Most agent memory is a chat log with extra steps. Dump turns into a vector store, retrieve the top-k by cosine similarity, and hope the right context shows up. It usually doesn't, and the agent pays for it three turns later.
Agentmemory MCP Server began as a fix for that. It combines BM25, dense vector search, and a knowledge graph, fuses them with reciprocal rank fusion, and ages stored memories on an Ebbinghaus forgetting curve so old context decays instead of drowning new signal. On LongMemEval-S it reaches 95.2% recall at 5. The repo trended on GitHub, ranked on Product Hunt, got picked up by AlphaSignal, and was recognized by the Agentic AI Foundation, none of it paid for.
This talk covers how it was built and where the easy version breaks. Single-method retrieval looks fine in a demo and falls apart on long histories. I'll show why each method earns its place, how the pieces compose with one engine an agent can query over MCP, and which design decisions held up once real workloads hit them.
You'll leave with a memory model that beats top-k vector search and a clear picture of how to expose it to agents through MCP.
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Presentation Language: English
Captioning will be available for attendees in 50+ languages through Wordly. See instructions in each room to utilize captioning.
