Defeating Nondeterminism in LLM Inference (37 minute read)
Reproducibility is the bedrock of scientific progress, but it is remarkably difficult to get reproducible results from large language models. LLM APIs are not deterministic in practice, even when adjusting the temperature down to 0. Sampling isn't deterministic even when running inference on your own hardware with an OSS inference library. This article looks at the root causes of nondeterminism to give the community a solid understanding of how to resolve it in their reference systems.
Build Agents that never forget (12 minute read)
LLM agents fail without structured memory because stateless calls lose context, break multi-step tasks, and force repeated mistakes. Vector search alone cannot answer multi-hop questions, so Cognee combines relational, vector, and graph stores to preserve provenance, meaning, and relationships. The framework exposes four async calls to ingest, structure, refine, and retrieve memory, enabling agents to persist knowledge, link entities, and improve over time.