agent-memory-mcp

You can add persistent memory to AI agents by running an MCP server that stores and retrieves long-term project knowledge, capturing cross-project context and decision history for rapid recall between sessions.

An MCP server handles searchable memory management by exposing tooling commands for memory_search, memory_write, memory_read, and memory_stats, allowing agents to automatically sync with and query project documentation.

Yes, you can retrieve specific agent memories by using the memory_search command to find relevant memories by query, type, or tags, and use memory_read to fetch specific entries by key.

The best way to capture decision history and patterns for AI agents is using the memory_write command to store new knowledge, then monitoring retention and usage over time with the memory_stats command.

Yes, project documentation is utilized as the persistent memory bank automatically syncs with it, requiring YAML frontmatter with name and description to properly store and retrieve long-term memories.

AI agents lose context between sessions without a persistent memory bank, which this skill solves by storing long-term project knowledge and pattern capture in an MCP server for durable knowledge retention.