I built Mnemory, an open-source memory layer for long-running AI agents.
The goal is to make agent memory more structured than “put everything into a vector DB”. Mnemory stores facts, preferences, episodic/context memory, TTLs, importance, user/agent scoping, and artifact-backed long-form memory. It also exposes an MCP server interface.
I built it because I kept running into the same problem with agents: durable facts and short-lived context need different treatment, but many systems collapse everything into one retrieval bucket.
Also I wanted to have one memory system that I can just plug and play into any system.. Opencode, OpenWebUI, Openclaw, Hermes, Cognis, etc.
I’d be interested in feedback from people building personal agents, long-running automations, or memory systems for LLM apps.
I built Mnemory, an open-source memory layer for long-running AI agents.
The goal is to make agent memory more structured than “put everything into a vector DB”. Mnemory stores facts, preferences, episodic/context memory, TTLs, importance, user/agent scoping, and artifact-backed long-form memory. It also exposes an MCP server interface.
I built it because I kept running into the same problem with agents: durable facts and short-lived context need different treatment, but many systems collapse everything into one retrieval bucket.
Also I wanted to have one memory system that I can just plug and play into any system.. Opencode, OpenWebUI, Openclaw, Hermes, Cognis, etc.
I’d be interested in feedback from people building personal agents, long-running automations, or memory systems for LLM apps.