What makes Agentmem different from other memory tools?
Agentmem focuses on trust, not just storage. It uses memory lifecycle states, conflict detection, staleness detection, and health scoring to ensure the agent retrieves only what is still true.
@Thezenmonster
基本情報
Governed memory for long-lived coding agents. Trust, provenance, conflict detection. Local-first.
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Agentmem is a shared memory system for Claude Code, Cursor, and Codex that tracks the trustworthiness of stored information. It saves sessions, detects stale and conflicting rules, and prevents AI coding assistants from repeating old mistakes. Agentmem is built for developers who want their agent to remember what is still true across sessions.
Install Agentmem with pip install quilmem[mcp], then run agentmem init --tool claude --project myapp to set up. Restart your editor to give your agent 13 memory tools. For Claude Code, add the MCP server configuration to .claude/settings.json. Agentmem also provides a Python API and CLI for direct use.
Agentmem focuses on trust, not just storage. It uses memory lifecycle states, conflict detection, staleness detection, and health scoring to ensure the agent retrieves only what is still true.
Agentmem requires only SQLite with WAL mode. No external databases, API keys, or cloud infrastructure are needed. It uses FTS5 for search and porter stemming.
Recall uses a composite score of text relevance (25%), trust status (20%), provenance (20%), recency (15%), frequency (10%), and confidence (10%). Deprecated and superseded memories are excluded.
Yes. You can scope memories by project using the `project` parameter in the Python API or CLI. Each project keeps its own set of memories in the same database file.
Agentmem is released under the MIT license.
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