Agentmemory

In plain words

Agentmemory is an open-source tool that provides persistent memory for AI coding agents like Claude Code, Codex, and Hermes. It stores observations from coding sessions using 92% fewer tokens than standard context dumping, enabling agents to maintain searchable memory of up to 1,000 observations while staying within token limits. The tool allows developers to execute significantly more tool calls before hitting context constraints, reducing memory overhead in long-running coding projects.

written from the facts on this page · September 2026

From the sources

You can now give Hermes, Claude Code, and Codex infinite memory. Agentmemory is trending on GitHub with 5,000+ Stars. CLAUDE md dumps 22,000+ tokens into context at 240 observations agentmemory: 1,900 tokens. same observations. 92% less. At 1,000 observations, 80% of your built-in memories become invisible. agentmemory keeps 100% searchable. benchmarked on 240 real coding sessions → Up to 95% fewer tokens per session → 200x more tool calls before hitting context limits → 100% open source

Persistent memory for AI coding agents. Capture every session, recall it in the next one. Runs locally with zero external databases. Works with every MCP client.

Give your coding agent a memory that survives the session. 95.2% retrieval recall on LongMemEval-S with about 92% fewer input tokens than full-context replay, all on your own machine. Built on the iii engine: every memory operation is a worker, a function, or a trigger. No external databases, queues, or vector stores. The entire runtime is one process. 12 auto-capture hooks piped into every coding agent. Every tool call, every prompt, every stop becomes a compressed observation, stamped with its origin and agent. Hybrid retrieval on the primary recall path: BM25, vector, and knowledge graph scores ranked together, reranked on device. Superseded versions stay out of results. With an LLM…from agent-memory.dev

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