by unibaseio
Provides a lightweight decentralized memory gateway that connects AI agents to Membase for persistent, verifiable multi-session memory.
Membase Mcp enables AI agents to store and retrieve conversation history, interaction records, and knowledge on the Unibase decentralized network. It acts as a bridge between agents and the Membase protocol, ensuring data persistence, traceability, and verification across sessions.
git clone https://github.com/unibaseio/membase-mcp.git
cd membase-mcp
uv run src/membase_mcp/server.py
MEMBASE_ACCOUNT, MEMBASE_CONVERSATION_ID, MEMBASE_ID).get_conversation_idswitch_conversationsave_messageget_messages
The functions interact with the running MCP server to manage memory for the current conversation.get_conversation_id, switch_conversation, save_message, get_messages).Q: Do I need a blockchain node to run the server? A: No. The server uses the Unibase DA network via the Membase protocol; you only need an account and appropriate environment variables.
Q: Can I change the conversation ID at runtime?
A: Yes, use the switch_conversation function to load a different conversation ID and its history.
Q: What format are messages stored in?
A: Messages are stored as raw strings in the Membase network and can be retrieved as a list of the most recent n entries.
Q: Is the memory encrypted? A: Data is secured by the underlying Unibase protocol, which provides cryptographic verification and integrity.
Q: How do I integrate with Claude, Windsurf, Cursor, or Cline? A: Add the JSON snippet from the README to the respective model’s configuration, providing the same environment variables.
Membase is the first decentralized memory layer for AI agents, powered by Unibase. It provides secure, persistent storage for conversation history, interaction records, and knowledge — ensuring agent continuity, personalization, and traceability.
The Membase-MCP Server enables seamless integration with the Membase protocol, allowing agents to upload and retrieve memory from the Unibase DA network for decentralized, verifiable storage.
Messages or memoiries can be visit at: https://testnet.hub.membase.io/
git clone https://github.com/unibaseio/membase-mcp.git
cd membase-mcp
uv run src/membase_mcp/server.py
{
"mcpServers": {
"membase": {
"command": "uv",
"args": [
"--directory",
"path/to/membase-mcp",
"run",
"src/membase_mcp/server.py"
],
"env": {
"MEMBASE_ACCOUNT": "your account, 0x...",
"MEMBASE_CONVERSATION_ID": "your conversation id, should be unique",
"MEMBASE_ID": "your sub account, any string"
}
}
}
}
call functions in llm chat


Please log in to share your review and rating for this MCP.
{
"mcpServers": {
"membase": {
"command": "uv",
"args": [
"run",
"src/membase_mcp/server.py"
],
"env": {
"MEMBASE_ACCOUNT": "<YOUR_ACCOUNT>",
"MEMBASE_CONVERSATION_ID": "<YOUR_CONVERSATION_ID>",
"MEMBASE_ID": "<YOUR_INSTANCE_ID>"
}
}
}
}claude mcp add membase uv run src/membase_mcp/server.pyExplore related MCPs that share similar capabilities and solve comparable challenges
by modelcontextprotocol
A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.
by topoteretes
Provides dynamic memory for AI agents through modular ECL (Extract, Cognify, Load) pipelines, enabling seamless integration with graph and vector stores using minimal code.
by basicmachines-co
Enables persistent, local‑first knowledge management by allowing LLMs to read and write Markdown files during natural conversations, building a traversable knowledge graph that stays under the user’s control.
by smithery-ai
Provides read and search capabilities for Markdown notes in an Obsidian vault for Claude Desktop and other MCP clients.
by chatmcp
Summarize chat messages by querying a local chat database and returning concise overviews.
by dmayboroda
Provides on‑premises conversational retrieval‑augmented generation (RAG) with configurable Docker containers, supporting fully local execution, ChatGPT‑based custom GPTs, and Anthropic Claude integration.
by qdrant
Provides a Model Context Protocol server that stores and retrieves semantic memories using Qdrant vector search, acting as a semantic memory layer.
by doobidoo
Provides a universal memory service with semantic search, intelligent memory triggers, OAuth‑enabled team collaboration, and multi‑client support for Claude Desktop, Claude Code, VS Code, Cursor and over a dozen AI applications.
by GreatScottyMac
Provides a project‑specific memory bank that stores decisions, progress, architecture, and custom data, exposing a structured knowledge graph via MCP for AI assistants and IDE tools.