The Cognee Blog

What has to run on your own hardware for agent memory to count as local: four levels of locality, where cloud calls hide in extraction, embeddings, and reranking, how much hardware each stage needs, and a disconnect test for fully offline memory.

Compare LangMem, Mem0, cognee, Zep, and Letta by how much of the stack each AI memory tool takes over — from memory primitives over your own store to managed temporal graphs and memory built into the agent runtime.

Compare how Mem0, Zep, cognee, Letta, and LangMem let you evaluate AI memory — open benchmark harnesses, retrieval-versus-answer scoring, published results, and what you still have to test yourself.

Compare Graphiti, Mem0, cognee, Basic Memory, and Letta for small models and local agents — which AI memory tools need generative inference, what runs offline, and how much work lands on the agent’s own model.

Compare DeepEval, Ragas, Promptfoo, Inspect AI, and Opik across output, retrieval, trajectory, and task-outcome evaluation layers — what each tool actually tests and how far it goes beyond scoring a single model response.

Compare HelixDB, turbopuffer, LanceDB, SurrealDB, and Turso across architecture, retrieval, and primary AI use — from graph-and-vector engines to object-storage-backed search and a database provisioned per agent.

Keenable returns clean Markdown for any URL, and cognee turns it into persistent, graph-backed memory an agent can recall later — how the fetch backend, config, and pipeline fit together.

Compare Fastino, distil labs, Liquid AI, Arcee AI, and Mistral AI across model focus, deployment, and use case — from structured extraction and distilled students to on-device inference, sparse MoE, and general-purpose compact generation.

A breakdown of Anthropic API pricing for 2026: per-model Claude token rates, prompt caching and Batch API discounts, worked cost examples, and how much a memory layer can cut off the bill.