cognee is an open-source semantic memory layer for AI agents, built on vector and graph databases. It constructs knowledge graphs from retrieved data, enabling AI apps and agents to deliver accurate, context-aware responses.
We built cognee to give AI agents a better memory.
Today, most AI assistants struggle to recall information beyond simple text snippets, which can lead to incorrect or vague answers. We felt that a more structured memory was needed to truly unlock context-aware intelligence.
We give you 90% accuracy out of the box
The best part is that you can do it in just 5 lines of code!
If you're curious to test it out firsthand, try cognee and give us a ⭐ on GitHub! We’d also love to chat about all things AI memory in our lively Discord – join in to:
Share feedback
Discuss features you want to see next
Learn from our awesome community (+300 members)
If you feel inspired to help shape cognee’s future and build the best AI memory layer out there while sharpening your skills, contribute to our open-source codebase. We have plenty of open issues you can start with!
We built cognee to give AI agents a better memory. Your AI assistants struggle to recall information? Giving you incorrect or vague answers? We know what you need to truly unlock context-aware intelligence.
The best part is: You can do it in 5 lines of code! If you are curious to test it out firsthand, try cognee quickly and give us a GitHub star! We’d also love to chat about all things AI memory in our Discord - join in to share feedback, discuss features you want to see next, or learn from our awesome community +300 members.
Feeling inspired to shape cognee’s future and help us build the best AI memory out there while sharpening your skills? Contribute to our open-source codebase! We have plenty of open issues that you can pick and start with! If you’re curious how cognee might fit into your business, just book a call for a 1:1 with Vasilije, cognee's co-founder.
How cognee works:
Instead of just embedding documents and fetching them (like typical RAG systems), cognee combines vector search with a semantic knowledge graph. When your AI agent retrieves data, cognee builds a knowledge graph on the fly out of those pieces – utilizing ontologies, connecting the dots between related facts, concepts, and context. This graph-based approach lets the AI understand relationships in your data (like how concept A relates to B), so it can reason more accurately and reduce hallucinations. The memory is stateful, meaning your agent can accumulate and reference knowledge over time, across sessions.
How you can add cognee to your existing stack: cognee is open-source and designed to plug into your existing stack. It works alongside popular vector databases (Weaviate, Qdrant, LanceDB, etc.) and graph databases (like Neo4j, kuzu) – you can keep your current data infrastructure. It also plays nicely with frameworks like LangChain or LlamaIndex for ingestion and chunking. In short, cognee isn’t another all-in-one platform; it’s a focused memory layer that integrates with your data pipeline to make your LLM applications smarter.
We’re excited to see what you build with cognee! Imagine support chatbots that truly remember past conversations, financial research assistants that link insights across reports, or internal search tools that actually understand how disparate documents relate to each other. If you have any questions, ideas, or just want to chat about AI memory (our favorite topic!), we’d love to hear from you. Thank you for giving cognee a look!
Sample Use Cases:
Intelligent Chatbots & Virtual Assistants: cognee can power chatbots that remember previous conversations and user details. For example, a customer support bot could reference past support tickets and product info to provide fast, context-rich answers without repeating questions.
Financial Research & Analysis: In finance, cognee helps AI assistants link insights across documents and data sources. Imagine an analyst’s AI that correlates financial reports, news articles, and market data via a knowledge graph – the assistant can answer complex questions (like risk factors or trend explanations) with evidence and accuracy.
Knowledge Search: cognee enables an internal Q&A system that understands your company’s knowledge. It can connect data from wikis, PDFs, emails, and databases into a semantic graph, so employees can ask something like “How will project X impact department Y?” and get a comprehensive, accurate answer drawing from all relevant internal sources.
and many more ♾️
Don't forget to check out our website and blog where you can find many insights about AI memory and cognee's approach. Try cognee yourself from our GitHub repo, give us a ⭐ , and join our Discord community. We look forward to hearing what you are building and we are here to help!
Congratulations on introducing Cognee! It's impressive to see an open-source solution enhancing AI agents' contextual awareness. What measures are you implementing to ensure the accuracy and relevance of the knowledge graphs generated from retrieved data?
This is awesome, love it! Congrats on the launch folks. As early tester I highly recommend to give cognee a shot to anyone who wanna build reliable LLM apps
Congrats on launching Cognee! I love this poduct! Your approach to enhancing AI memory is so impressive, especially with its focus on knowledge graphs.
How do you ensure that the context-aware responses remain accurate as the knowledge graph evolves over time? :)
Congrats on the launch! We love that you've integrated Zep'sGraphiti graph framework into Cognee! It would be great to see the evals rerun with our recommended implementation of Graphiti!
I like how there are different SearchTypes, for insights, information, etc…
How do you make the search user-specific? I read on website you personalize to user based on user features but didn’t find it in the docs
Congrats on the launch team. Memory is probably the most important component of modern AI systems and what you’re doing with knowledge graphs is a very smart way to solve for it!
Congrats on the launch! 🔥 Love the focus on structured memory — feels like a crucial piece for taking AI agents to the next level. Excited to see where cognee goes next! 🚀
Congrats on the launch team. Memory seems to be a big advancement in LLM development. Look forward to trying it out.
I really like the idea of turning meetings into searchable knowledge hubs. 🙏
Curious how Cognee handles sensitive information — is all the data processed locally, or does it run through external servers?
This looks exciting, congrats on the launch! Do you have any links/docs to understand the graph generation process and how that fits into LLM workflows? I'd love to gain a deeper understanding of it
This is amazing. How does this compare against something like Mem0? Hope I'm not mixing things up too much hahaha
Wow! This looks promising! Congrats on the launch!
