The Autonomous Nervous System for AI Agents — local-first MCP middleware with temporal graph memory, active coercion, and proactive goal alignment.
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<div align="center"> <img src="assets/memento-logo.svg" alt="Memento Logo" width="100%"> <h1>Memento</h1> <p><strong>The Autonomous Nervous System for AI Agents</strong></p> [](https://pypi.org/project/memento-mcp/) [](https://www.gnu.org/licenses/agpl-3.0) [](https://www.python.org/downloads/) [](https://modelcontextprotocol.io/) </div> --- Memento is a local-first, open-source MCP middleware that gives your AI agents (Cursor, Claude Desktop, Trae, etc.) persistent memory, proactive goal enforcement, and autonomous intelligence — all running on a zero-cost **SQLite temporal graph** with **Reciprocal Rank Fusion (RRF)** retrieval. No cloud databases. No API calls for storage. Everything stays on your machine. --- ## Architecture ### Temporal Graph Memory (RRF) Built on **SQLite FTS5** (full-text search) and **cosine similarity** (vector embeddings). Fuses keyword matches and semantic meaning via **Reciprocal Rank Fusion**. WAL-mode enabled for concurrency. ### Tri-State Goal Enforcer Keep your AI aligned with project objectives at three escalation levels: - **Level 1 — Context Injection**: Automatically injects active goals into every search result. Active by default. - **Level 2 — Strict Mentor**: Forces the AI to submit code/plans for goal alignment evaluation via LLM. - **Level 3 — Daemon Push**: File-watcher monitors your workspace and proactively flags goal drift. ### Active Coercion (Code Immune System) Deterministic regex/tree-sitter rules that block anti-patterns at commit time and in the IDE. 100% deterministic — zero LLM hallucination risk during enforcement. ### Autonomous Agent Background cognitive loop with four levels: - **off**: No background behavior (default)…
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