home / mcp / hippocampus memory mcp server
Python-based MCP server that stores, retrieves, consolidates, and forgets memories via semantic search for persistent LLM context.
Configuration
View docs{
"mcpServers": {
"jameslovespancakes-memory-mcp": {
"command": "python",
"args": [
"-m",
"memory_mcp_server.server"
]
}
}
}You can run a Python-based MCP server that gives large language models persistent, hippocampus-inspired memory across sessions. It stores, retrieves, consolidates, and forgets memories using semantic similarity search, helping you build richer, long-lived context for your LLM workflows.
Connect to the memory MCP server from your MCP client to store and retrieve memories by semantic meaning. Use memory_write to persist new memories with tags and metadata, memory_read to fetch memories that match a query semantically, memory_consolidate to merge similar memories and reduce redundancy, memory_forget to remove memories by age, importance, or tags, and memory_stats to check system status.
Prerequisites: Python 3.9 or newer must be installed on your system.
pip install hippocampus-memory-mcpStart a local MCP server using the provided development command. This runs the server in your environment without needing a remote host.
python -m memory_mcp_server.serverTo add persistent memory support to Claude Desktop, configure the MCP server entry so Claude launches the server as a local process. The example below shows how to invoke the server directly from Python.
{
"mcpServers": {
"memory": {
"command": "python",
"args": ["-m", "memory_mcp_server.server"]
}
}
}Retrieve memories by semantic similarity using a query text and optional filters.
Store new memories with text, tags, metadata, and an optional importance score.
Merge similar memories to reduce redundancy based on a similarity threshold.
Remove memories by age, importance, or tags to manage storage.
Provide system statistics and memory usage metrics.