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Remembra MCP Server

@remembra-ai

Repository

Basic information

Category

Developer Tools

Transports

stdio

Author

remembra-ai

Submitted by

Mani87-nq

About Remembra MCP Server

Persistent memory layer for AI agents with entity resolution, PII detection, AES-256-GCM encryption at rest, and hybrid search. Self-hosted. 100% on LoCoMo benchmark. Works with Claude Code, Cursor, VS Code, Windsurf, JetBrains, and more.

#memory#ai-memory#mcp#self-hosted#privacy#encryption#rag

Remembra MCP Server config

Add this server to your MCP-compatible client using the configuration below.

{
  "mcpServers": {
    "remembra": {
      "command": "docker",
      "args": [
        "compose",
        "-f",
        "docker-compose.quickstart.yml",
        "up",
        "-d"
      ]
    }
  }
}

Remembra MCP Server tools

Tool list not available yet

This listing does not currently have a tool list to display. This does not mean the server has no tools; check its documentation for supported capabilities.

We auto-extract tools from the README. The maintainer can list them under a ## Tools heading to populate this section.

Overview

What is Remembra?

Remembra is a self-hosted persistent memory layer for AI applications that provides entity resolution, temporal decay, graph-aware recall, and hybrid search. It is designed for developers building AI chatbots, agents, and assistants that need to remember facts, decisions, and context across sessions without vendor lock-in.

How to use Remembra?

Install with one command (curl -sSL ... | bash) or via Docker Compose, then use the REST API, Python SDK (pip install remembra), TypeScript SDK (npm install remembra), or MCP server (remembra-mcp) to store and recall memories. Configure the MCP server by adding the remembra MCP tool to Claude, Cursor, or other compatible clients using environment variables like REMEMBRA_URL.

Key features of Remembra

  • Smart LLM-powered fact extraction from raw text
  • Entity resolution merging aliases into one identity
  • Bi-temporal knowledge graph with point-in-time queries
  • Hybrid semantic + keyword search for accurate recall
  • Built-in PII detection, anomaly monitoring, and audit logging
  • 11 MCP tools including store, recall, update, timeline, and share

Use cases of Remembra

  • Give a chatbot persistent memory across user sessions without repeating questions
  • Enable an AI coding agent to recall past decisions and project context
  • Build a personal assistant that remembers preferences, contacts, and schedules
  • Create multi-agent systems with shared memory spaces for collaborative reasoning
  • Power temporal queries like “Where did Alice work in January 2022?”

Frequently asked questions

1

How does Remembra compare to alternatives like Mem0, Zep, or Letta?

Remembra offers free bi-temporal relationships, entity resolution, built-in PII detection, hybrid search, 6 embedding providers, and an MCP server with 11 tools — features often gated behind paid plans or missing in other solutions. See the full feature comparison table in the README.

2

What dependencies or runtime does Remembra require?

Remembra uses Qdrant for vector storage, SQLite for metadata/graph storage, and optionally Ollama for local embedding inference. It can be run via Docker Compose or the quickstart script; no external API keys are needed for local use.

3

Where does my data live?

Data is stored locally in your self-hosted instance (Qdrant and SQLite). You control all data; no data is sent to external services unless you configure an external embedding provider.

4

What transport and authentication does the MCP server support?

The MCP server communicates via REST API over HTTP. Authentication is handled through the `REMEMBRA_URL` environment variable; local setups typically run without authentication, but enterprise deployments can integrate security features like audit logging and field encryption.

5

Is Remembra open-source and what license does it use?

Yes, Remembra is released under the MIT License, allowing free use, modification, and distribution.

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