MARM Memory is a local-first MCP memory server that fuses session memory, a code graph, and a concept graph into one embedded SQLite store, exposing 14 tools over STDIO or HTTP. It suits self-hosters who want persistent context shared across multiple AI coding clients without sending memory to the cloud; recall uses an FTS5 exact lane plus semantic reranking, and the project's own benchmarks show recall latency scaling slowly with memory size. Note that it is not an official vendor product, and HTTP exposure, Docker mounts, and optional consolidation/compaction features add setup complexity and real performance tradeoffs, so verify keys, network binding, and data-directory permissions before deploying.
MARM Memory is a local-first MCP server that gives AI agents a permanent, private memory across Claude Code, Codex, Grok, Gemini, VS Code, and Cursor. It fuses session history, repository code indexing, and a concept graph into an embedded SQLite store, enabling zero-cloud, privacy-first context retention and instant recall, including for multi-agent swarms. The server exposes 14 MCP tools: 7 core memory tools (logging, hybrid recall, summaries, notebooks, log viewing, deletion, and compaction) plus 5 code-graph tools (repo indexing, symbol lookup, call tracing, architecture overview, change-impact analysis) and 2 concept-graph tools (entity/relationship build and query). Recall uses a hybrid pipeline of an FTS5 BM25 exact lane plus semantic reranking, with long memories chunked into overlapping rows and collapsed back to the parent memory. Both STDIO and HTTP transports are supported, with managed swarm, swarm-max, and trusted rate-limit profiles for shared deployments.
{
"mcpServers": {
"marm-memory-local": {
"type": "http",
"url": "http://localhost:8001/mcp"
},
"marm-memory-docker": {
"type": "http",
"url": "http://localhost:8001/mcp",
"headers": {
"Authorization": "Bearer ${env:MARM_API_KEY}"
}
}
}
}