Unverified50% confidenceFactExact time
Knowledge graphs turn the structural output of the AST into an explicit map—files, functions, and classes as nodes, calls, imports, and references as edges—so the agent can trace dependency chains across multiple files.
1
Sources
50%
Confidence
Long-term
Relevance
10/15/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedStructured traces are an event stream that records each step of an agent run—inputs, decisions, tool calls, return values, latency, token consumption, and trigger reasons—in a machine-readable schema, can be queried, aggregated, and replayed by programs, and are distinct from ordinary human-readable logs.69% similarUnverifiedA common hybrid architecture in practice is: first use long context to generate a document map or summary index, then use RAG to locate specific fragments according to the question, and finally hand the retrieval results together with the map to the model for synthesis, requiring cited output (chunk IDs).68% similarUnverifiedThe "query–analyze–generate–distribute" pipeline involves multiple coordinating modules, including a data ingestion layer, an LLM orchestration layer, a tool execution layer, and an output distribution layer.68% similarUnverifiedThe references directory is essentially a local knowledge base; when an Agent executes a Skill, it loads relevant document content into the context window, which is closely related to the RAG retrieval-augmented generation mechanism.66% similarVerifiedThe GraphRAG approach extracts entities and relationships from documents, stores them as nodes and edges in a graph database, and during retrieval first uses vector search to recall candidate nodes before performing multi-hop reasoning along the graph's edges.66% similar
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