AgentMesh Uses FalkorDB Graph Memory to Help AI Agents Retain Project Context

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A developer built AgentMesh during FalkorDB Graph Hacks 2026 as a persistent memory layer for AI agents. The project addresses the difficulty agents have retaining project decisions across separate sessions and handling changed requirements. Instead of storing knowledge as isolated text, AgentMesh models memories, sessions, and decisions as a connected graph so relationships are part of the stored data. Three specialized agents handle extraction, retrieval, and contradiction review, allowing a newer decision that conflicts with an earlier one to be flagged. The developer presents the graph-based approach as a way to retrieve prior context without requiring users to restate earlier decisions.

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