assert "[p2]" in out or "Beta" in out

tests/test_memory_audit.py

@@ -0,0 +1,405 @@
+"""Tests for Phase 7 — weekly audit_memory job.
+
+Coverage:
+  1. audit_memory never raises even if inner work fails.
+  2. _scan_associative_contradictions skips when < 2 decryptable facts.
+  3. _scan_associative_contradictions calls LLM and deletes flagged rows.
+  4. _scan_associative_contradictions is a no-op when LLM fails.
+  5. _scan_associative_contradictions is a no-op when LLM returns non-list.
+  6. _canonicalize_relation_labels skips when no relation rows.
+  7. _canonicalize_relation_labels rewrites variant labels to canonical form.
+  8. _canonicalize_relation_labels is a no-op when LLM fails.
+  9. _canonicalize_relation_labels is a no-op when remap is empty.
+  10. Both helpers work correctly when Langfuse is unavailable (lf=None).
+  11. get_prompt_or_fallback called with correct Langfuse prompt names.
+"""
+
+from __future__ import annotations
+
+import json
+import uuid
+from contextlib import contextmanager, ExitStack
+from datetime import datetime, timezone
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+import pytest_asyncio
+from cryptography.fernet import Fernet
+from sqlalchemy import select
+
+from app.core.memory_maintenance import (
+    _canonicalize_relation_labels,
+    _scan_associative_contradictions,
+    audit_memory,
+)
+from app.db import get_session
+from app.main import app
+from app.models import MemoryAssociative, MemoryRelation, User
+from tests.conftest import TEST_USER_IDS
+
+PRO_USER_ID = TEST_USER_IDS["pro"]
+_FERNET_KEY = Fernet.generate_key().decode()
+_FERNET = Fernet(_FERNET_KEY.encode())
+
+
+# ── DB override ───────────────────────────────────────────────────────────────
+
+@pytest.fixture(autouse=True)
+def _override_db(db_session):
+    async def _gen():
+        yield db_session
+
+    app.dependency_overrides[get_session] = _gen
+    yield
+    app.dependency_overrides.pop(get_session, None)
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+@pytest_asyncio.fixture
+async def pro_user(db_session):
+    result = await db_session.execute(select(User).where(User.id == PRO_USER_ID))
+    user = result.scalar_one()
+    user.encryption_key = _FERNET_KEY
+    await db_session.commit()
+    return user
+
+
+def _enc(text: str) -> str:
+    return _FERNET.encrypt(text.encode()).decode()
+
+
+def _assoc_row(user_id: str, text: str) -> MemoryAssociative:
+    return MemoryAssociative(
+        id=str(uuid.uuid4()),
+        user_id=user_id,
+        content_encrypted=_enc(text),
+        updated_at=datetime.now(timezone.utc),
+    )
+
+
+def _relation_row(user_id: str, subject: str, predicate: str, obj: str) -> MemoryRelation:
+    return MemoryRelation(
+        id=str(uuid.uuid4()),
+        user_id=user_id,
+        subject_label=subject,
+        subject_type="person",
+        predicate=predicate,
+        object_label=obj,
+        object_type="company",
+        confidence=0.8,
+    )
+
+
+def _llm_response(content: str) -> MagicMock:
+    msg = MagicMock()
+    msg.content = content
+    msg.usage_metadata = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
+    return msg
+
+
+def _mock_llm(content: str) -> MagicMock:
+    llm = MagicMock()
+    llm.ainvoke = AsyncMock(return_value=_llm_response(content))
+    return llm
+
+
+@contextmanager
+def _patch_audit(llm_mock, lf=None, prompt_text: str = "fallback {facts}"):
+    """Context manager that patches all external deps for audit helpers."""
+    with ExitStack() as stack:
+        stack.enter_context(
+            patch("app.core.llm.get_agent_llm", return_value=llm_mock)
+        )
+        stack.enter_context(
+            patch("app.core.llm.model_for_agent", return_value="memory-auditor")
+        )
+        stack.enter_context(
+            patch("app.core.memory_maintenance.get_langfuse", return_value=lf)
+        )
+        stack.enter_context(
+            patch(
+                "app.core.memory_maintenance.get_prompt_or_fallback",
+                return_value=(prompt_text, None),
+            )
+        )
+        stack.enter_context(
+            patch(
+                "app.core.memory_maintenance.compile_prompt",
+                side_effect=lambda tmpl, obj, **kw: tmpl.format(**kw) if "{" in tmpl else tmpl,
+            )
+        )
+        yield
+
+
+# ── Test 1: audit_memory never raises ────────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_audit_memory_never_raises_on_missing_user(db_session):
+    """audit_memory with a non-existent user_id must not raise."""
+    await audit_memory(db_session, str(uuid.uuid4()))
+
+
+@pytest.mark.asyncio
+async def test_audit_memory_never_raises_on_llm_failure(db_session, pro_user):
+    """audit_memory must swallow inner exceptions."""
+    llm = MagicMock()
+    llm.ainvoke = AsyncMock(side_effect=RuntimeError("LLM down"))
+
+    with (
+        patch("app.core.llm.get_agent_llm", return_value=llm),
+        patch("app.core.llm.model_for_agent", return_value="memory-auditor"),
+        patch("app.core.memory_maintenance.get_langfuse", return_value=None),
+        patch(
+            "app.core.memory_maintenance.get_prompt_or_fallback",
+            return_value=("p {facts}", None),
+        ),
+        patch("app.core.memory_maintenance.compile_prompt", return_value="compiled"),
+    ):
+        await audit_memory(db_session, PRO_USER_ID)
+
+
+# ── Test 2: _scan skips when < 2 facts ───────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_scan_contradictions_skips_with_one_fact(db_session, pro_user):
+    row = _assoc_row(PRO_USER_ID, "Prefers morning meetings")
+    db_session.add(row)
+    await db_session.commit()
+
+    llm = MagicMock()
+    llm.ainvoke = AsyncMock(return_value=_llm_response("[]"))
+
+    with _patch_audit(llm):
+        await _scan_associative_contradictions(db_session, PRO_USER_ID, _FERNET)
+
+    llm.ainvoke.assert_not_called()
+
+
+# ── Test 3: _scan deletes flagged contradiction ───────────────────────────────
+
+@pytest.mark.asyncio
+async def test_scan_contradictions_deletes_flagged_row(db_session, pro_user):
+    keep = _assoc_row(PRO_USER_ID, "Prefers morning meetings")
+    drop = _assoc_row(PRO_USER_ID, "Never schedules before noon")
+    db_session.add(keep)
+    db_session.add(drop)
+    await db_session.commit()
+
+    deletion_payload = json.dumps([{"delete": drop.id, "reason": "contradicts morning pref"}])
+    llm = _mock_llm(deletion_payload)
+
+    with _patch_audit(llm, prompt_text="p {facts}"):
+        await _scan_associative_contradictions(db_session, PRO_USER_ID, _FERNET)
+
+    result = await db_session.execute(
+        select(MemoryAssociative).where(MemoryAssociative.user_id == PRO_USER_ID)
+    )
+    remaining = result.scalars().all()
+    remaining_ids = {r.id for r in remaining}
+    assert keep.id in remaining_ids
+    assert drop.id not in remaining_ids
+
+
+# ── Test 4: _scan is no-op on LLM failure ────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_scan_contradictions_noop_on_llm_failure(db_session, pro_user):
+    for text in ("Fact A", "Fact B"):
+        db_session.add(_assoc_row(PRO_USER_ID, text))
+    await db_session.commit()
+
+    llm = MagicMock()
+    llm.ainvoke = AsyncMock(side_effect=RuntimeError("LLM down"))
+
+    with _patch_audit(llm, prompt_text="p {facts}"):
+        await _scan_associative_contradictions(db_session, PRO_USER_ID, _FERNET)
+
+    result = await db_session.execute(
+        select(MemoryAssociative).where(MemoryAssociative.user_id == PRO_USER_ID)
+    )
+    assert len(result.scalars().all()) == 2
+
+
+# ── Test 5: _scan is no-op when LLM returns non-list ─────────────────────────
+
+@pytest.mark.asyncio
+async def test_scan_contradictions_noop_on_non_list_response(db_session, pro_user):
+    for text in ("Fact A", "Fact B"):
+        db_session.add(_assoc_row(PRO_USER_ID, text))
+    await db_session.commit()
+
+    llm = _mock_llm('"unexpected string"')
+
+    with _patch_audit(llm, prompt_text="p {facts}"):
+        await _scan_associative_contradictions(db_session, PRO_USER_ID, _FERNET)
+
+    result = await db_session.execute(
+        select(MemoryAssociative).where(MemoryAssociative.user_id == PRO_USER_ID)
+    )
+    assert len(result.scalars().all()) == 2
+
+
+# ── Test 6: _canonicalize skips when no relations ────────────────────────────
+
+@pytest.mark.asyncio
+async def test_canonicalize_skips_when_no_relations(db_session, pro_user):
+    llm = MagicMock()
+    llm.ainvoke = AsyncMock(return_value=_llm_response("[]"))
+
+    with _patch_audit(llm, prompt_text="p {labels}"):
+        await _canonicalize_relation_labels(db_session, PRO_USER_ID)
+
+    llm.ainvoke.assert_not_called()
+
+
+# ── Test 7: _canonicalize rewrites variant labels ────────────────────────────
+
+@pytest.mark.asyncio
+async def test_canonicalize_rewrites_variant_labels(db_session, pro_user):
+    row_a = _relation_row(PRO_USER_ID, "giulia", "works_at", "Acme")
+    row_b = _relation_row(PRO_USER_ID, "Giulia R.", "reports_to", "Marco")
+    row_c = _relation_row(PRO_USER_ID, "Marco", "manages", "Giulia")
+    db_session.add(row_a)
+    db_session.add(row_b)
+    db_session.add(row_c)
+    await db_session.commit()
+
+    groups = json.dumps([
+        {"canonical": "Giulia", "variants": ["giulia", "Giulia R."]}
+    ])
+    llm = _mock_llm(groups)
+
+    with _patch_audit(llm, prompt_text="p {labels}"):
+        await _canonicalize_relation_labels(db_session, PRO_USER_ID)
+
+    await db_session.refresh(row_a)
+    await db_session.refresh(row_b)
+    await db_session.refresh(row_c)
+
+    assert row_a.subject_label == "Giulia"
+    assert row_b.subject_label == "Giulia"
+    assert row_c.object_label == "Giulia"
+    assert row_c.subject_label == "Marco"
+
+
+# ── Test 8: _canonicalize is no-op on LLM failure ────────────────────────────
+
+@pytest.mark.asyncio
+async def test_canonicalize_noop_on_llm_failure(db_session, pro_user):
+    row = _relation_row(PRO_USER_ID, "giulia", "works_at", "Acme")
+    db_session.add(row)
+    await db_session.commit()
+
+    llm = MagicMock()
+    llm.ainvoke = AsyncMock(side_effect=RuntimeError("LLM down"))
+
+    with _patch_audit(llm, prompt_text="p {labels}"):
+        await _canonicalize_relation_labels(db_session, PRO_USER_ID)
+
+    await db_session.refresh(row)
+    assert row.subject_label == "giulia"
+
+
+# ── Test 9: _canonicalize is no-op when remap is empty ───────────────────────
+
+@pytest.mark.asyncio
+async def test_canonicalize_noop_when_remap_empty(db_session, pro_user):
+    row = _relation_row(PRO_USER_ID, "Giulia", "works_at", "Acme")
+    db_session.add(row)
+    await db_session.commit()
+
+    llm = _mock_llm("[]")
+
+    with _patch_audit(llm, prompt_text="p {labels}"):
+        await _canonicalize_relation_labels(db_session, PRO_USER_ID)
+
+    await db_session.refresh(row)
+    assert row.subject_label == "Giulia"
+
+
+# ── Test 10: both helpers work without Langfuse ───────────────────────────────
+
+@pytest.mark.asyncio
+async def test_scan_works_without_langfuse(db_session, pro_user):
+    keep = _assoc_row(PRO_USER_ID, "Prefers dark mode")
+    drop = _assoc_row(PRO_USER_ID, "Prefers light mode")
+    db_session.add(keep)
+    db_session.add(drop)
+    await db_session.commit()
+
+    deletion_payload = json.dumps([{"delete": drop.id, "reason": "contradicts dark mode"}])
+    llm = _mock_llm(deletion_payload)
+
+    with _patch_audit(llm, lf=None, prompt_text="p {facts}"):
+        await _scan_associative_contradictions(db_session, PRO_USER_ID, _FERNET)
+
+    result = await db_session.execute(
+        select(MemoryAssociative).where(MemoryAssociative.user_id == PRO_USER_ID)
+    )
+    remaining_ids = {r.id for r in result.scalars().all()}
+    assert keep.id in remaining_ids
+    assert drop.id not in remaining_ids
+
+
+@pytest.mark.asyncio
+async def test_canonicalize_works_without_langfuse(db_session, pro_user):
+    row = _relation_row(PRO_USER_ID, "giulia", "works_at", "Acme")
+    db_session.add(row)
+    db_session.add(_relation_row(PRO_USER_ID, "Marco", "manages", "Giulia"))
+    await db_session.commit()
+
+    groups = json.dumps([{"canonical": "Giulia", "variants": ["giulia"]}])
+    llm = _mock_llm(groups)
+
+    with _patch_audit(llm, lf=None, prompt_text="p {labels}"):
+        await _canonicalize_relation_labels(db_session, PRO_USER_ID)
+
+    await db_session.refresh(row)
+    assert row.subject_label == "Giulia"
+
+
+# ── Test 11: correct Langfuse prompt names used ───────────────────────────────
+
+@pytest.mark.asyncio
+async def test_scan_uses_correct_langfuse_prompt_name(db_session, pro_user):
+    for text in ("Fact A", "Fact B"):
+        db_session.add(_assoc_row(PRO_USER_ID, text))
+    await db_session.commit()
+
+    llm = _mock_llm("[]")
+    mock_get_prompt = MagicMock(return_value=("p {facts}", None))
+
+    with (
+        patch("app.core.llm.get_agent_llm", return_value=llm),
+        patch("app.core.llm.model_for_agent", return_value="memory-auditor"),
+        patch("app.core.memory_maintenance.get_langfuse", return_value=None),
+        patch("app.core.memory_maintenance.get_prompt_or_fallback", mock_get_prompt),
+        patch("app.core.memory_maintenance.compile_prompt", return_value="compiled"),
+    ):
+        await _scan_associative_contradictions(db_session, PRO_USER_ID, _FERNET)
+
+    mock_get_prompt.assert_called_once()
+    assert mock_get_prompt.call_args[0][0] == "memory_audit_contradictions"
+
+
+@pytest.mark.asyncio
+async def test_canonicalize_uses_correct_langfuse_prompt_name(db_session, pro_user):
+    db_session.add(_relation_row(PRO_USER_ID, "Giulia", "works_at", "Acme"))
+    db_session.add(_relation_row(PRO_USER_ID, "Marco", "manages", "Acme"))
+    await db_session.commit()
+
+    llm = _mock_llm("[]")
+    mock_get_prompt = MagicMock(return_value=("p {labels}", None))
+
+    with (
+        patch("app.core.llm.get_agent_llm", return_value=llm),
+        patch("app.core.llm.model_for_agent", return_value="memory-auditor"),
+        patch("app.core.memory_maintenance.get_langfuse", return_value=None),
+        patch("app.core.memory_maintenance.get_prompt_or_fallback", mock_get_prompt),
+        patch("app.core.memory_maintenance.compile_prompt", return_value="compiled"),
+    ):
+        await _canonicalize_relation_labels(db_session, PRO_USER_ID)
+
+    mock_get_prompt.assert_called_once()
+    assert mock_get_prompt.call_args[0][0] == "memory_audit_canonicalize"

tests/test_memory_extraction.py

@@ -0,0 +1,345 @@
+"""Tests for Phase 2 — Mem0-style Extract/Update pipeline.
+
+Coverage:
+  2.1 extract_candidates returns valid ExtractionResult with mocked LLM.
+  2.2 decide_action — all 4 branches (ADD/UPDATE/DELETE/NOOP + empty existing).
+  2.3 run_extraction end-to-end with mocked LLM writes expected rows.
+  2.4 _dispatch_extraction — Pro user triggers realtime task; Free enqueues row.
+"""
+
+from __future__ import annotations
+
+import json
+import uuid
+from unittest.mock import AsyncMock, MagicMock, patch
+
+import pytest
+import pytest_asyncio
+from cryptography.fernet import Fernet
+from sqlalchemy import select
+
+from app.core.memory_extraction import (
+    ExtractionResult,
+    MemoryCandidate,
+    decide_action,
+    extract_candidates,
+    run_extraction,
+)
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import get_session
+from app.main import app
+from app.models import ExtractionQueue, MemoryCore, User
+from tests.conftest import TEST_USER_IDS
+
+
+PRO_USER_ID = TEST_USER_IDS["pro"]
+FREE_USER_ID = TEST_USER_IDS["free"]
+_FERNET_KEY = Fernet.generate_key().decode()
+
+
+# ── DB override ───────────────────────────────────────────────────────────────
+
+@pytest.fixture(autouse=True)
+def _override_db(db_session):
+    async def _gen():
+        yield db_session
+
+    app.dependency_overrides[get_session] = _gen
+    yield
+    app.dependency_overrides.pop(get_session, None)
+
+
+# ── Helpers ───────────────────────────────────────────────────────────────────
+
+@pytest_asyncio.fixture
+async def pro_user(db_session):
+    """Update the seeded pro user to have an encryption_key."""
+    result = await db_session.execute(select(User).where(User.id == PRO_USER_ID))
+    user = result.scalar_one()
+    user.encryption_key = _FERNET_KEY
+    await db_session.commit()
+    return user
+
+
+@pytest_asyncio.fixture
+async def free_user(db_session):
+    """Update the seeded free user to have an encryption_key."""
+    result = await db_session.execute(select(User).where(User.id == FREE_USER_ID))
+    user = result.scalar_one()
+    user.encryption_key = _FERNET_KEY
+    await db_session.commit()
+    return user
+
+
+def _make_llm_response(content: str) -> MagicMock:
+    msg = MagicMock()
+    msg.content = content
+    msg.usage_metadata = {"input_tokens": 10, "output_tokens": 5, "total_tokens": 15}
+    return msg
+
+
+# ── TASK 2.1 — extract_candidates ────────────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_extract_candidates_returns_valid_result():
+    payload = {
+        "candidates": [
+            {
+                "type": "fact",
+                "content": "User's CFO is Giulia",
+                "target_tier": "core",
+                "subject": None,
+                "predicate": None,
+                "object": None,
+                "confidence": 0.85,
+            }
+        ]
+    }
+    mock_response = _make_llm_response(json.dumps(payload))
+
+    with (
+        patch("app.core.memory_extraction.get_agent_llm") as mock_get_llm,
+        patch("app.core.memory_extraction.get_langfuse", return_value=None),
+        patch("app.core.memory_extraction.get_prompt_or_fallback") as mock_prompt,
+    ):
+        mock_prompt.return_value = (
+            "system prompt {last_turn} {core_memory} {recent_episodes}",
+            None,
+        )
+        llm_instance = MagicMock()
+        llm_instance.bind.return_value = llm_instance
+        llm_instance.ainvoke = AsyncMock(return_value=mock_response)
+        mock_get_llm.return_value = llm_instance
+
+        result = await extract_candidates(
+            last_turn="User: My CFO is Giulia\nAssistant: Noted.",
+            core_memory={},
+            recent_episodes=[],
+        )
+
+    assert isinstance(result, ExtractionResult)
+    assert len(result.candidates) == 1
+    assert result.candidates[0].type == "fact"
+    assert "Giulia" in result.candidates[0].content
+    assert result.candidates[0].confidence == 0.85
+
+
+@pytest.mark.asyncio
+async def test_extract_candidates_returns_empty_on_llm_failure():
+    with (
+        patch("app.core.memory_extraction.get_agent_llm") as mock_get_llm,
+        patch("app.core.memory_extraction.get_langfuse", return_value=None),
+        patch("app.core.memory_extraction.get_prompt_or_fallback") as mock_prompt,
+    ):
+        mock_prompt.return_value = ("prompt {last_turn} {core_memory} {recent_episodes}", None)
+        llm_instance = MagicMock()
+        llm_instance.bind.return_value = llm_instance
+        llm_instance.ainvoke = AsyncMock(side_effect=RuntimeError("LLM down"))
+        mock_get_llm.return_value = llm_instance
+
+        result = await extract_candidates("turn", {}, [])
+
+    assert isinstance(result, ExtractionResult)
+    assert result.candidates == []
+
+
+# ── TASK 2.2 — decide_action ─────────────────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_decide_action_add_when_no_existing():
+    candidate = MemoryCandidate(type="fact", content="CFO is Giulia", target_tier="core")
+    action = await decide_action(candidate, existing=[])
+    assert action == "ADD"
+
+
+@pytest.mark.asyncio
+async def test_decide_action_noop():
+    candidate = MemoryCandidate(type="fact", content="CFO is Giulia", target_tier="core")
+    mock_response = _make_llm_response("NOOP")
+
+    with (
+        patch("app.core.memory_extraction.get_agent_llm") as mock_get_llm,
+        patch("app.core.memory_extraction.get_langfuse", return_value=None),
+        patch("app.core.memory_extraction.get_prompt_or_fallback") as mock_prompt,
+    ):
+        mock_prompt.return_value = ("p {candidate} {existing_memories}", None)
+        llm_instance = MagicMock()
+        llm_instance.ainvoke = AsyncMock(return_value=mock_response)
+        mock_get_llm.return_value = llm_instance
+
+        action = await decide_action(candidate, existing=["CFO is Giulia"])
+
+    assert action == "NOOP"
+
+
+@pytest.mark.asyncio
+async def test_decide_action_update():
+    candidate = MemoryCandidate(type="fact", content="CFO is Marco", target_tier="core")
+    mock_response = _make_llm_response("UPDATE")
+
+    with (
+        patch("app.core.memory_extraction.get_agent_llm") as mock_get_llm,
+        patch("app.core.memory_extraction.get_langfuse", return_value=None),
+        patch("app.core.memory_extraction.get_prompt_or_fallback") as mock_prompt,
+    ):
+        mock_prompt.return_value = ("p {candidate} {existing_memories}", None)
+        llm_instance = MagicMock()
+        llm_instance.ainvoke = AsyncMock(return_value=mock_response)
+        mock_get_llm.return_value = llm_instance
+
+        action = await decide_action(candidate, existing=["CFO is Giulia"])
+
+    assert action == "UPDATE"
+
+
+@pytest.mark.asyncio
+async def test_decide_action_delete():
+    candidate = MemoryCandidate(type="fact", content="No longer have a CFO", target_tier="core")
+    mock_response = _make_llm_response("DELETE")
+
+    with (
+        patch("app.core.memory_extraction.get_agent_llm") as mock_get_llm,
+        patch("app.core.memory_extraction.get_langfuse", return_value=None),
+        patch("app.core.memory_extraction.get_prompt_or_fallback") as mock_prompt,
+    ):
+        mock_prompt.return_value = ("p {candidate} {existing_memories}", None)
+        llm_instance = MagicMock()
+        llm_instance.ainvoke = AsyncMock(return_value=mock_response)
+        mock_get_llm.return_value = llm_instance
+
+        action = await decide_action(candidate, existing=["CFO is Giulia"])
+
+    assert action == "DELETE"
+
+
+@pytest.mark.asyncio
+async def test_decide_action_defaults_add_on_llm_failure():
+    candidate = MemoryCandidate(type="fact", content="CFO is Marco", target_tier="core")
+
+    with (
+        patch("app.core.memory_extraction.get_agent_llm") as mock_get_llm,
+        patch("app.core.memory_extraction.get_langfuse", return_value=None),
+        patch("app.core.memory_extraction.get_prompt_or_fallback") as mock_prompt,
+    ):
+        mock_prompt.return_value = ("p {candidate} {existing_memories}", None)
+        llm_instance = MagicMock()
+        llm_instance.ainvoke = AsyncMock(side_effect=RuntimeError("LLM down"))
+        mock_get_llm.return_value = llm_instance
+
+        action = await decide_action(candidate, existing=["old memory"])
+
+    assert action == "ADD"
+
+
+# ── TASK 2.3 — run_extraction end-to-end ─────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_run_extraction_writes_core_candidate(db_session, pro_user):
+    """'My CFO is Giulia' → fact candidate → core row written."""
+    fact_payload = {
+        "candidates": [
+            {
+                "type": "fact",
+                "content": "User prefers morning meetings",
+                "target_tier": "core",
+                "confidence": 0.8,
+            }
+        ]
+    }
+
+    def _mock_llm_response(content: str):
+        msg = MagicMock()
+        msg.content = content
+        msg.usage_metadata = {}
+        return msg
+
+    call_count = 0
+
+    async def _ainvoke_side_effect(messages):
+        nonlocal call_count
+        call_count += 1
+        if call_count == 1:
+            # extract_candidates call
+            return _mock_llm_response(json.dumps(fact_payload))
+        # decide_action — no existing → short-circuits to ADD without LLM
+        return _mock_llm_response("ADD")
+
+    with (
+        patch("app.core.memory_extraction.get_agent_llm") as mock_get_llm,
+        patch("app.core.memory_extraction.get_langfuse", return_value=None),
+        patch(
+            "app.core.memory_extraction.get_prompt_or_fallback",
+            side_effect=lambda name, fb: (
+                ("p {last_turn} {core_memory} {recent_episodes}", None)
+                if name == "memory_extraction"
+                else ("p {candidate} {existing_memories}", None)
+            ),
+        ),
+    ):
+        llm_instance = MagicMock()
+        llm_instance.bind.return_value = llm_instance
+        llm_instance.ainvoke = AsyncMock(side_effect=_ainvoke_side_effect)
+        mock_get_llm.return_value = llm_instance
+
+        await run_extraction(
+            db=db_session,
+            user_id=PRO_USER_ID,
+            last_user_msg="My CFO is Giulia",
+            last_assistant_msg="Noted, I will remember that.",
+            session_id="test-session",
+        )
+
+    # core row should exist
+    result = await db_session.execute(
+        select(MemoryCore).where(MemoryCore.user_id == PRO_USER_ID)
+    )
+    rows = result.scalars().all()
+    assert len(rows) >= 1
+    fernet = Fernet(_FERNET_KEY.encode())
+    values = [fernet.decrypt(r.value_encrypted.encode()).decode() for r in rows]
+    assert any("morning meetings" in v for v in values)
+
+
+# ── TASK 2.4 — dispatch ───────────────────────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_dispatch_realtime_for_pro(db_session, pro_user):
+    """Pro user: asyncio.create_task called (not queue row)."""
+    middleware = MemoryMiddleware(db_session)
+
+    with (
+        patch("app.core.memory_middleware.asyncio.create_task") as mock_task,
+        patch("app.billing.tier_manager.tier_manager.check_feature", return_value=True),
+    ):
+        await middleware._dispatch_extraction(
+            user_id=PRO_USER_ID,
+            episode_id=str(uuid.uuid4()),
+            last_user_msg="hello",
+            last_assistant_msg="hi",
+            session_id=None,
+        )
+
+    mock_task.assert_called_once()
+
+
+@pytest.mark.asyncio
+async def test_dispatch_queue_for_free(db_session, free_user):
+    """Free user: ExtractionQueue row inserted."""
+    middleware = MemoryMiddleware(db_session)
+    ep_id = str(uuid.uuid4())
+
+    with patch("app.billing.tier_manager.tier_manager.check_feature", return_value=False):
+        await middleware._dispatch_extraction(
+            user_id=FREE_USER_ID,
+            episode_id=ep_id,
+            last_user_msg="hello",
+            last_assistant_msg="hi",
+            session_id=None,
+        )
+
+    result = await db_session.execute(
+        select(ExtractionQueue).where(ExtractionQueue.user_id == FREE_USER_ID)
+    )
+    rows = result.scalars().all()
+    assert len(rows) == 1
+    assert rows[0].episode_id == ep_id

tests/test_memory_middleware.py

@@ -12,13 +12,14 @@ from __future__ import annotations
 
 import json
 import uuid
-from unittest.mock import patch
+from unittest.mock import AsyncMock, MagicMock, patch
 
 import pytest
 import pytest_asyncio
 from cryptography.fernet import Fernet
 from sqlalchemy import select
 
+from app.core.embeddings import embed_text
 from app.core.memory_middleware import MemoryMiddleware
 from app.db import get_session
 from app.main import app
@@ -321,7 +322,7 @@ def test_home_request_calls_memory_middleware(client):
     ):
         with client.websocket_connect(f"/api/v1/ws/device?token={token}") as ws:
             ws.send_text(json.dumps({
-                "type": "device_hello", "device_id": "dev-mem", "agent_ids": []
+                "type": "device_hello", "device_id": "dev-mem", "scout_ids": []
             }))
             ws.send_text(json.dumps({
                 "type": "home_request",
@@ -341,3 +342,33 @@ def test_home_request_calls_memory_middleware(client):
     stored_session_id, stored_message = store_calls[0][1], store_calls[0][2]
     assert stored_session_id == session_id
     assert stored_message == "Show tasks"
+
+
+# ── embed_text ─────────────────────────────────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_embed_text_returns_1536_floats():
+    """embed_text returns a 1536-dim float list when OpenAI responds successfully."""
+    fake_embedding = [0.1] * 1536
+
+    mock_response = MagicMock()
+    mock_response.data = [MagicMock(embedding=fake_embedding)]
+
+    mock_client = MagicMock()
+    mock_client.embeddings.create = AsyncMock(return_value=mock_response)
+
+    with patch("app.core.embeddings.AsyncOpenAI", return_value=mock_client):
+        result = await embed_text("test text")
+
+    assert result is not None
+    assert len(result) == 1536
+    assert all(isinstance(x, float) for x in result)
+
+
+@pytest.mark.asyncio
+async def test_embed_text_returns_none_on_failure():
+    """embed_text returns None when OpenAI raises; must not propagate the exception."""
+    with patch("app.core.embeddings.AsyncOpenAI", side_effect=Exception("no key")):
+        result = await embed_text("test text")
+
+    assert result is None

tests/test_memory_proactive.py

@@ -0,0 +1,153 @@
+"""Tests for Phase 5 — proactive hints surfacing.
+
+Coverage:
+  1. _proactive_hints_injection returns correct section for seeded hints
+  2. _proactive_hints_injection returns empty string when no hints
+  3. enrich_context includes proactive_hints key from MemoryProactive row
+  4. System prompt includes proactive line when row exists + confidence >= threshold
+  5. TierManager.check_feature returns True for power/team, False for free/pro
+"""
+
+from __future__ import annotations
+
+import uuid
+
+import pytest
+import pytest_asyncio
+from cryptography.fernet import Fernet
+from sqlalchemy import select
+
+from app.billing.tier_manager import tier_manager
+from app.core.deep_agent import _proactive_hints_injection
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import get_session
+from app.main import app
+from app.models import MemoryProactive, User
+from tests.conftest import TEST_USER_IDS
+
+
+USER_ID = TEST_USER_IDS["power"]
+_FERNET_KEY = Fernet.generate_key().decode()
+
+
+# ── DB override ───────────────────────────────────────────────────────────────
+
+@pytest.fixture(autouse=True)
+def _override_db(db_session):
+    async def _gen():
+        yield db_session
+
+    app.dependency_overrides[get_session] = _gen
+    yield
+    app.dependency_overrides.pop(get_session, None)
+
+
+# ── Fixtures ──────────────────────────────────────────────────────────────────
+
+@pytest_asyncio.fixture
+async def user_with_key(db_session):
+    result = await db_session.execute(select(User).where(User.id == USER_ID))
+    user = result.scalar_one()
+    user.encryption_key = _FERNET_KEY
+    await db_session.commit()
+    return user
+
+
+def _enc(plaintext: str) -> str:
+    return Fernet(_FERNET_KEY.encode()).encrypt(plaintext.encode()).decode()
+
+
+# ── _proactive_hints_injection unit tests ─────────────────────────────────────
+
+def test_proactive_hints_injection_with_hints():
+    context = {"proactive_hints": ["Works late on Thursdays", "Prefers bullet points"]}
+    result = _proactive_hints_injection(context)
+    assert "I noticed" in result
+    assert "Works late on Thursdays" in result
+    assert "Prefers bullet points" in result
+
+
+def test_proactive_hints_injection_empty():
+    assert _proactive_hints_injection({}) == ""
+    assert _proactive_hints_injection({"proactive_hints": []}) == ""
+    assert _proactive_hints_injection({"proactive_hints": None}) == ""
+
+
+def test_proactive_hints_injection_truncates_long_hints():
+    hints = ["x" * 200] * 10
+    result = _proactive_hints_injection({"proactive_hints": hints})
+    assert len(result) <= 600
+    assert result.endswith("...")
+
+
+# ── enrich_context includes proactive hints ───────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_enrich_context_returns_proactive_hints(db_session, user_with_key):
+    pattern = "Always checks tasks before meetings"
+    db_session.add(MemoryProactive(
+        id=str(uuid.uuid4()),
+        user_id=USER_ID,
+        pattern_encrypted=_enc(pattern),
+        confidence=0.8,
+        source="inferred",
+    ))
+    await db_session.commit()
+
+    middleware = MemoryMiddleware(db_session)
+    ctx = await middleware.enrich_context(USER_ID, "test message")
+
+    assert "proactive_hints" in ctx
+    assert pattern in ctx["proactive_hints"]
+
+
+@pytest.mark.asyncio
+async def test_enrich_context_excludes_low_confidence_proactive(db_session, user_with_key):
+    pattern = "Low confidence pattern"
+    db_session.add(MemoryProactive(
+        id=str(uuid.uuid4()),
+        user_id=USER_ID,
+        pattern_encrypted=_enc(pattern),
+        confidence=0.1,
+        source="inferred",
+    ))
+    await db_session.commit()
+
+    middleware = MemoryMiddleware(db_session)
+    ctx = await middleware.enrich_context(USER_ID, "test message")
+
+    hints = ctx.get("proactive_hints", [])
+    assert pattern not in hints
+
+
+# ── proactive hints appear in system prompt string ───────────────────────────
+
+@pytest.mark.asyncio
+async def test_proactive_hints_in_system_prompt_string(db_session, user_with_key):
+    pattern = "Frequently requests end-of-day summaries"
+    db_session.add(MemoryProactive(
+        id=str(uuid.uuid4()),
+        user_id=USER_ID,
+        pattern_encrypted=_enc(pattern),
+        confidence=0.75,
+        source="inferred",
+    ))
+    await db_session.commit()
+
+    middleware = MemoryMiddleware(db_session)
+    ctx = await middleware.enrich_context(USER_ID, "summarize my day")
+
+    system_prompt_suffix = _proactive_hints_injection(ctx)
+    assert pattern in system_prompt_suffix
+
+
+# ── Tier gate ─────────────────────────────────────────────────────────────────
+
+@pytest.mark.parametrize("tier,expected", [
+    ("free", False),
+    ("pro", False),
+    ("power", True),
+    ("team", True),
+])
+def test_proactive_mining_tier_gate(tier, expected):
+    assert tier_manager.check_feature(tier, "proactive_mining") == expected

tests/test_memory_relations.py

@@ -0,0 +1,220 @@
+"""Tests for Phase 3 — relational tier (Mem0g-light).
+
+Coverage:
+  1. upsert_relation inserts a row and query_relations returns it
+  2. upsert_relation updates existing row on duplicate (subject/predicate/object)
+  3. tier gating: Free user gets empty list from query_relations + enrich_context
+  4. enrich_context includes relational_memory key for Pro user
+  5. decay_relations decays confidence and prunes rows below threshold
+"""
+
+from __future__ import annotations
+
+import uuid
+from datetime import datetime, timedelta, timezone
+from unittest.mock import patch
+
+import pytest
+import pytest_asyncio
+from cryptography.fernet import Fernet
+from sqlalchemy import select
+
+from app.core.memory_maintenance import decay_relations
+from app.core.memory_middleware import MemoryMiddleware
+from app.db import get_session
+from app.main import app
+from app.models import MemoryRelation, User
+from tests.conftest import TEST_USER_IDS
+
+PRO_USER_ID = TEST_USER_IDS["pro"]
+FREE_USER_ID = TEST_USER_IDS["free"]
+_FERNET_KEY = Fernet.generate_key().decode()
+
+
+# ── DB override ───────────────────────────────────────────────────────────────
+
+@pytest.fixture(autouse=True)
+def _override_db(db_session):
+    async def _gen():
+        yield db_session
+
+    app.dependency_overrides[get_session] = _gen
+    yield
+    app.dependency_overrides.pop(get_session, None)
+
+
+@pytest_asyncio.fixture
+async def pro_user_with_key(db_session):
+    """Set encryption_key on the pro test user so Fernet works."""
+    result = await db_session.execute(select(User).where(User.id == PRO_USER_ID))
+    user = result.scalar_one()
+    user.encryption_key = _FERNET_KEY
+    await db_session.commit()
+    return user
+
+
+@pytest_asyncio.fixture
+async def free_user_with_key(db_session):
+    """Set encryption_key on the free test user."""
+    result = await db_session.execute(select(User).where(User.id == FREE_USER_ID))
+    user = result.scalar_one()
+    user.encryption_key = _FERNET_KEY
+    await db_session.commit()
+    return user
+
+
+# ── Tests ─────────────────────────────────────────────────────────────────────
+
+@pytest.mark.asyncio
+async def test_upsert_relation_inserts_and_queries(db_session, pro_user_with_key):
+    """upsert_relation inserts a row; query_relations returns it."""
+    mm = MemoryMiddleware(db_session)
+    await mm.upsert_relation(
+        PRO_USER_ID,
+        subject="Giulia",
+        subject_type="person",
+        predicate="works_at",
+        object_="Acme Corp",
+        object_type="company",
+        confidence=0.9,
+    )
+    rows = await mm.query_relations(PRO_USER_ID, subject="Giulia")
+    assert len(rows) == 1
+    assert rows[0].subject_label == "Giulia"
+    assert rows[0].predicate == "works_at"
+    assert rows[0].object_label == "Acme Corp"
+    assert abs(rows[0].confidence - 0.9) < 0.001
+
+
+@pytest.mark.asyncio
+async def test_upsert_relation_updates_on_duplicate(db_session, pro_user_with_key):
+    """Second upsert on same triple updates confidence and last_confirmed_at."""
+    mm = MemoryMiddleware(db_session)
+    await mm.upsert_relation(
+        PRO_USER_ID,
+        subject="Marco",
+        subject_type="person",
+        predicate="stakeholder_of",
+        object_="Project Nexus",
+        object_type="project",
+        confidence=0.7,
+    )
+    await mm.upsert_relation(
+        PRO_USER_ID,
+        subject="Marco",
+        subject_type="person",
+        predicate="stakeholder_of",
+        object_="Project Nexus",
+        object_type="project",
+        confidence=0.95,
+    )
+    rows = await mm.query_relations(PRO_USER_ID, subject="Marco")
+    # Only one row despite two upserts
+    assert len(rows) == 1
+    assert abs(rows[0].confidence - 0.95) < 0.001
+    assert rows[0].last_confirmed_at is not None
+
+
+@pytest.mark.asyncio
+async def test_free_tier_relation_skipped(db_session, free_user_with_key):
+    """Free user: upsert_relation is silently skipped (no row created)."""
+    mm = MemoryMiddleware(db_session)
+    await mm.upsert_relation(
+        FREE_USER_ID,
+        subject="Alice",
+        subject_type="person",
+        predicate="reports_to",
+        object_="Bob",
+        object_type="person",
+        confidence=0.8,
+    )
+    rows = await mm.query_relations(FREE_USER_ID, subject="Alice")
+    assert rows == []
+
+
+@pytest.mark.asyncio
+async def test_enrich_context_includes_relational_memory(db_session, pro_user_with_key):
+    """enrich_context includes relational_memory key for Pro user."""
+    mm = MemoryMiddleware(db_session)
+    await mm.upsert_relation(
+        PRO_USER_ID,
+        subject="Elena",
+        subject_type="person",
+        predicate="cfo_of",
+        object_="StartupXYZ",
+        object_type="company",
+        confidence=0.85,
+    )
+
+    with patch("app.core.memory_middleware.MemoryMiddleware._load_associative", return_value=[]):
+        ctx = await mm.enrich_context(PRO_USER_ID, "who is Elena?")
+
+    assert "relational_memory" in ctx
+    assert any("Elena" in r for r in ctx["relational_memory"])
+
+
+@pytest.mark.asyncio
+async def test_enrich_context_relational_empty_for_free(db_session, free_user_with_key):
+    """Free user: relational_memory is empty list in enrich_context."""
+    mm = MemoryMiddleware(db_session)
+
+    with patch("app.core.memory_middleware.MemoryMiddleware._load_associative", return_value=[]):
+        ctx = await mm.enrich_context(FREE_USER_ID, "test message")
+
+    assert ctx.get("relational_memory") == []
+
+
+@pytest.mark.asyncio
+async def test_decay_relations_reduces_confidence(db_session, pro_user_with_key):
+    """decay_relations reduces confidence on stale rows."""
+    old_date = datetime.now(timezone.utc) - timedelta(days=35)
+    row = MemoryRelation(
+        id=str(uuid.uuid4()),
+        user_id=PRO_USER_ID,
+        subject_label="OldContact",
+        subject_type="person",
+        predicate="knows",
+        object_label="SomeProject",
+        object_type="project",
+        confidence=0.8,
+        last_confirmed_at=old_date,
+    )
+    db_session.add(row)
+    await db_session.commit()
+
+    await decay_relations(db_session, PRO_USER_ID)
+
+    result = await db_session.execute(
+        select(MemoryRelation).where(MemoryRelation.subject_label == "OldContact")
+    )
+    updated = result.scalar_one_or_none()
+    assert updated is not None
+    assert updated.confidence < 0.8
+
+
+@pytest.mark.asyncio
+async def test_decay_relations_prunes_low_confidence(db_session, pro_user_with_key):
+    """decay_relations deletes rows whose confidence drops below 0.2 threshold."""
+    # Start at 0.21 with 60-day-old last_confirmed_at → two decay periods → 0.21 * 0.95^2 ≈ 0.19 → pruned
+    old_date = datetime.now(timezone.utc) - timedelta(days=65)
+    row = MemoryRelation(
+        id=str(uuid.uuid4()),
+        user_id=PRO_USER_ID,
+        subject_label="ExpiredContact",
+        subject_type="person",
+        predicate="used_to_work_with",
+        object_label="OldCorp",
+        object_type="company",
+        confidence=0.21,
+        last_confirmed_at=old_date,
+    )
+    db_session.add(row)
+    await db_session.commit()
+
+    await decay_relations(db_session, PRO_USER_ID)
+
+    result = await db_session.execute(
+        select(MemoryRelation).where(MemoryRelation.subject_label == "ExpiredContact")
+    )
+    pruned = result.scalar_one_or_none()
+    assert pruned is None

tests/test_output_formatter.py

@@ -5,7 +5,7 @@ from __future__ import annotations
 import pytest
 
 from app.core.output_formatter import StreamFormatter
-from app.schemas import WsFloatingDomain, WsStreamEnd, WsStreamStart, WsStreamText
+from app.schemas import WsStreamEnd, WsStreamStart, WsStreamText
 
 
 async def _stream(*events: tuple[str, object]):
@@ -36,29 +36,6 @@ async def test_stream_formatter_text_stream() -> None:
     assert isinstance(frames[-1], WsStreamEnd)
 
 
-@pytest.mark.asyncio
-async def test_stream_formatter_floating_domain_first() -> None:
-    formatter = StreamFormatter(request_id="req-2")
-    frames = await _collect(
-        formatter,
-        _stream(
-            (
-                "floating_domain",
-                {"type": "node", "id": "n-1", "section": None},
-            ),
-            ("token", "Summary"),
-        ),
-    )
-
-    assert isinstance(frames[0], WsFloatingDomain)
-    assert frames[0].domain.type == "node"
-    assert frames[0].domain.id == "n-1"
-    assert isinstance(frames[1], WsStreamStart)
-    assert isinstance(frames[2], WsStreamText)
-    assert frames[2].chunk == "Summary"
-    assert isinstance(frames[-1], WsStreamEnd)
-
-
 @pytest.mark.asyncio
 async def test_stream_formatter_ignores_unknown_events() -> None:
     formatter = StreamFormatter(request_id="req-3")

tests/test_run_contextual.py

@@ -0,0 +1,85 @@
+"""Tests for run_contextual_stream.
+
+These tests monkeypatch _run_single_agent_stream (the actual internal runner)
+rather than the plan's fictional _run_agent_loop, matching the real
+deep_agent.py architecture.
+"""
+import pytest
+from unittest.mock import AsyncMock, MagicMock, patch
+from app.schemas.contextual import ContextualScope
+
+
+@pytest.mark.asyncio
+async def test_run_contextual_stream_includes_scope_block(monkeypatch):
+    """run_contextual_stream must inject the scope block into the system prompt
+    and include get_page_details in the tool list while excluding note-edit tools."""
+    import app.core.deep_agent as deep_agent
+
+    captured = {}
+
+    async def fake_stream(
+        *,
+        user_id,
+        system_prompt,
+        message,
+        context,
+        agent_name="agent",
+        tools=None,
+        conversation_history=None,
+        **kwargs,
+    ):
+        captured["sys"] = system_prompt
+        captured["tool_names"] = [getattr(t, "name", str(t)) for t in (tools or [])]
+        captured["agent_name"] = agent_name
+        # Async generator that yields nothing — still satisfies the protocol.
+        if False:
+            yield  # pragma: no cover
+
+    monkeypatch.setattr(deep_agent, "_run_single_agent_stream", fake_stream)
+
+    scope = ContextualScope(
+        page="project",
+        entity_type="project",
+        entity_id="p1",
+        entity_name="Acme",
+        counts={"tasks": 1, "notes": 0, "milestones": 0},
+    )
+
+    context = {
+        "conversation_history": [],
+        "_debug": {"session_id": "s1"},
+    }
+
+    results = []
+    async for item in deep_agent.run_contextual_stream(
+        user_id="user1",
+        message="hi",
+        context=context,
+        scope=scope,
+    ):
+        results.append(item)
+
+    assert "Acme" in captured["sys"], "scope block must appear in system prompt"
+    assert "Current view" in captured["sys"], "section header must be present"
+
+    names = captured["tool_names"]
+    assert "get_page_details" in names, "get_page_details tool must be included"
+
+    # Entity-create tools: at least one of these must be present.
+    assert any(n in names for n in ("create_task", "create_note", "update_task")), (
+        "at least one entity-create tool must be present"
+    )
+
+    assert "create_timeline" in names, "create_timeline tool must be included"
+
+    # Note edit tools must NOT be exposed.
+    assert "propose_note_edit" not in names, "propose_note_edit must be excluded"
+
+    # Legacy read tools must be excluded — they return shallow snapshots and
+    # cause the agent to under-answer (see trace 0b46841484ba7d024ed9f8d5ac8b1df0).
+    assert "list_projects" not in names, "list_projects must be excluded (legacy read)"
+    assert "get_project" not in names, "get_project must be excluded (legacy read)"
+    assert "list_tasks" not in names, "list_tasks must be excluded (legacy read)"
+    assert "get_task" not in names, "get_task must be excluded (legacy read)"
+    assert "list_notes" not in names, "list_notes must be excluded (legacy read)"
+    assert "get_note" not in names, "get_note must be excluded (legacy read)"

tests/test_schemas_v3.py

@@ -4,12 +4,8 @@ import pytest
 from pydantic import ValidationError
 
 from app.schemas import (
-    WsDomain,
     WsFrameType,
     WsHomeRequest,
-    WsFloatingDomain,
-    WsFloatingRequest,
-    WsFloatingScope,
     WsStreamEnd,
     WsStreamStart,
     WsStreamText,
@@ -22,11 +18,9 @@ from app.schemas import (
 def test_v3_frame_types_exist():
     v3_types = [
         "home_request",
-        "floating_request",
         "stream_start",
         "stream_text",
         "stream_end",
-        "floating_domain",
         "data_request",
         "data_response",
         "mutation",
@@ -45,9 +39,6 @@ def test_v2_frame_types_still_exist():
         "tool_result",
         "final",
         "ping",
-        "agent_run",
-        "agent_data",
-        "agent_complete",
         "device_hello",
     ]
     for name in v2_types:
@@ -89,51 +80,6 @@ def test_home_request_requires_message():
         WsHomeRequest.model_validate({"type": "home_request"})
 
 
-# ── WsFloatingRequest ────────────────────────────────────────────────────
-
-
-def test_floating_request_basic():
-    frame = WsFloatingRequest(
-        message="Summarise",
-        scope=WsFloatingScope(type="task", id="task-123"),
-    )
-    assert frame.type == WsFrameType.floating_request
-    assert frame.scope.type == "task"
-    assert frame.scope.id == "task-123"
-
-
-def test_floating_request_scope_without_id():
-    frame = WsFloatingRequest(
-        message="Show all",
-        scope=WsFloatingScope(type="project"),
-    )
-    assert frame.scope.id is None
-
-
-def test_floating_request_serializes():
-    frame = WsFloatingRequest(
-        message="Test",
-        scope=WsFloatingScope(type="note", id="n-1"),
-    )
-    data = frame.model_dump()
-    assert data["type"] == "floating_request"
-    assert data["scope"]["type"] == "note"
-    assert data["scope"]["id"] == "n-1"
-
-
-def test_floating_request_invalid_scope_type():
-    with pytest.raises(ValidationError):
-        WsFloatingRequest(
-            message="X",
-            scope=WsFloatingScope(type="unknown"),  # type: ignore[arg-type]
-        )
-
-
-def test_floating_request_requires_scope():
-    with pytest.raises(ValidationError):
-        WsFloatingRequest.model_validate({"type": "floating_request", "message": "X"})
-
-
 # ── WsStreamStart ─────────────────────────────────────────────────────
 
 
@@ -192,51 +138,3 @@ def test_stream_end_deserializes():
     assert frame.request_id == "r3"
 
 
-# ── WsFloatingDomain ─────────────────────────────────────────────────────
-
-
-def test_floating_domain_tasks():
-    frame = WsFloatingDomain(request_id="r1", domain=WsDomain(type="task"))
-    assert frame.type == WsFrameType.floating_domain
-    assert frame.domain.type == "task"
-
-
-def test_floating_domain_valid_domains():
-    frame = WsFloatingDomain(
-        request_id="r1",
-        domain=WsDomain(type="project", id="213213-312321-312312-421321", section="task"),
-    )
-    assert frame.domain.type == "project"
-    assert frame.domain.id == "213213-312321-312312-421321"
-    assert frame.domain.section == "task"
-
-
-def test_floating_domain_object_valid():
-    frame = WsFloatingDomain(
-        request_id="r1",
-        domain=WsDomain(type="project", id="p1", section="task"),
-    )
-    assert frame.domain.type == "project"
-
-
-def test_floating_domain_serializes():
-    d = WsFloatingDomain(
-        request_id="r1",
-        domain=WsDomain(type="timeline"),
-    ).model_dump()
-    assert d == {
-        "type": "floating_domain",
-        "request_id": "r1",
-        "domain": {"type": "timeline", "id": None, "section": None},
-    }
-
-
-def test_floating_domain_deserializes():
-    raw = {
-        "type": "floating_domain",
-        "request_id": "r1",
-        "domain": {"type": "node", "id": "n-1", "section": None},
-    }
-    frame = WsFloatingDomain.model_validate(raw)
-    assert frame.domain.type == "node"
-    assert frame.domain.id == "n-1"

tests/test_ws_index_session.py

@@ -0,0 +1,196 @@
+"""Tests for WS folder index_session handlers (Task 9).
+
+Tests the three handler functions directly with a minimal fake WebSocket so
+no real WS connection or LLM call is made.
+"""
+
+from __future__ import annotations
+
+import json
+from datetime import datetime, timezone
+from unittest.mock import AsyncMock, patch
+
+import pytest
+import pytest_asyncio
+
+from app.api.routes.device_ws import (
+    _handle_index_session_start,
+    _handle_index_file_batch,
+    _handle_index_session_cancel,
+    _index_sessions,
+)
+from app.billing.quota import add_token_usage
+from app.core.folder_indexer import IndexResult
+from app.models import MonthlyTokenUsage
+from app.schemas import WsFrameType
+from tests.conftest import TEST_USER_IDS
+
+pytestmark = pytest.mark.asyncio
+
+USER_ID = TEST_USER_IDS["free"]
+POWER_USER_ID = TEST_USER_IDS["power"]
+
+
+# ── Fake WebSocket ────────────────────────────────────────────────────
+
+class _FakeWebSocket:
+    """Minimal WebSocket stand-in that records send_text calls."""
+
+    def __init__(self) -> None:
+        self.sent: list[dict] = []
+
+    async def send_text(self, text: str) -> None:
+        self.sent.append(json.loads(text))
+
+    def sent_types(self) -> list[str]:
+        return [f["type"] for f in self.sent]
+
+
+# ── Helpers ───────────────────────────────────────────────────────────
+
+def _make_session_id() -> str:
+    import uuid
+    return str(uuid.uuid4())
+
+
+def _fake_summarize_text_factory(summary: str = "A test summary.", tokens: int = 100):
+    """Return an AsyncMock that resolves to a fixed IndexResult."""
+    async def _fake(**kwargs) -> IndexResult:
+        return IndexResult(summary=summary, tokens_used=tokens)
+    return _fake
+
+
+# ── Fixtures ──────────────────────────────────────────────────────────
+
+@pytest_asyncio.fixture(autouse=True)
+async def _clean_sessions():
+    """Ensure _index_sessions is empty before and after each test."""
+    _index_sessions.clear()
+    yield
+    _index_sessions.clear()
+
+
+# ── Tests ─────────────────────────────────────────────────────────────
+
+async def test_index_session_happy_path(db_session):
+    """start + batch of 2 text files → 2 index_file_result + 1 progress + 1 done(completed)."""
+    ws = _FakeWebSocket()
+    session_id = _make_session_id()
+
+    # Register session.
+    await _handle_index_session_start(ws, USER_ID, {
+        "sessionId": session_id,
+        "projectId": "proj-1",
+        "totalFiles": 2,
+    })
+
+    # Verify session was registered.
+    assert session_id in _index_sessions
+    assert _index_sessions[session_id]["total"] == 2
+    assert _index_sessions[session_id]["processed"] == 0
+    # No response frames expected for session_start.
+    assert ws.sent == []
+
+    # Send batch of 2 text files — patch summarize_text so no LLM call needed.
+    with patch(
+        "app.api.routes.device_ws._handle_index_file_batch.__globals__",
+        # We patch the module-level function in folder_indexer instead:
+    ) if False else patch("app.core.folder_indexer.summarize_text", side_effect=_fake_summarize_text_factory()):
+        with patch("app.api.routes.device_ws.async_session") as mock_async_session:
+            # Wire db_session into the context manager.
+            mock_cm = AsyncMock()
+            mock_cm.__aenter__ = AsyncMock(return_value=db_session)
+            mock_cm.__aexit__ = AsyncMock(return_value=False)
+            mock_async_session.return_value = mock_cm
+
+            await _handle_index_file_batch(ws, USER_ID, {
+                "sessionId": session_id,
+                "files": [
+                    {"relPath": "README.md", "kind": "text", "content": "hello", "ext": ".md"},
+                    {"relPath": "notes.txt", "kind": "text", "content": "world", "ext": ".txt"},
+                ],
+            })
+
+    types = ws.sent_types()
+    # Expect 2 file results + 1 progress + 1 done(completed).
+    assert types.count(WsFrameType.index_file_result) == 2
+    assert types.count(WsFrameType.index_session_progress) == 1
+    assert types.count(WsFrameType.index_session_done) == 1
+
+    done_frame = next(f for f in ws.sent if f["type"] == WsFrameType.index_session_done)
+    assert done_frame["status"] == "completed"
+
+    progress_frame = next(f for f in ws.sent if f["type"] == WsFrameType.index_session_progress)
+    assert progress_frame["processed"] == 2
+    assert progress_frame["total"] == 2
+
+    # Verify session cleaned up.
+    assert session_id not in _index_sessions
+
+
+async def test_index_session_cancel(db_session):
+    """start then cancel → index_session_done(cancelled)."""
+    ws = _FakeWebSocket()
+    session_id = _make_session_id()
+
+    await _handle_index_session_start(ws, USER_ID, {
+        "sessionId": session_id,
+        "totalFiles": 5,
+    })
+    assert session_id in _index_sessions
+
+    await _handle_index_session_cancel(ws, {"sessionId": session_id})
+
+    types = ws.sent_types()
+    assert WsFrameType.index_session_done in types
+    done_frame = next(f for f in ws.sent if f["type"] == WsFrameType.index_session_done)
+    assert done_frame["status"] == "cancelled"
+
+    # Session should be cleaned up.
+    assert session_id not in _index_sessions
+
+
+async def test_index_session_quota_exceeded(db_session):
+    """Pre-fill usage to cap → batch one file → index_session_done(quota_exceeded)."""
+    ws = _FakeWebSocket()
+    session_id = _make_session_id()
+
+    # Pre-fill monthly token usage to the free-tier cap (100_000).
+    ym = datetime.now(timezone.utc).strftime("%Y-%m")
+    db_session.add(MonthlyTokenUsage(
+        user_id=USER_ID,
+        year_month=ym,
+        feature="folder_index",
+        tokens_used=100_000,  # free tier cap exactly
+    ))
+    await db_session.commit()
+
+    await _handle_index_session_start(ws, USER_ID, {
+        "sessionId": session_id,
+        "totalFiles": 1,
+    })
+
+    with patch("app.core.folder_indexer.summarize_text", side_effect=_fake_summarize_text_factory(tokens=1)):
+        with patch("app.api.routes.device_ws.async_session") as mock_async_session:
+            mock_cm = AsyncMock()
+            mock_cm.__aenter__ = AsyncMock(return_value=db_session)
+            mock_cm.__aexit__ = AsyncMock(return_value=False)
+            mock_async_session.return_value = mock_cm
+
+            await _handle_index_file_batch(ws, USER_ID, {
+                "sessionId": session_id,
+                "files": [
+                    {"relPath": "file.md", "kind": "text", "content": "content", "ext": ".md"},
+                ],
+            })
+
+    types = ws.sent_types()
+    # Should have 1 file result (success) then done(quota_exceeded).
+    assert WsFrameType.index_file_result in types
+    assert WsFrameType.index_session_done in types
+
+    done_frame = next(f for f in ws.sent if f["type"] == WsFrameType.index_session_done)
+    assert done_frame["status"] == "quota_exceeded"
+
+    # Session should be cleaned up.
+    assert session_id not in _index_sessions

tests/test_ws_unified.py

@@ -1,6 +1,6 @@
 """Integration tests for the unified WebSocket handler (Step 5).
 
-Tests the device WS endpoint with home_request and floating_request frames,
+Tests the device WS endpoint with home_request frames,
 verifying that the correct v3 frame sequence is returned.
 
 LLM calls are mocked to avoid network dependency.
@@ -34,7 +34,7 @@ def _override_db(db_session):
 
 
 def _recv_until_end(ws, max_frames: int = 20) -> list[dict]:
-    """Receive frames until stream_end (or stream_end inside floating flow), or max_frames."""
+    """Receive frames until stream_end or max_frames."""
     frames = []
     for _ in range(max_frames):
         raw = ws.receive_text()
@@ -49,11 +49,6 @@ async def _mock_home_stream(user_id, message, context):
     yield "token", "Hello"
 
 
-async def _mock_floating_stream(user_id, message, context):
-    yield "floating_domain", {"type": "task", "id": None, "section": None}
-    yield "token", "Here is a summary"
-
-
 # ── tests ─────────────────────────────────────────────────────────────────────
 
 def test_home_request_produces_stream_frames(client):
@@ -63,7 +58,7 @@ def test_home_request_produces_stream_frames(client):
     with patch("app.api.routes.device_ws.run_home_stream", side_effect=_mock_home_stream):
         with client.websocket_connect(f"/api/v1/ws/device?token={token}") as ws:
             ws.send_text(json.dumps({
-                "type": "device_hello", "device_id": "dev-1", "agent_ids": []
+                "type": "device_hello", "device_id": "dev-1", "scout_ids": []
             }))
             ws.send_text(json.dumps({
                 "type": "home_request",
@@ -79,33 +74,6 @@ def test_home_request_produces_stream_frames(client):
     assert types.index(WsFrameType.stream_start) < types.index(WsFrameType.stream_end)
 
 
-def test_floating_request_produces_domain_frame(client):
-    """floating_request → floating_domain first, then stream_text*, stream_end."""
-    token = make_jwt("power", user_id=USER_ID)
-
-    with patch("app.api.routes.device_ws.run_floating_stream", side_effect=_mock_floating_stream):
-        with client.websocket_connect(f"/api/v1/ws/device?token={token}") as ws:
-            ws.send_text(json.dumps({
-                "type": "device_hello", "device_id": "dev-2", "agent_ids": []
-            }))
-            ws.send_text(json.dumps({
-                "type": "floating_request",
-                "request_id": "p1",
-                "message": "Summarize this task",
-                "scope": {"type": "task", "id": "task-123"},
-            }))
-            frames = _recv_until_end(ws)
-
-    types = [f["type"] for f in frames]
-    assert WsFrameType.floating_domain in types
-    assert WsFrameType.stream_end in types
-    assert types.index(WsFrameType.floating_domain) < types.index(WsFrameType.stream_end)
-
-    domain_frame = next(f for f in frames if f["type"] == WsFrameType.floating_domain)
-    assert domain_frame["domain"]["type"] == "task"
-    assert domain_frame["request_id"] == "p1"
-
-
 def test_home_request_request_id_propagated(client):
     """request_id in home_request is echoed in all response frames."""
     token = make_jwt("power", user_id=USER_ID)
@@ -117,7 +85,7 @@ def test_home_request_request_id_propagated(client):
     with patch("app.api.routes.device_ws.run_home_stream", side_effect=_stream):
         with client.websocket_connect(f"/api/v1/ws/device?token={token}") as ws:
             ws.send_text(json.dumps({
-                "type": "device_hello", "device_id": "dev-3", "agent_ids": []
+                "type": "device_hello", "device_id": "dev-3", "scout_ids": []
             }))
             ws.send_text(json.dumps({
                 "type": "home_request",
@@ -138,7 +106,7 @@ def test_tool_result_dispatch_silent_on_unknown_id(client):
     with patch("app.api.routes.device_ws._HEARTBEAT_INTERVAL", 0.05):
         with client.websocket_connect(f"/api/v1/ws/device?token={token}") as ws:
             ws.send_text(json.dumps({
-                "type": "device_hello", "device_id": "dev-4", "agent_ids": []
+                "type": "device_hello", "device_id": "dev-4", "scout_ids": []
             }))
             ws.send_text(json.dumps({
                 "type": "tool_result", "id": "no-such-id", "ok": True