GrayMatter MCP server

GrayMatter

AI agents forget everything between sessions. GrayMatter gives them persistent memory, a self-building knowledge graph, and cuts context tokens by 90%. One binary. Drop it in. Run it. No Docker, no databases, no config files, no cloud accounts, no bullshit. General-purpose MCP server. Zero vendor lock-in. Works with Claude Code, Cursor, Codex, OpenCode, Antigravity — and any MCP-compatible client. Also a plain Go library if you don't use MCP. Free. Offline. No account required.

Why

Every AI agent is stateless by default. Each run re-injects the full conversation history — and that history grows linearly. Two prompts in and you've already burned half of your daily quota.

That's not just a memory problem. That's a money and performance problem.

Mem0, Zep, Supermemory solve this — but they're Python/TypeScript-only and require a running server. The Go ecosystem has no production-ready, embeddable, zero-dependency memory layer for agents.

That gap is GrayMatter.

~90% reduction in context tokens — versus full-history injection. Remembers what a sliding window forgets: facts planted 96 sessions back come back 83% of the time. Context quality improves over time as consolidation surfaces only what matters. No Docker. No Redis. No API key required for storage. Drop it in once. It auto-connects to Claude Code, Cursor, Codex, OpenCode, Antigravity — any MCP-compatible client picks it up automatically.

Knowledge Graph

Your agent doesn't just remember facts — it builds a map of how they connect.

Run the daemon with --kg and every consolidation cycle extracts typed entities (person, organization, project) and links the ones that appear together. No manual tagging. No configuration. The graph builds itself from ordinary use.

Shortened. The full README is on GitHub.