feat(langfuse): propagate user_id and session_id to all traces

- Add hash_user_id() to SHA-256 hash user IDs before sending to Langfuse
- Add langfuse_context() helper wrapping propagate_attributes()
- deep_agent: extract session_id from _debug context, wrap all agent
  runs and classifier with langfuse_context(user_id, session_id)
- agent_runner: add session_id param, pass run_id as session for batch
- agent_setup: wrap journey LLM calls with langfuse_context
- Remove redundant metadata dicts (now handled by propagate_attributes)

app/api/routes/agent_setup.py

@@ -32,7 +32,7 @@ from typing import Any
 from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage
 
 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
+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
 
@@ -260,11 +260,13 @@ async def _call_llm_with_tools(
     llm_with_tools = llm.bind_tools(tools)
     tool_map = {tool_def.name: tool_def for tool_def in tools}
 
+    _lf_ctx = langfuse_context(user_id=user_id or None, session_id=session_id or None)
+    _lf_ctx.__enter__()
+
     _span_ctx = (
         lf.start_as_current_observation(
             as_type="span",
             name="journey-setup",
-            metadata={"user_id": user_id or None, "session_id": session_id or None},
             input=history[-1]["content"] if history else "",
         )
         if lf else None
@@ -286,7 +288,7 @@ async def _call_llm_with_tools(
             _gen = _gen_ctx.__enter__() if _gen_ctx else None
             response: AIMessage = await llm_with_tools.ainvoke(messages)
             if _gen_ctx:
-                _gen.update(output=_as_text(response.content), usage=extract_usage(response))
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
                 _gen_ctx.__exit__(None, None, None)
 
             resp_text = _as_text(response.content)
@@ -342,6 +344,7 @@ async def _call_llm_with_tools(
     finally:
         if _span_ctx:
             _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
         if lf:
             lf.flush()
 

app/api/routes/agents.py

@@ -12,9 +12,12 @@ in backend agent-config tables.
 from __future__ import annotations
 
 import asyncio
+import logging
 import uuid
 from datetime import datetime, timedelta, timezone
 
+logger = logging.getLogger(__name__)
+
 from fastapi import APIRouter, Depends, HTTPException, status
 from sqlalchemy import func, select
 from sqlalchemy.ext.asyncio import AsyncSession
@@ -182,7 +185,6 @@ async def trigger_agent_run(
         if body.last_run_at
         else None
     )
-
     config = LocalAgentConfig(
         id=str(uuid.uuid4()),
         user_id=current_user.id,

app/core/agent_runner.py

@@ -44,7 +44,7 @@ from app.agents.project_agent import PROJECT_TOOLS
 from app.agents.task_agent import TASK_TOOLS
 from app.agents.timeline_agent import TIMELINE_TOOLS
 from app.core.device_manager import DeviceConnectionManager
-from app.core.langfuse_client import compile_prompt, extract_usage, get_langfuse, get_prompt_or_fallback
+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.core.preprocessors import detect_content_type, preprocess
 from app.core.ws_context import clear_client_executor, execute_on_client, set_client_executor
@@ -227,6 +227,7 @@ async def _run_agent_with_tools(
     tools: list[Any],
     max_steps: int,
     user_id: str = "",
+    session_id: str = "",
     langfuse_prompt: Any = None,
     agent_name: str = "batch-agent",
     _tool_calls_out: list[str] | None = None,
@@ -246,6 +247,9 @@ async def _run_agent_with_tools(
 
     tool_map = {tool_def.name: tool_def for tool_def in tools}
 
+    _lf_ctx = langfuse_context(user_id=user_id or None, session_id=session_id or None)
+    _lf_ctx.__enter__()
+
     _span_ctx = (
         lf.start_as_current_observation(
             as_type="span",
@@ -272,7 +276,7 @@ async def _run_agent_with_tools(
             _gen = _gen_ctx.__enter__() if _gen_ctx else None
             response: AIMessage = await llm_with_tools.ainvoke(messages)
             if _gen_ctx:
-                _gen.update(output=_as_text(response.content), usage=extract_usage(response))
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
                 _gen_ctx.__exit__(None, None, None)
 
             messages.append(response)
@@ -317,6 +321,7 @@ async def _run_agent_with_tools(
     finally:
         if _span_ctx:
             _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
         if lf:
             lf.flush()
 
@@ -385,7 +390,8 @@ async def _scan_directories(
     for file_path in all_files:
         try:
             meta = await execute_on_client(action="get_file_metadata", data={"path": file_path})
-            modified_at = meta.get("modifiedAt")
+            # FE sends snake_case keys on the wire (toSnakeCase transform)
+            modified_at = meta.get("modified_at") or meta.get("modifiedAt")
             if modified_at is None:
                 filtered.append(file_path)
                 continue
@@ -606,7 +612,6 @@ async def run_local_agent(
 
     try:
         # ── Code: scan directories ───────────────────────────────────
-        logger.info("agent_runner: run=%s scanning directories user=%s", run_id, user_id)
         file_paths = await _scan_directories(
             paths=config.directory_paths,
             extensions=config.file_extensions or [],
@@ -685,6 +690,7 @@ async def run_local_agent(
                     tools=processing_tools,
                     max_steps=_MAX_PROCESSING_STEPS,
                     user_id=user_id,
+                    session_id=run_id,
                     langfuse_prompt=prompt_obj,
                     agent_name="unified-processor",
                     _tool_calls_out=file_tool_calls,
@@ -916,6 +922,7 @@ async def run_cloud_agent(
                     tools=processing_tools,
                     max_steps=_MAX_PROCESSING_STEPS,
                     user_id=user_id,
+                    session_id=run_id,
                     langfuse_prompt=cloud_prompt_obj,
                     agent_name="cloud-processor",
                 )

app/core/deep_agent.py

@@ -16,7 +16,7 @@ from app.agents.note_agent import NOTE_TOOLS
 from app.agents.project_agent import PROJECT_TOOLS
 from app.agents.task_agent import TASK_TOOLS
 from app.agents.timeline_agent import TIMELINE_TOOLS
-from app.core.langfuse_client import extract_usage, get_langfuse, get_prompt_or_fallback
+from app.core.langfuse_client import extract_usage, get_langfuse, get_prompt_or_fallback, langfuse_context
 from app.core.llm import get_agent_llm, model_for_agent
 from app.core.memory_middleware import MemoryMiddleware
 from app.core.ws_context import clear_tool_result_collector, execute_on_client, set_tool_result_collector
@@ -148,6 +148,15 @@ def _trace_id_from_context(context: dict[str, Any]) -> str | None:
     return None
 
 
+def _session_id_from_context(context: dict[str, Any]) -> str | None:
+    debug = context.get("_debug")
+    if isinstance(debug, dict):
+        session_id = debug.get("session_id")
+        if isinstance(session_id, str) and session_id:
+            return session_id
+    return None
+
+
 def _context_for_model(context: dict[str, Any]) -> dict[str, Any]:
     sanitized = dict(context)
     sanitized.pop("_debug", None)
@@ -550,18 +559,25 @@ async def _infer_floating_domain(message: str, context: dict[str, Any]) -> dict[
         _, classifier_prompt_obj = get_prompt_or_fallback(
             "floating_domain_classifier", _FLOATING_DOMAIN_CLASSIFIER_PROMPT
         )
-        if lf:
+
-            with lf.start_as_current_observation(
+        # Extract user/session from context for Langfuse attribution
-                as_type="generation",
+        _debug = context.get("_debug") if isinstance(context, dict) else None
-                name="floating-classifier",
+        _lf_user = (_debug or {}).get("user_id") if isinstance(_debug, dict) else None
-                model=model_for_agent("classifier"),
+        _lf_session = (_debug or {}).get("session_id") if isinstance(_debug, dict) else None
-                prompt=classifier_prompt_obj,
+
-                input=classifier_messages,
+        with langfuse_context(user_id=_lf_user, session_id=_lf_session):
-            ) as gen:
+            if lf:
+                with lf.start_as_current_observation(
+                    as_type="generation",
+                    name="floating-classifier",
+                    model=model_for_agent("classifier"),
+                    prompt=classifier_prompt_obj,
+                    input=classifier_messages,
+                ) as gen:
+                    response = await llm.ainvoke(classifier_messages)
+                    gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
+            else:
                 response = await llm.ainvoke(classifier_messages)
-                gen.update(output=_as_text(response.content), usage=extract_usage(response))
-        else:
-            response = await llm.ainvoke(classifier_messages)
         parsed = _parse_json_object(_as_text(response.content))
         if parsed is not None:
             domain = _normalize_domain_payload(parsed, project_id)
@@ -590,6 +606,7 @@ async def _run_single_agent(
     agent_name: str = "agent",
 ) -> str:
     trace_id = _trace_id_from_context(context)
+    session_id = _session_id_from_context(context)
     lf = get_langfuse()
     llm = get_agent_llm(agent_name)
     tools = _all_tools_for_user(user_id, trace_id)
@@ -610,6 +627,9 @@ async def _run_single_agent(
     collected: list[dict[str, Any]] = []
     set_tool_result_collector(collected)
 
+    _lf_ctx = langfuse_context(user_id=user_id, session_id=session_id)
+    _lf_ctx.__enter__()
+
     _span_ctx = (
         lf.start_as_current_observation(
             as_type="span",
@@ -636,7 +656,7 @@ async def _run_single_agent(
             _gen = _gen_ctx.__enter__() if _gen_ctx else None
             response: AIMessage = await llm_with_tools.ainvoke(messages)
             if _gen_ctx:
-                _gen.update(output=_as_text(response.content), usage=extract_usage(response))
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
                 _gen_ctx.__exit__(None, None, None)
 
             messages.append(response)
@@ -698,6 +718,7 @@ async def _run_single_agent(
         clear_tool_result_collector()
         if _span_ctx:
             _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
         if lf:
             lf.flush()
 
@@ -713,6 +734,7 @@ async def _run_single_agent_stream(
     agent_name: str = "agent",
 ) -> AsyncGenerator[tuple[str, Any], None]:
     trace_id = _trace_id_from_context(context)
+    session_id = _session_id_from_context(context)
     lf = get_langfuse()
     llm = get_agent_llm(agent_name)
     tools = _all_tools_for_user(user_id, trace_id)
@@ -734,6 +756,9 @@ async def _run_single_agent_stream(
     collected: list[dict[str, Any]] = []
     set_tool_result_collector(collected)
 
+    _lf_ctx = langfuse_context(user_id=user_id, session_id=session_id)
+    _lf_ctx.__enter__()
+
     _span_ctx = (
         lf.start_as_current_observation(
             as_type="span",
@@ -761,7 +786,7 @@ async def _run_single_agent_stream(
             _gen = _gen_ctx.__enter__() if _gen_ctx else None
             response: AIMessage = await llm_with_tools.ainvoke(messages)
             if _gen_ctx:
-                _gen.update(output=_as_text(response.content), usage=extract_usage(response))
+                _gen.update(output=_as_text(response.content), usage_details=extract_usage(response))
                 _gen_ctx.__exit__(None, None, None)
 
             messages.append(response)
@@ -841,6 +866,7 @@ async def _run_single_agent_stream(
         clear_tool_result_collector()
         if _span_ctx:
             _span_ctx.__exit__(None, None, None)
+        _lf_ctx.__exit__(None, None, None)
         if lf:
             lf.flush()
 

app/core/langfuse_client.py

@@ -39,8 +39,10 @@ Linking a prompt to a generation::
 
 from __future__ import annotations
 
+import hashlib
 import logging
-from typing import Any
+from contextlib import contextmanager
+from typing import Any, Generator
 
 logger = logging.getLogger(__name__)
 
@@ -145,3 +147,44 @@ def extract_usage(response: Any) -> dict[str, int]:
         "output": int(meta.get("output_tokens", 0)),
         "total": int(meta.get("total_tokens", 0)),
     }
+
+
+def hash_user_id(user_id: str) -> str:
+    """Return a SHA-256 hash of *user_id* for use as Langfuse ``user_id``.
+
+    This avoids sending raw database UUIDs to external observability services
+    while still providing a stable, deterministic identifier for per-user
+    metrics in the Langfuse dashboard.
+    """
+    return hashlib.sha256(user_id.encode()).hexdigest()
+
+
+@contextmanager
+def langfuse_context(
+    user_id: str | None = None,
+    session_id: str | None = None,
+) -> Generator[None, None, None]:
+    """Propagate ``user_id`` (hashed) and ``session_id`` to all Langfuse observations.
+
+    No-op when Langfuse is not configured or parameters are empty.
+    """
+    lf = get_langfuse()
+    if lf is None or (not user_id and not session_id):
+        yield
+        return
+
+    try:
+        from langfuse import propagate_attributes
+    except ImportError:
+        logger.debug("langfuse: propagate_attributes not available — skipping context")
+        yield
+        return
+
+    attrs: dict[str, str] = {}
+    if user_id:
+        attrs["user_id"] = hash_user_id(user_id)
+    if session_id:
+        attrs["session_id"] = session_id
+
+    with propagate_attributes(**attrs):
+        yield