Bind the log context for tool continuations

gen_continuation is not wrapped by @log_activity, so the llm_* log lines
from a resumed turn carried no user, agent, conversation or endpoint. Every
OpenAI-compatible /v1 client-tool round landed anonymous in the logs.

Add agent_log_context, which binds the same keys log_activity binds while
keeping any enclosing activity_id, and wrap gen_continuation in it. Both
now derive the keys from one helper.

docsgpt/agents/base.py

@@ -30,7 +30,13 @@ from docsgpt.guardrails.stream import StreamingOutputGuard
 from docsgpt.guardrails.types import Action, Stage, resolve_tool_result
 from docsgpt.llm.handlers.handler_creator import LLMHandlerCreator
 from docsgpt.llm.llm_creator import LLMCreator
-from docsgpt.logging import build_stack_data, log_activity, LogContext, start_agent_span
+from docsgpt.logging import (
+    agent_log_context,
+    build_stack_data,
+    log_activity,
+    LogContext,
+    start_agent_span,
+)
 
 logger = logging.getLogger(__name__)
 
@@ -494,7 +500,8 @@ class BaseAgent(ABC):
         hands back to the LLM to continue the conversation.
 
         Unlike :meth:`gen` this is not wrapped by ``@log_activity``, so the
-        continuation's ``invoke_agent`` trace span is opened here.
+        continuation's ``invoke_agent`` trace span is opened here, and the
+        user/agent/endpoint log context is bound here too.
 
         Args:
             messages: The saved messages array from the pause point.
@@ -502,7 +509,7 @@ class BaseAgent(ABC):
             pending_tool_calls: The pending tool call descriptors from the pause.
             tool_actions: Client-provided actions resolving the pending calls.
         """
-        with start_agent_span(self, continuation=True):
+        with agent_log_context(self), start_agent_span(self, continuation=True):
             yield from self._gen_continuation_inner(
                 messages, tools_dict, pending_tool_calls, tool_actions, reasoning_content
             )

docsgpt/logging.py

@@ -5,7 +5,8 @@ import time
 
 import logging
 import uuid
-from typing import Any, Callable, Dict, Generator, List, Optional
+from contextlib import contextmanager
+from typing import Any, Callable, Dict, Generator, Iterator, List, Optional
 
 from docsgpt import tracing
 from docsgpt.core import log_context
@@ -87,22 +88,52 @@ def build_stack_data(
     return data
 
 
+def _agent_log_keys(agent: Any, data: Optional[Dict] = None) -> Dict[str, Any]:
+    """Return the log-context keys identifying an agent run."""
+    if data is None:
+        data = build_stack_data(agent)
+    return {
+        "user_id": data.get("user", "local"),
+        "agent_id": getattr(agent, "agent_id", None),
+        "conversation_id": getattr(agent, "conversation_id", None),
+        "endpoint": data.get("endpoint", ""),
+        "model": getattr(agent, "gpt_model", None) or getattr(agent, "model", None),
+    }
+
+
+@contextmanager
+def agent_log_context(agent: Any) -> Iterator[None]:
+    """Bind an agent's identity to the log context without opening an activity.
+
+    Any enclosing ``activity_id`` is kept.
+
+    Args:
+        agent: The agent whose identity the log lines should carry.
+
+    Yields:
+        None, with the context bound until the block exits.
+    """
+    token = log_context.bind(**_agent_log_keys(agent))
+    try:
+        yield
+    finally:
+        log_context.reset(token)
+
+
 def log_activity() -> Callable:
     def decorator(func: Callable) -> Callable:
         @functools.wraps(func)
         def wrapper(*args: Any, **kwargs: Any) -> Any:
             activity_id = str(uuid.uuid4())
             data = build_stack_data(args[0])
-            endpoint = data.get("endpoint", "")
-            user = data.get("user", "local")
+            keys = _agent_log_keys(args[0], data)
+            endpoint = keys["endpoint"]
+            user = keys["user_id"]
             api_key = data.get("user_api_key", "")
             query = kwargs.get("query", getattr(args[0], "query", ""))
-            agent_id = getattr(args[0], "agent_id", None) or kwargs.get("agent_id")
-            conversation_id = (
-                kwargs.get("conversation_id")
-                or getattr(args[0], "conversation_id", None)
-            )
-            model = getattr(args[0], "gpt_model", None) or getattr(args[0], "model", None)
+            agent_id = keys["agent_id"] or kwargs.get("agent_id")
+            conversation_id = kwargs.get("conversation_id") or keys["conversation_id"]
+            model = keys["model"]
 
             # Capture the surrounding activity_id before overlaying ours,
             # so nested activities record the parent → child link.