MCP Server Interface¶

Agent Memory Server offers an MCP (Model Context Protocol) server interface powered by FastMCP, providing tool-based memory management for LLMs and agents:

• set_working_memory: Set working memory for a session (like PUT /sessions/{id}/memory API). Stores structured memory records and JSON data in working memory with automatic promotion to long-term storage.
• create_long_term_memories: Create long-term memories directly, bypassing working memory. Useful for bulk memory creation.
• search_long_term_memory: Perform semantic search across long-term memories with advanced filtering options.
• edit_long_term_memory: Update existing long-term memories with new or corrected information. Allows partial updates to specific fields while preserving other data.
• delete_long_term_memories: Remove specific long-term memories by ID. Useful for cleaning up outdated or incorrect information.
• get_long_term_memory: Retrieve specific memories by ID for detailed inspection or verification before editing.
• memory_prompt: Generate prompts enriched with working memory session and long-term memories. Essential for retrieving relevant context before answering questions.

Available MCP Tools¶

The MCP server provides the following tools that AI agents can use to manage memories:

Memory Search and Retrieval¶

search_long_term_memory - Search for memories using semantic similarity - Supports advanced filtering by user, session, namespace, topics, entities, and timestamps - Configurable query optimization and recency boost - Returns ranked results with relevance scores

get_long_term_memory - Retrieve specific memories by their unique ID - Useful for inspecting memory details before editing - Returns complete memory record with all metadata

memory_prompt - Generate AI prompts enriched with relevant memory context - Combines working memory and long-term memory search results - Essential for providing context to AI agents before responses

Memory Management¶

create_long_term_memories - Create new persistent memories directly - Bypasses working memory for bulk operations - Supports all memory types (semantic, episodic, message) - Automatic indexing and embedding generation

edit_long_term_memory - Update existing memories with corrections or new information - Supports partial updates (only change specific fields) - Automatic re-indexing and embedding regeneration - Preserves memory ID and creation timestamp

delete_long_term_memories - Remove specific memories by ID - Supports batch deletion of multiple memories - Useful for cleanup and data management

Working Memory¶

set_working_memory - Manage session-specific conversation state - Store messages, structured memories, and arbitrary data - Automatic promotion of memories to long-term storage - TTL-based expiration for session cleanup

Using the MCP Server with Claude Desktop, Cursor, etc.¶

You can use the MCP server that comes with this project in any application or SDK that supports MCP tools.

Claude¶

For example, with Claude, use the following configuration:

{
  "mcpServers": {
    "redis-memory-server": {
      "command": "uv",
      "args": [
        "--directory",
        "/ABSOLUTE/PATH/TO/REPO/DIRECTORY/agent-memory-server",
        "run",
        "agent-memory",
        "mcp",
        "--mode",
        "stdio"
      ]
    }
  }
}

NOTE: On a Mac, this configuration requires that you use brew install uv to install uv. Probably any method that makes the uv command globally accessible, so Claude can find it, would work.

Cursor¶

Cursor's MCP config is similar to Claude's, but it also supports SSE servers, so you can run the server in SSE mode and pass in the URL:

{
  "mcpServers": {
    "redis-memory-server": {
      "url": "http://localhost:9000/sse"
    }
  }
}