Phase 3 — WS frame + REST fallbacka
This commit is contained in:
@@ -287,7 +287,6 @@ async def _run_agent_with_tools(
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return final_text
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for call in response.tool_calls:
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call_id = str(call.get("id", ""))
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call_name = str(call.get("name", ""))
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call_args = call.get("args", {})
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logger.info(
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222
app/core/brief_agent.py
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222
app/core/brief_agent.py
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@@ -0,0 +1,222 @@
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"""Brief agent — produces plain-text home and project status briefs.
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Read-only tool subset only. Never calls _normalize_tagged_list_lines —
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the brief prompt forbids XML tags, so skipping post-processing is intentional.
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"""
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from __future__ import annotations
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from collections.abc import AsyncGenerator
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from datetime import date
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from typing import Any
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from app.agents.note_agent import NOTE_READ_TOOLS
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from app.agents.project_agent import PROJECT_READ_TOOLS
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from app.agents.task_agent import TASK_READ_TOOLS
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from app.agents.timeline_agent import TIMELINE_READ_TOOLS
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from app.core.deep_agent import (
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_language_instruction,
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_proactive_hints_injection,
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_read_only_memory_tools,
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_relational_memory_injection,
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_run_single_agent_stream,
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_trace_id_from_context,
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)
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from app.core.langfuse_client import compile_prompt, get_prompt_or_fallback
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_LANGUAGE_NAMES: dict[str, str] = {
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"en": "English", "it": "Italian", "es": "Spanish",
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"fr": "French", "de": "German",
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"english": "English", "italian": "Italian", "italiano": "Italian",
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"spanish": "Spanish", "español": "Spanish",
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"french": "French", "français": "French",
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"german": "German", "deutsch": "German",
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}
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_HOME_BRIEF_FALLBACK = """\
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You are the user's personal assistant producing a short daily brief.
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ROLE
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Act like a calm, attentive secretary writing a stand-up note for your boss.
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Warm and human, never breezy. Never cheerful filler, never emojis, never
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"here is your brief" meta-text. The user is opening the app mid-workday and
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is probably stressed — your job is to lower cognitive load, not add noise.
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TOOLS — always call before writing
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Pull fresh data every run. Do not invent counts or titles. Use at minimum:
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- list_tasks_due_today — tasks the user owes today
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- list_timelines_today — events starting or ending today
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- list_all_projects — projects currently in progress or at risk
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- memory_list_blocks / memory_get — personal context about people, clients,
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payment habits, working preferences
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If a tool returns nothing, simply omit that topic. Never report zeros.
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WHAT TO INCLUDE
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1. Tasks due today (title + priority; group the 1-2 most important).
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2. Timeline events starting or ending today (and anything that starts/ends
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tomorrow if the user has a very light day).
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3. Active projects that need a nudge — stalled, blocked, or awaiting input.
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4. Memory-aware colour where it sharpens the brief. Examples:
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- "Client Rossi tends to pay late — the Acme invoice is 6 days out."
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- "You usually dislike meetings before 10:00 — the call at 09:30 is unusual."
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Only add a memory line when it changes what the user does. Do not pad.
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WHAT TO OMIT
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- Zero-counts ("no overdue items", "0 meetings today").
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- Statistics ("2 active projects, 3 completed tasks").
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- Headers, titles, greetings, sign-offs, dates, emojis, slang.
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- Meta-phrases ("here is", "let me know if", "hope this helps").
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- XML/HTML tags of any kind. Plain prose only.
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LIGHT-DAY CLAUSE
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If tasks + events + active-project-nudges together produce fewer than two
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sentences of content, also list 1-2 projects in status on_hold or waiting
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and ask a single, specific question about them — e.g. "Is the Bianchi
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redesign still paused, or ready to pick back up?" One question max, grounded
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in a real project name.
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VOICE
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- Calm. Concise. Human. Short sentences.
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- Use **bold** sparingly for task titles, project names, and people's names.
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- No bullet lists. Flow as 2-4 sentences of prose.
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LENGTH
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2-4 sentences total. Hard cap 4. If the day is truly empty, one sentence.
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Respond in the user's language ({language}). Today is {today}.\
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"""
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_PROJECT_BRIEF_FALLBACK = """\
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You are the project assistant producing a short status brief for ONE project.
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ROLE
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A senior project manager summarising state-of-play for the owner. Factual,
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sharp, forward-looking. Never reassuring filler, never emojis.
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SCOPE
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Work only with project_id = {project_id}. Do not mention or pull data from
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other projects. Use tools to fetch fresh data:
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- get_project — current status, dates, description
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- list_tasks(project_id) — open work, split by status
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- list_timelines(project_id) — milestones hit, upcoming, overdue
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- list_notes(project_id) — any recent decisions or blockers
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- memory_get — relevant context about the client, collaborators, constraints
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STRUCTURE — follow exactly, one short paragraph per section, no headers
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1. **State.** One sentence: current phase, health (on track / at risk / blocked),
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and why. Cite the concrete signal (overdue milestone, stalled tasks, recent
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blocker note).
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2. **What's moving.** What was completed or progressed recently. Name specific
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tasks or milestones.
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3. **Next steps.** The 1-3 most important things the user should do next, in
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priority order. Be concrete — task name, who owns it, when due if known.
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If waiting on someone else, name them and what the ask is.
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4. **Risks / memory-flagged items.** One line max. Only include when there is
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a real risk or a relevant memory (e.g. late-paying client, tight deadline,
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scope change). Omit the section entirely if nothing to say.
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WHAT TO OMIT
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- Zero-counts ("no overdue tasks").
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- Generic advice ("keep up the good work").
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- Greetings, headers, bullet lists, emojis, sign-offs, meta-phrases.
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- XML/HTML tags or bracketed id lists. Plain prose only.
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VOICE
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- Direct. Factual. No fluff.
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- Use **bold** sparingly for task titles, milestone names, and the owner's name.
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- Short sentences. Prefer verbs over nouns ("Client review is blocking release"
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not "There is a blocker which is the client review").
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LENGTH
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4-8 sentences total across the 3-4 sections. Hard cap 8.
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Respond in the user's language ({language}). Today is {today}.\
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"""
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def _resolve_language(context: dict[str, Any]) -> str:
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core = context.get("core_memory") or {}
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raw = (core.get("language") or "en").strip().lower()
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return _LANGUAGE_NAMES.get(raw, raw.title()) or "English"
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def _build_read_tools(user_id: str, trace_id: str | None) -> list[Any]:
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return [
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*TASK_READ_TOOLS,
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*PROJECT_READ_TOOLS,
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*TIMELINE_READ_TOOLS,
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*NOTE_READ_TOOLS,
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*_read_only_memory_tools(user_id, trace_id),
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]
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async def run_home_brief(
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user_id: str,
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context: dict[str, Any],
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) -> AsyncGenerator[tuple[str, Any], None]:
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"""Stream a plain-text daily home brief.
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Yields (event_type, data) tuples identical to _run_single_agent_stream.
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Do NOT post-process output through _normalize_tagged_list_lines.
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"""
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trace_id = _trace_id_from_context(context)
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today = date.today().isoformat()
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language = _resolve_language(context)
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raw_template, langfuse_prompt = get_prompt_or_fallback("home_brief", _HOME_BRIEF_FALLBACK)
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system_prompt = compile_prompt(raw_template, langfuse_prompt, language=language, today=today)
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system_prompt += _relational_memory_injection(context)
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system_prompt += _proactive_hints_injection(context)
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system_prompt += _language_instruction(context)
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if today not in system_prompt:
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system_prompt += f"\nToday is {today}."
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tools = _build_read_tools(user_id, trace_id)
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async for event in _run_single_agent_stream(
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user_id=user_id,
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system_prompt=system_prompt,
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message="Generate the daily brief.",
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context=context,
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langfuse_prompt=langfuse_prompt,
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agent_name="brief-agent",
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tools=tools,
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):
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yield event
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async def run_project_brief(
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user_id: str,
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project_id: str,
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context: dict[str, Any],
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) -> AsyncGenerator[tuple[str, Any], None]:
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"""Stream a plain-text project status brief for project_id.
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Yields (event_type, data) tuples identical to _run_single_agent_stream.
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Do NOT post-process output through _normalize_tagged_list_lines.
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"""
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trace_id = _trace_id_from_context(context)
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today = date.today().isoformat()
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language = _resolve_language(context)
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raw_template, langfuse_prompt = get_prompt_or_fallback("project_brief", _PROJECT_BRIEF_FALLBACK)
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system_prompt = compile_prompt(
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raw_template, langfuse_prompt,
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language=language, today=today, project_id=project_id,
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)
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system_prompt += _relational_memory_injection(context)
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system_prompt += _proactive_hints_injection(context)
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system_prompt += _language_instruction(context)
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if today not in system_prompt:
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system_prompt += f"\nToday is {today}."
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tools = _build_read_tools(user_id, trace_id)
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async for event in _run_single_agent_stream(
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user_id=user_id,
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system_prompt=system_prompt,
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message=f"Generate the project status brief for project {project_id}.",
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context=context,
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langfuse_prompt=langfuse_prompt,
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agent_name="brief-agent",
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tools=tools,
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):
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yield event
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@@ -489,6 +489,13 @@ def _memory_tools(user_id: str, trace_id: str | None) -> list[Any]:
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]
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def _read_only_memory_tools(user_id: str, trace_id: str | None) -> list[Any]:
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"""Return memory tools that only read — safe for the read-only brief-agent subset."""
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all_mem = _memory_tools(user_id, trace_id)
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_read_names = {"memory_list_blocks", "memory_get", "archival_memory_search", "conversation_search"}
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return [t for t in all_mem if t.name in _read_names]
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def _all_tools_for_user(user_id: str, trace_id: str | None) -> list[Any]:
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return [*_all_tools(), *_memory_tools(user_id, trace_id)]
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@@ -792,12 +799,14 @@ async def _run_single_agent_stream(
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max_steps: int = 6,
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langfuse_prompt: Any = None,
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agent_name: str = "agent",
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tools: list[Any] | None = None,
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) -> AsyncGenerator[tuple[str, Any], None]:
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trace_id = _trace_id_from_context(context)
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session_id = _session_id_from_context(context)
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lf = get_langfuse()
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llm = get_agent_llm(agent_name)
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tools = _all_tools_for_user(user_id, trace_id)
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if tools is None:
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tools = _all_tools_for_user(user_id, trace_id)
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model_context = _context_for_model(context)
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logger.info("deep_agent: run_single_agent_stream_start trace=%s user=%s", trace_id or "-", user_id)
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llm_with_tools = llm.bind_tools(tools)
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@@ -102,6 +102,7 @@ _AGENT_MODEL_SETTINGS: dict[str, Callable[[], str]] = {
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"floating-agent": lambda: settings.LLM_MODEL_FLOATING_AGENT or settings.LLM_MODEL,
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"unified-processor": lambda: settings.LLM_MODEL_UNIFIED_PROCESSOR or settings.LLM_MODEL,
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"cloud-processor": lambda: settings.LLM_MODEL_CLOUD_PROCESSOR or settings.LLM_MODEL,
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"brief-agent": lambda: settings.LLM_MODEL_BRIEF_AGENT or settings.LLM_MODEL,
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"setup": lambda: settings.LLM_MODEL_SETUP_AGENT or settings.LLM_MODEL,
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"memory-extractor": lambda: settings.LLM_MODEL_MEMORY_EXTRACTOR or "gpt-4o-mini",
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"memory-miner": lambda: settings.LLM_MODEL_MEMORY_MINER or "gpt-4o-mini",
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