rmbr MCP server

rmbr

<!-- mcp-name: io.github.SRock44/rmbr -->

Give your agent memory and knowledge. One file, three lines, no server, no API key.

rmbr ("remember", vowels deleted) is an embedded, local-first memory + retrieval engine for AI agents and LLM apps — what SQLite is to Postgres, rmbr aims to be to hosted memory services.

v0.2.7. pip install rmbr gets you a working library: Memory, Index, Policy, MCP support, an optional HTTP server, PDF/DOCX ingestion, and framework adapters for LangChain/LlamaIndex/LangGraph/mem0 (all below), all implemented and tested — see docs/PLAN.md and docs/ARCHITECTURE.md for the design.

Start with three lines, then reach for exactly as much more as you need — nothing below is required to use the part above it:

• Memory — durable, searchable notes your agent chooses to keep, namespaced per agent
• Index — hybrid (keyword + semantic) search over your own docs, for RAG
• Policy — deny-by-default access control, so one agent can't read another's memory unless you explicitly allow it
• Framework adapters — LangChain and LlamaIndex retrievers, a real LangGraph BaseStore, a mem0-API-compatible drop-in, raw OpenAI/Anthropic tool-calling export
• An MCP server, for any MCP client (Claude Desktop, Claude Code, Cursor, ...) — optional
• An HTTP server, for serverless functions or anything that'd rather curl it than hold a connection open — also optional

If you only ever use the first three, that's not a "basic" use of rmbr — that is rmbr for most people. The server modes exist for the specific cases they solve, not because you're expected to grow into them.

Contents: Why · Quickstart · Multi-agent isolation · MCP support · HTTP support · Alternatives · Performance · Roadmap

Why

Agents can already "remember" things across restarts — a CLAUDE.md, a system prompt, a JSON file on disk. That's not new, and rmbr isn't claiming otherwise.

What breaks is what happens as that file grows. Every fact in a static context file costs tokens on every single call, whether it's relevant to the current task or not — so it either stays small (a few dozen hand-curated notes) or turns into noise nobody's cheaply reading anymore. There's no ranking: the agent gets the whole file, or nothing, never just the 5 facts that actually matter for this turn. A static file gets more expensive and less useful the more the agent learns; a searchable memory gets more useful and stays the same cost per call. mem.recall(query) returns the k most relevant memories out of however many thousand you've accumulated — that's the actual gap between "an agent that can write to a file" and "an agent with memory."

Shortened. The full README is on GitHub.