mcp-blackboard

What is mcp-blackboard?

mcp-blackboard is a lightweight memory server designed for managing context and memory in multi-agent task execution using the Model Context Protocol (MCP). It allows multiple AI agents to store, retrieve, and score context on a shared blackboard.

How to use mcp-blackboard?

To use mcp-blackboard, clone the repository from GitHub, set up a local development environment or use Docker Compose, and run the server. The API will be available at http://127.0.0.1:8000 by default.

Key features of mcp-blackboard?

• Unified memory for agent context management.
• Filesystem abstraction with support for various storage backends.
• Real-time updates via Server-Sent Events (SSE).
• Automatic housekeeping with pluggable cron jobs.
• Container-ready with a slim Docker image.

Use cases of mcp-blackboard?

1. Facilitating collaboration among AI agents in research tasks.
2. Managing context for complex workflows in AI applications.
3. Storing and retrieving embeddings and structured objects efficiently.

FAQ from mcp-blackboard?

• What is the purpose of mcp-blackboard?

It serves as a centralized memory server for AI agents to manage context and collaborate effectively.

• Is mcp-blackboard easy to integrate?

Yes! It is designed to be easily integrated into any MCP-compatible workflow.

• What programming language is mcp-blackboard built with?

mcp-blackboard is built using Python.