An open source natural temporal memory for claude code, hermes and openclaw agent

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Summary

agentmemory is an open-source library that provides natural temporal memory for AI agents like Claude Code, Hermes, and OpenClaw. It uses a three-tier architecture with hybrid retrieval (BM25, vector, knowledge graph) and Ebbinghaus decay, achieving ~92% fewer tokens and 200x more tool calls before context limits.

You can now give Hermes Agent infinite memory. The three-tier architecture is the cleanest I've seen in any open-source agent. The Tier 1 cap is the constraint. MEMORY md file is 2,200 chars. USER md file is 1,375 chars. Hit 80% and consolidation kicks in: the agent merges related entries into denser versions, which is lossy. The longer you run Hermes, the more your earlier context gets compressed away. Tier 2 (SQLite FTS) is unlimited capacity but every retrieval needs an LLM summarization pass. Tokens and latency on the critical path. Tier 3 is the plug-in slot. That's where agentmemory fits. What it adds on top of the existing design: → Hybrid retrieval: BM25 + vector + knowledge graph, fused with RRF → Ebbinghaus decay so unused memories fade gracefully instead of getting consolidated out → Token-budgeted injection that keeps Tier 1 clean → Benchmarked on LongMemEval → 90% savings Same numbers as the Claude Code benchmarks: ~92% fewer tokens at 240 observations. 200x more tool calls before hitting context limits. Hermes already exposes the slot. agentmemory is the obvious thing to plug in. https://github.com/rohitg00/agentmemory
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rohitg00/agentmemory

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agentmemory is an open-source persistent memory layer for AI coding agents (Claude Code, Cursor, Gemini CLI, Codex CLI, etc.) that uses knowledge graphs, confidence scoring, and hybrid search to give agents long-term memory across sessions via MCP, hooks, or REST API. Built on the iii engine, it requires no external databases and exposes 51 MCP tools.

Agentmemory

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Agentmemory offers persistent memory for AI models such as Codex, Hermes, OpenClaw, and Claude, allowing them to maintain long-term context across interactions.