Mem.ai notes and collections accessible from your agent.

Setup guides & articles for Mem.ai MCP

Retrieval-augmented agent memory: RAG specifically built for agentic loopsGeneric RAG retrieves once and stops. Agentic RAG retrieves repeatedly inside the loop, refines the query, and feeds back into the next step. Here is the pattern, the failure modes, and a reference implementation.

Semantic memory for AI agents: knowledge graphs that survive contact with productionSemantic memory is the agent's long-term knowledge of facts and relationships. Pure vector stores miss the structure; knowledge graphs catch it. Here is how to build a graph-backed memory layer that holds up at scale.

Agent episodic memory implementation: what to capture, what to drop, how to retrieveEpisodic memory is what the agent remembers about specific past sessions — events, decisions, who-said-what-when. Here is how to capture it without bloating the index, and how to retrieve it without confusing the agent.

Agent forgetting curve optimization: deciding what your agent should forget on purposeMemory growth is a UX problem before it is a cost problem. Forgetting is a feature, not a bug. Five forgetting strategies — time decay, relevance pruning, contradiction collapse, consent-driven, importance-weighted — with implementation patterns.

Facts