Trace scheduled, webhook, search, MCP and graph-extraction runs

run_agent_headless records each unattended run under its endpoint; the
scheduler passes its run id and the webhook worker its task id so Logs rows
can find their trace, while the LLM's own request id stays untouched for
quota counts. /api/search and MCP search_docs record their retrieval, and a
graph build records every extraction call under one step.

docsgpt/services/search_service.py

@@ -10,10 +10,12 @@ from __future__ import annotations
 import logging
 from typing import Any, Dict, List, Optional
 
+from docsgpt import tracing
 from docsgpt.core.settings import settings
 from docsgpt.retriever.fanout import fetch_per_source
 from docsgpt.storage.db.repositories.agents import AgentsRepository
 from docsgpt.storage.db.session import db_readonly
+from docsgpt.tracing.retrieval import describe_documents, start_retrieval_span
 from docsgpt.vectorstore.vector_creator import VectorCreator
 
 logger = logging.getLogger(__name__)
@@ -219,14 +221,21 @@ def _search_sources(
     return results[:chunks]
 
 
-def search(api_key: str, query: str, chunks: int = 5) -> List[Dict[str, Any]]:
+def search(
+    api_key: str, query: str, chunks: int = 5, *, source: str = "search"
+) -> List[Dict[str, Any]]:
     """Resolve an agent by API key and search its sources.
 
+    Every search that reaches the sources is recorded as an execution trace
+    owned by the agent's owner, under ``source``.
+
     Args:
         api_key: Agent API key (the opaque string stored on
             ``agents.key`` in Postgres).
         query: Free-text search query.
         chunks: Max number of hits to return.
+        source: Trace source name: ``search`` for ``/api/search``, ``mcp``
+            for the MCP ``search_docs`` tool.
 
     Returns:
         List of hit dicts with ``text``, ``title``, ``source`` keys.
@@ -256,4 +265,20 @@ def search(api_key: str, query: str, chunks: int = 5) -> List[Dict[str, Any]]:
     if not source_ids:
         return []
 
-    return _search_sources(query, source_ids, chunks)
+    trace = tracing.start_trace(
+        source=source,
+        user_id=agent.get("user_id"),
+        agent_id=str(agent.get("id")) if agent.get("id") else None,
+    )
+    with tracing.activate(trace):
+        try:
+            with start_retrieval_span(
+                f"retrieval {source}",
+                sources=source_ids,
+                **{"docsgpt.top_k": chunks},
+            ) as span:
+                results = _search_sources(query, source_ids, chunks)
+                describe_documents(span, results, query=query)
+            return results
+        finally:
+            tracing.flush(trace)