TeamMemory
Team experience database - let AI accumulate, retrieve and apply team knowledge across chats.
What is TeamMemory?
TeamMemory is a Model Context Protocol (MCP) server that allows AI assistants like Claude, Cursor, and VS Code to team experience database - let ai accumulate, retrieve and apply team knowledge across chats.
Team experience database - let AI accumulate, retrieve and apply team knowledge across chats.
This server falls under the Knowledge & Memory category on MCPgee, the world's largest MCP server directory with 33,000+ servers.
Features
- Team experience database - let AI accumulate, retrieve and a
Use Cases
Maintainer
Works with
Installation
PIP
pip install team_memoryManual Installation
pip install team_memoryConfiguration
Configuration Details
claude_desktop_config.json
Performance
Response Metrics
Resource Usage
Frequently Asked Questions about TeamMemory
What is TeamMemory?
TeamMemory is a Model Context Protocol (MCP) server that team experience database - let ai accumulate, retrieve and apply team knowledge across chats. It connects AI assistants to external tools and data sources through a standardized interface.
How do I install TeamMemory?
Install via pip with: pip install team_memory. Then configure your AI client to connect to this MCP server.
Which AI clients work with TeamMemory?
TeamMemory works with all major MCP-compatible AI clients including Claude Desktop, Claude Code, Cursor, VS Code (GitHub Copilot), Windsurf, and Cline.
Is TeamMemory free to use?
Yes, TeamMemory is open source and available under the MIT license. You can use it freely in both personal and commercial projects.
TeamMemory Alternatives — Similar Knowledge & Memory Servers
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Everos
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Browse More Knowledge & Memory MCP Servers
Explore all knowledge & memory servers available in the MCPgee directory. Each server includes setup guides for Claude, Cursor, and VS Code.
Set Up TeamMemory in Your Editor
Choose your AI client for step-by-step setup instructions.
Quick Config Preview
Add this to your claude_desktop_config.json or .cursor/mcp.json
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