MCP ServerSTDIOOfficialv0.1.2

TeamMemory MCP Server

Store and retrieve team knowledge in a PostgreSQL vector database so AI can learn from past decisions, solutions, and context across conversations. Built for teams that want institutional memory without manual documentation.

io.github.ysydhc/team-memory

Hosted URL

Local install

Transport

STDIO

Auth

1 required env var

What the TeamMemory MCP server does

How models use it and what it is built for.

Store and retrieve team knowledge in a PostgreSQL vector database so AI can learn from past decisions, solutions, and context across conversations. Built for teams that want institutional memory without manual documentation.

Connect to TeamMemory

Local install — runs as a subprocess.

uvx team_memory==0.1.2

Environment variables

Configuration this server reads at startup.

  • DATABASE_URLRequiredSecret

    PostgreSQL connection string (with pgvector extension)

  • TEAM_MEMORY_CONFIG

    Path to config.yaml (optional)

Resources

Where to find authoritative docs and source for TeamMemory.

Example prompts for TeamMemory

Paste any of these into Agent Studio after connecting TeamMemory.

  • Add our decision to use PostgreSQL for this project and why we chose it over MongoDB
  • What solutions has the team tried for authentication in the past?
  • Retrieve all past incidents related to database performance
  • Show me similar problems the team has solved before

TeamMemory MCP server — FAQ

Common questions about connecting and running TeamMemory.

  • What database does team-memory use?

    Team Memory requires PostgreSQL with the pgvector extension enabled. You provide the connection string via the DATABASE_URL environment variable, allowing you to self-host or use a managed PostgreSQL service.

  • How do I set up team-memory?

    Install via `uvx team_memory@0.1.2`, set the required DATABASE_URL environment variable to your PostgreSQL connection string, and optionally provide a TEAM_MEMORY_CONFIG path pointing to a config.yaml file for additional settings.

  • Can I customize how team memory stores or retrieves knowledge?

    Yes, you can provide a config.yaml file by setting the TEAM_MEMORY_CONFIG environment variable. Refer to the project documentation for the schema and available configuration options.

  • Does team-memory use vector embeddings?

    Yes, it uses pgvector (PostgreSQL's vector extension) to store and search embeddings, enabling semantic similarity search across team knowledge rather than just keyword matching.

  • What happens if I don't set DATABASE_URL?

    The server will fail to start because DATABASE_URL is required. Make sure your PostgreSQL instance has pgvector installed and your connection string is valid before launching the MCP server.

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