Team Memory MCP: Open Source Shared Memory for Claude Code with Bayesian Confidence Scoring
Team Memory MCP is an open source (MIT licensed) shared memory solution for Claude Code that addresses the problem of AI agents forgetting team-specific patterns between sessions. The tool tracks collective confidence in patterns rather than just storing information.
Key Features
- Bayesian Confidence Scoring: Uses a Beta-Bernoulli model to rank patterns based on real-world evidence. Confirmations from engineers increase confidence; corrections decrease it.
- Temporal Decay: Knowledge that isn't re-validated gradually fades with a 90-day half-life, keeping the memory relevant.
- Transparent Scoring: The scoring is computed from real-world evidence using pure math, not expensive LLM API calls.
- Zero-Config Setup: Can be added to Claude Code in seconds with a single command.
Setup
Add Team Memory MCP to Claude Code with this command:
claude mcp add team-memory -- npx team-memory-mcpResources
The developer has published a deep dive article covering the technical implementation, the Bayesian math behind the scoring system, and a full setup guide on LinkedIn. The project is available on GitHub at github.com/gustavolira/team-memory-mcp.
This tool is designed for development teams using Claude Code who need to maintain consistent project-specific standards and patterns across AI coding sessions.
📖 Read the full source: r/ClaudeAI
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