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

Conference Context

Session Description

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.

Media Evidence

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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Supporting Slides

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Synthesis

Synthesized Breakdown

CrabRAG: Why Automated Assistants Need Graph Memory, Not More Tokens ## Conference Context - Date/time: 2026-07-01 · 10:45am-11:05am - Track/room: Graphs · Track 5 - Speaker(s): Stephen Chin - Session type/status: sponsor · confirmed - Track: Graphs - Room: Track 5 - Session type: sponsor - Status: confirmed ## Session Description 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.

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