CrabRAG: Why Automated Assistants Need Graph Memory, Not More Tokens

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Autonomous assistants are easy to demo and hard to make reliable. The problem is usually not tool

access. It is memory. Most assistant architectures still treat memory as a chat log plus vector

retrieval. That is fine for document question answering, but it breaks down when the assistant must

connect conversations, people, tools, and decisions across multiple tool iterations. For an AI

engineer, a single request can depend on a Slack thread, a GitHub PR, a failed CI run, a calendar

event, and prior operating preferences or constraints. These are not isolated pieces of context.

They form a connected state that changes as work progresses and context grows. In this talk, I’ll

show why knowledge graphs, context graphs, and GraphRAG provide a better foundation for OpenClaw-

style assistants. Knowledge graphs capture durable entities and relationships. Context graphs

capture the operational layer assistants usually lose, including actions, decision traces,

provenance, and recency. GraphRAG turns that structure into task-time context by combining graph

traversal, semantic retrieval, and tool use. Attendees will leave with practical patterns for schema

design, retrieval routing, and evaluation, plus a concrete blueprint for assistants that remember

more than the last prompt and retrieve more than the nearest chunk.

Related YouTube Video

Connecting the Dots with Context Graphs — Stephen Chin, Neo4j (speaker-match related prior/adjacent AI Engineer video; captions: English auto-captions).

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Related video transcript availability: English auto-captions. Treat this as supporting context, not a recording of this exact scheduled session unless later confirmed. Not fetched yet.

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