send_delta popped the buffer before self.send ran, so a transient WeChat
send failure dropped the completed streamed reply: ChannelManager
_send_with_retry re-invokes the same _stream_end message, but the buffer
was already gone, so the retry sent empty content and returned — turning a
delivery retry into silent message loss.
Build `full` from the buffer without popping, send, then clear only after a
successful send. The _stream_end message's own content (set when the manager
coalesces deltas into the end message) is folded into `full` via addition
rather than appended to the buffer, so a retry recomputes the same `full`
from an unchanged buffer instead of double-counting it.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
WeixinConfig lacked a streaming field, so channels.weixin.streaming was
silently dropped by pydantic and supports_streaming stayed False, forcing the
non-streaming Messages API. Some upstream Anthropic relays drop tool_use
id/name/input on the non-stream path (but handle SSE fine), breaking WeChat
tool calls.
Two parts:
1. Add a streaming field (default True) so WeChat routes LLM calls through the
streaming API. WeChat iLink has no native incremental delivery, so this is
user-invisible — it only changes how the LLM is called.
2. WeChat send_delta previously dropped content, and the manager bypasses send
for the _streamed final answer, so a streamed reply never reached the user.
send_delta now buffers content deltas and flushes the full reply in one shot
at _stream_end (also stopping the typing indicator via send).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Combine malformed tool-call handling with placeholder filtering and a
no-tools fallback so a relay that returns tool_use blocks with null
id/name/input can no longer crash a turn or permanently wedge a session.
Adapted to the ContextGovernor architecture (context governance now lives
in nanobot/agent/context_governance.py, not runner.py):
- ToolCallRequest.has_valid_name(): single source of truth for "usable
name" (non-empty string).
- tool_hints.format_tool_hints(): skip tool calls with a non-string/empty
name instead of raising AttributeError on the whole turn.
- ContextGovernor.strip_placeholder_assistant_messages() and
strip_malformed_tool_calls() (plus the _tool_call_name_is_valid helper):
history-cleaning staticmethods invoked at the START of
prepare_for_model() — strip_placeholder, then strip_malformed, then the
existing drop_orphan/backfill chain. Both only repair the model-facing
copy and leave persisted history untouched (return a copy, or the same
list when nothing changes). Also wired into runner's minimal-repair path.
- AgentRunner._drop_malformed_tool_calls(): returns
(dropped, all_dropped, original_finish_reason); clears finish_reason to
"stop" when all calls are dropped.
- AgentRunner._malformed_tool_call_retry_messages() + _request_model
malformed_retry flag: when an all-dropped tool_calls response comes back,
retry once with a corrective note; if the retry STILL comes back
all-dropped, fall back to _request_no_tools for graceful text degradation.
Tests for the history-cleaning methods live with ContextGovernor in
tests/agent/test_runner_governance.py; response-layer and tool-hint tests
stay on AgentRunner / tool_hints.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
