Build Smarter Agents Your Way

Ship fast on our serverless cloud or deploy privately on your own infrastructure. Same features, flexible control.

Start in the cloud

Foundations

Scalable

Grows with your data. Autoscaling compute and distributed graphs can handle any workload.

Performant

Production-ready and built to support demanding workloads. Tuned pipelines and caching deliver millisecond responses.

Secure

Fully GDPR-compliant. Data is encrypted at rest and in transit. Made for air-gapped enterprise deployment.

Cost at scale

Query cost that stays flat

10 questions

$30.37

$42.67

20 questions

$30.45

$85.35

50 questions

$30.66

$213.37

cogneeGPT-5.5

Corpus size: 853,439 tokens. GPT-5.5 modeled at $5.00 / 1M input and $30.00 / 1M output tokens; full-context and post-ingestion query totals are priced as input tokens.

Token corpus, ingested once0

Cognee cost — flat at any volume0

of GPT-5.5 cost at 50 questions0

Cheaper than GPT-5.5 at 50 questions0

Cognee SDK

A memory-native API for agents

Cognee SDKHTTPMCPCustom graph models

Four verbs — remember, recall, forget, improve — are the product surface. The same memory API across the Cognee SDK, HTTP, and MCP, replacing lower-level add/cognify/search framing.

# The main ingestion entry point — stores information
# in memory with a single API call.
cognee.remember(
    ,
    ,
    ,
    ,
    
)

data: str | DataItem | list

What to remember — raw text, a file path, an HTTP/HTTPS or S3 URL, or DataItem object(s).

Hover or click a parameter

One knowledge graph, every MCP-compatible agent.

Zed

Qwen Code

Kiro

Roo Code

Kilo Code

Goose

Self-improving

Memory that improves with use.

BeforeAfter

MemoryImprovements

Decorator

One decorator. Graph memory and session memory composed.

Wrap 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.

agent.py

@cognee.agent_memory
async def agent(query: str):
    # retrieval-before-execution, memory injected into the LLM call,
    # and a bounded trace persisted afterwards — automatically.
    ...

Sessions

Sessions with a full lifecycle

Read the docs See the research and evaluation results

User request

Investigate the latency spike.

prompt

Claudesynced

Codexsynced

OpenClawsession active

$ openclaw session start
→ connecting to Cognee Cloud
→ loading company brain…
✓ context loaded
› working in session…
✓ session complete
→ improving memory▋

company brain

improve memory

Improve memory

Company brain

Cognee Cloud

26 nodes

No graph database

Read the docs

Custom deployment