OmniMem
A self-hosted MCP server that gives AI Agents persistent memory across sessions, projects, and machines.
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Overview
Self-hosted semantic memory for AI Agents. Persistent sessions, experience scoring, and a graveyard for dead ends, backed by knowledge acquired from the RSS you read and stored in a Valkey vector search then exposed as an MCP server. https://omnimem.org agent -- "MCP · Streamable HTTP / SSE · :8765" --> mcp browser -- "HTTP · :8080" --> webui subgraph stack["Docker Compose stack"] mcp["MCP server<br/>Python · FastMCP<br/><i>remember · recall · briefing<br/>compile_skill · record_experience</i>"] webui["Web UI<br/>Starlette · htmx · Jinja2<br/><i>dashboard · search · skills<br/>projects · backups · /metrics</i>"] rss["RSS worker<br/>feedparser · APScheduler<br/>Claude Haiku summaries"] valkey[("Valkey + valkey-search<br/>HNSW vector indexes<br/><i>idx:episodic · idx:project · idx:knowledge<br/>idx:preference · idx:skill</i>")]
Installation
docker compose up -d
Configuration
{
"mcpServers": {
"omnimem": {
"type": "sse",
"url": "http://localhost:8765/sse"
}
}Capabilities
- Session briefing
Ask for a `briefing()` to get project context, experience stats, stale memories, new articles, and skill suggestions in one call.
- Avoid past dead ends
Have the agent check the graveyard before suggesting a library or pattern that previously failed.
- Recall past decisions
Use `recall` to surface decisions, fixes, and patterns from earlier sessions across projects.
- Record hard-won experience
Log a fix with `record_experience` so lessons that took real effort surface more readily next time.
- Deduplicate memories
Run `find_duplicates()` to flag and bulk-clean near-identical stored memories.
- Backup your memory
Export everything to a JSON file you own with `dump_to_file()`.
Tags
- memory
- ai-agent
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