Ship fast on our serverless cloud or deploy privately on your own infrastructure. Same features, flexible control.
Grows with your data. Autoscaling compute and distributed graphs can handle any workload.
Production-ready and built to support demanding workloads. Tuned pipelines and caching deliver millisecond responses.
Fully GDPR-compliant. Data is encrypted at rest and in transit. Made for air-gapped enterprise deployment.
Four verbs — remember, recall, forget, improve — are the product surface. The same memory API across the Python SDK, HTTP, and MCP, replacing lower-level add/cognify/search framing.
# Write content into memory.# Scope to a dataset, weight by importance at ingest,# and ground extraction in your own graph model.
cognee.remember(
, , , ,)What to remember. Text, a file path, or a structured payload.
Zed
Qwen Code
Kiro
Roo Code
Kilo Code
Goose
ZedQwen CodeKiroRoo CodeKilo CodeGooseWrap an async agent entrypoint with @cognee.agent_memory and Cognee composes graph memory and session memory, then turns the agent’s own execution history into queryable memory.
@cognee.agent_memory
async def agent(query: str):
# retrieval-before-execution, memory injected into the LLM call,
# and a bounded trace persisted afterwards — automatically.
...
Cognee excels at delivering answers that feel human and contextually right. It combines precision, reasoning depth, and consistency across complex multi-hop questions.
Run cognee without operating the infrastructure yourself.
An embedded AI memory engine that gives any agent persistent, queryable knowledge-graph memory. One binary, zero infrastructure. Vector store, graph store, relational metadata, and local embeddings all run in-process.
The Python stack it replaces — interpreter, imports, and connections to Postgres, Neo4j, and a vector DB — takes seconds to become query-ready and needs those services already running. Cognee-Rust pays once at startup.
Hosted memory APIs add network latency on every call. Cognee-Rust runs in-process, so after the cold start each query stays local.
~350 ms sits under the threshold where you can spin up memory per request in a Lambda or Workers-style environment: ephemeral compute with persistent memory.
Serverless functions, CLI tools, mobile apps, and edge devices can now carry real knowledge-graph memory.
Memory in three lines, typed SDKs, no vector DB to deploy.
~350 ms cold start — memory that fits inside a single function invocation.
The only AI memory engine that runs on-device.
Your agents’ memory never leaves your perimeter.
Keep your structure fresh. Cognee continuously updates your ontologies as data changes. No manual rebuilds, no stale taxonomies. Your system stays aligned and ready for new insights.
Cognee is deployed on your own systems. You have complete control over your data. Meaning less chance of external breaches and full regulatory compliance.
“Cognee and the FDE team have been terrific for us. We launched the first memory system within 30 days. Our teacher panel gave it overwhelmingly positive feedback. It opened our eyes to what’s possible.”
“We tried understanding the proximity and relationships of our users before, but SQLs got out of hand and embeddings were too sparse. With cognee, we managed to get a POC done in 2 days on 40,000 students from Bremen.”
“SlideSpeak customers create slides from scratch over and over again. With cognee, we added memory, so users can create better slides from shared context and improve those slides over time.”
“Cognee helped us enrich the data for thousands of our customers and provide them with personalized support better suited to their needs. The cognee team built and deployed the entire solution within a month.”
“The team helped me build a custom knowledge base of building codes and regulations that I could upload, query, and keep updated as regulations changed.”
“Cognee’s design allowed our team to implement AI memory capabilities on-prem without worrying about graph or vector database configuration. The accuracy of our information retrieval has significantly increased.”
