445865e429
Some checks failed
- Integrate GPU scoring inline into reasoning/multi_path.py (auto-uses GPU when available) - Integrate GPU deduplication into multi_agent/consensus_engine.py - Add semantic_search() method to memory/semantic_graph.py with GPU acceleration - Integrate GPU training into self_improvement/training.py AutoTrainer - Fix all 758 ruff lint issues (whitespace, import sorting, unused imports, ambiguous vars, undefined names) - Fix all 40 mypy type errors across the codebase (no-any-return, union-attr, arg-type, etc.) - Fix deprecated ruff config keys (select/ignore -> [tool.ruff.lint]) - Add .dockerignore to exclude .venv/, tests/, docs/ from Docker builds - Add type hints and docstrings to verification/outcome.py - Fix E402 import ordering in witness_agent.py - Fix F821 undefined names in vector_pgvector.py and native.py - Fix E741 ambiguous variable names in reflective.py and recommender.py All 276 tests pass. 0 ruff errors. 0 mypy errors. Co-Authored-By: Nakamoto, S <defi@defi-oracle.io>
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
"""Post-task reflection: run Critic and write lessons/heuristics to reflective memory."""
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from typing import Any, Callable, Protocol
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from fusionagi._logger import logger
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from fusionagi.schemas.messages import AgentMessage, AgentMessageEnvelope
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class CriticLike(Protocol):
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"""Protocol for critic agent: must have identity and handle_message."""
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identity: str
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def handle_message(self, envelope: AgentMessageEnvelope) -> AgentMessageEnvelope | None:
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...
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class ReflectiveMemoryLike(Protocol):
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"""Protocol for reflective memory: must have add_lesson and set_heuristic."""
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def add_lesson(self, lesson: dict[str, Any]) -> None:
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...
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def set_heuristic(self, key: str, value: Any) -> None:
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...
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ReflectionCallback = Callable[[str, dict[str, Any]], None]
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"""Callback (event_type, payload) -> None. Emits 'reflection_done' with task_id and evaluation."""
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def run_reflection(
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critic_agent: CriticLike,
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task_id: str,
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outcome: str,
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trace: list[dict[str, Any]],
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plan: dict[str, Any] | None,
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reflective_memory: ReflectiveMemoryLike | None,
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orchestrator_callback: ReflectionCallback | None = None,
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) -> dict[str, Any] | None:
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"""
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Trigger reflection: send evaluate_request to Critic, then write evaluation
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to reflective memory (lessons, heuristics). Optionally notify orchestrator
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via orchestrator_callback(event_type, payload); e.g. "reflection_done" with task_id and evaluation.
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Returns evaluation dict or None.
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"""
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envelope = AgentMessageEnvelope(
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message=AgentMessage(
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sender="orchestrator",
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recipient=critic_agent.identity,
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intent="evaluate_request",
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payload={
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"outcome": outcome,
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"trace": trace,
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"plan": plan,
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},
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),
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task_id=task_id,
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)
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response = critic_agent.handle_message(envelope)
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if not response or response.message.intent != "evaluation_ready":
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return None
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evaluation: dict[str, Any] = response.message.payload.get("evaluation", {}) # type: ignore[assignment]
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if reflective_memory:
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reflective_memory.add_lesson({
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"task_id": task_id,
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"outcome": outcome,
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"evaluation": evaluation,
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})
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suggestions = evaluation.get("suggestions", [])
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for i, s in enumerate(suggestions[:5]):
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reflective_memory.set_heuristic(f"suggestion_{task_id}_{i}", s)
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if orchestrator_callback:
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try:
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orchestrator_callback("reflection_done", {"task_id": task_id, "evaluation": evaluation})
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except Exception:
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logger.exception(
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"Orchestrator callback failed (reflection_done); callback is best-effort",
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extra={"task_id": task_id},
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)
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return evaluation
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