Use the existing in-flight context governor to replace tool output that cannot fit the next model request with a bounded, actionable instruction. The model can retry with narrower arguments, use another tool, or explain the context limit without a second recovery state machine.
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>
Extract model-facing context governance from AgentRunner.
Only compact in-flight tool results when the model request is over budget, keep compacted IDs stable within a turn, and allow the newest result to be compacted as a last resort when it is the remaining source of overflow.
maintainer edit: move duplicate tool_use history repair out of AgentRunner and into Anthropic message conversion, reusing the OpenAI-compatible queue-mapping approach locally without broadening the shared runner path.
maintainer edit: remap duplicate tool_use/tool_call ids instead of dropping later calls, so Anthropic-compatible providers that reuse ids for distinct parallel tool calls keep all requested work while still sending unique ids.
Anthropic rejects any request where two tool_use blocks share an id
("messages.N.content.M: tool_use ids must be unique"). A mis-assembled
stream could surface the same tool_use block twice in one assistant turn;
the runner persisted it verbatim, so the malformed message was re-sent on
every subsequent turn and permanently bricked the session — the agent
silently stopped replying.
Fix at two layers:
- AnthropicProvider._parse_response: drop duplicate tool_use ids (keep
first) as the response enters nanobot, so corruption is never persisted.
- AgentRunner._dedup_tool_calls: a new context-governance pass that dedupes
assistant tool_calls and tool results by id before each send, healing any
history that was already corrupted.
Add regression tests covering both the dedup and the no-op fast path.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
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