Open-source MCP memory server with knowledge graph and learning features

An open-source MCP server called cuba-memorys provides persistent memory for AI agents, going beyond simple RAG or vector stores. Written in Rust with a PostgreSQL + pgvector backend, it implements a knowledge graph architecture with learning capabilities.

Architecture and features

The system stores entities, observations, and typed relations rather than flat documents. Key features include:

• Exponential decay — Memories fade realistically using importance = importance * exp(-0.693 * days/halflife) with a 30-day halflife
• Hebbian + BCM metaplasticity — Oja's rule with EMA sliding threshold; memories strengthen with access and self-normalize via BCM
• 4-signal RRF fusion (k=60) — Combines ts_rank + trigrams + pgvector HNSW + importance with entropy-routed weighting to detect keyword-dominant vs semantic queries
• Leiden community detection — Traag et al. 2019 algorithm for discovering clusters in the knowledge graph
• Personalized PageRank — Ranks entity importance based on graph topology
• Anti-hallucination verification — Triangulates claims against stored knowledge with graduated confidence scoring
• Error memory with pattern detection — Triggers warnings when ≥3 similar errors occur

Performance benchmarks

The Rust implementation shows significant improvements over the original Python version:

• Binary size: ~50MB venv (Python) vs 7.6MB (Rust)
• Entity create: ~2ms (Python) vs 498μs (Rust)
• Hybrid search: <5ms (Python) vs 2.52ms (Rust)
• Memory usage: ~120MB (Python) vs ~15MB (Rust)
• Dependencies: 12 packages (Python) vs 0 runtime (Rust)

Implementation details

The server provides 13 MCP tools and works with any MCP-compatible client including Claude Code, Cursor, and Windsurf. It's self-hosted with a PostgreSQL backend and makes no external API calls. All algorithms are based on peer-reviewed papers with citations in the README.

Installation options:

pip install cuba-memorys

or

npm install -g cuba-memorys

The project is available under CC BY-NC 4.0 license on GitHub.