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https://github.com/trustgraph-ai/trustgraph.git
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153ae9ad30
Refactor agent provenance so that the decision (thought + tool selection) and the result (observation) are separate DAG entities: Question ← Analysis+ToolUse ← Observation ← ... ← Conclusion Analysis gains tg:ToolUse as a mixin RDF type and is emitted before tool execution via an on_action callback in react(). This ensures sub-traces (e.g. GraphRAG) appear after their parent Analysis in the streaming event order. Observation becomes a standalone prov:Entity with tg:Observation type, emitted after tool execution. The linear DAG chain runs through Observation — subsequent iterations and the Conclusion derive from it, not from the Analysis. message_id is populated on streaming AgentResponse for thought and observation chunks, using the provenance URI of the entity being built. This lets clients group streamed chunks by entity. Wire changes: - provenance/agent.py: Add ToolUse type, new agent_observation_triples(), remove observation from iteration - agent_manager.py: Add on_action callback between reason() and tool execution - orchestrator/pattern_base.py: Split emit, wire message_id, chain through observation URIs - orchestrator/react_pattern.py: Emit Analysis via on_action before tool runs - agent/react/service.py: Same for non-orchestrator path - api/explainability.py: New Observation class, updated dispatch and chain walker - api/types.py: Add message_id to AgentThought/AgentObservation - cli: Render Observation separately, [analysis: tool] labels |
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| .. | ||
| __init__.py | ||
| agent.py | ||
| collection.py | ||
| config.py | ||
| diagnosis.py | ||
| flow.py | ||
| library.py | ||
| llm.py | ||
| lookup.py | ||
| nlp_query.py | ||
| prompt.py | ||
| query.py | ||
| retrieval.py | ||
| rows_query.py | ||
| storage.py | ||
| structured_query.py | ||
| tool_service.py | ||