Proven on your workload
The Retrieval Optimizer in GoodMem Cloud compares embedding models and rerankers — open-weight or API — on your real queries and validates the winner on held-out data. Evidence, not vibes.
Adding a memory layer makes your agents sharper, more grounded, and far cheaper to run.
The Retrieval Optimizer in GoodMem Cloud compares embedding models and rerankers — open-weight or API — on your real queries and validates the winner on held-out data. Evidence, not vibes.
Retrieve only the context that matters instead of stuffing full history into every prompt. In our benchmark, agents also finished with 23% fewer reasoning steps — and no measurable drop in answer quality.
Read the studyMemory with authorization built in: owners, roles, scoped API keys, and every retrieval logged. Answers stay grounded in your own sources; security teams keep control.
See the security comparisonStart with one agent and a folder of documents — the same memory layer scales to orchestrated agent teams.
From simple document Q&A to enterprise knowledge systems with semantic search and persistent context.
From basic conversational responses to agents with persistent memory and personalization.
From simple task automation to orchestrated multi-agent systems with shared memory and coordination.
One pipeline, any provider
The ability to swap providers, mix local and cloud infrastructure, and keep the retrieval pipeline consistent across all of them is genuinely impressive.
First-class support for the open model stack — vLLM, TEI, Ollama, OpenRouter — alongside the frontier APIs. see all integrations →
Two ways to run GoodMem — fully managed on GoodMem Cloud with the built-in Retrieval Optimizer, or self-hosted behind your own firewall. Run it on frontier APIs or entirely on open models, on hardware you control. Same API either way; your memory stays yours.