Introduction

Give your agent long term memory. It creates and searches vector memory snippets, enabling chatbots to retain custom user rules, code guidelines, and diagnostic patterns across dialog sessions.

Key Capabilities and Features

Below are the main actions this adapter exposes to Model Context Protocol clients:

  • Insert memory strings: Handled dynamically with schema-guaranteed JSON-RPC calls.
  • Vector database query lookups: Handled dynamically with schema-guaranteed JSON-RPC calls.
  • Clear memory nodes: Handled dynamically with schema-guaranteed JSON-RPC calls.
  • Save user parameters: Handled dynamically with schema-guaranteed JSON-RPC calls.

Sample Use Cases

Here is how development teams utilize this integration:

  1. Custom developer coding helper memory systems: Enabling models to execute deep semantic checks and audits contextually.
  2. Personal scheduling assistant memories: Enabling models to execute deep semantic checks and audits contextually.
  3. Persistent chat dialog logs: Enabling models to execute deep semantic checks and audits contextually.

Basic Installation and Setup

To plug this into your agent client (e.g., Claude Desktop, Cursor), execute or declare the following parameters coordinate:

pip install mcp-server-memory

Security Notes and Guidelines

  • Ensure memory databases are localized to prevent credentials leaks from entering database storage sheets.
  • Avoid committing tokens directly to public configurations.
  • Monitor resource limits during autonomous iteration loops.