Cognee – Open-Source AI Memory Layer That Remembers Context
Hey HN! We're Vasilije, Laszlo and Lazar, the authors of a new paper and part of https://www.cognee.ai. cognee let’s you build memory layers for AI applications and agents, allowing them to personalize results, connect various data sources, and add custom rules. This enables AI apps to deliver increasingly accurate responses, we reached almost 90% on standard industry benchmarks as you can see here https://github.com/topoteretes/cognee/tree/main/evals and our paper can be accessed at: https://arxiv.org/abs/2505.24478 and collab…
What it does
In the maker’s words, at launch
Hey HN! We're Vasilije, Laszlo and Lazar, the authors of a new paper and part of https://www.cognee.ai. cognee let’s you build memory layers for AI applications and agents, allowing them to personalize results, connect various data sources, and add custom rules. This enables AI apps to deliver increasingly accurate responses, we reached almost 90% on standard industry benchmarks as you can see here https://github.com/topoteretes/cognee/tree/main/evals and our paper can be accessed at: https://arxiv.org/abs/2505.24478 and collab is in the repository LLMs don't remember context well—they can’t ingest your data and keep it in memory. This limitation leads to lacking interactions, a lack of accuracy, and the inability to connect your data sources cheaply because developers must include long, unmanaged context in every prompt. When we were building RAGs we saw that we can’t find the data we need and that there are too many knobs to turn in RAG frameworks. We had to tweak many parameters and also could not specify the rules we wanted the data to follow. No ontologies, no rulesets, no state or good data engineering practices and a lot of manual work. That’s why cognee cognee builds memory that combines graph, vector, and relational stores. Here is how it works: Adding data: When you use cognee with your AI App, it can take in any message, string, S3 bucket or even a relational database and automatically ingest it Managing information: cognee sorts this information into semantic graph: - It extracts entities and connections between things. - It embeds the data in the vector store, It enriches data with custom ontologies you provide, that help ground the graphs and make them more reliable. - The overall information is stored in many layers of a graph and vector store that allows for finding similar information later using a variety of types of searches. Retrieving data: When given an input query, cognee searches for and retrieves related stored information by leveraging a combination of graph traversal techniques, vector similarity, and COT techniques. It can use the internal benchmarking system to make sure that your pipelines are returning only accurate data when you need it. --- cognee introduces a self-improving group of memory layers that cover various topics, data sources and can be customized. This reduces the need to build everything from scratch, and you can use our primitives to get started and move more quickly. On the other hand, you can do everything yourself, from start to end. We’ve designed the system to be modular and extensible. We’ve open-sourced cognee —specifically the framework and various vector and graph database adapters, as well as our default data pipeline, cognify—under the Apache 2.0 license. This includes the ability to add, cognify, and retrieve data within your AI applications and also extend it with custom components that are just pure Python. However, many keep features that are optimized for production use, as a part of our paid platform. We release these functionalities too, including in the last few weeks permission management + distributed pipelines(dev). These are a part of our open-source package and are available to those who in production environments need things like rate limiting and credential management. We will release a paid offering for developers to get easy API access to our platform. Our automation tooling allows us to optimize the pipelines and find the best combination of parameters that answer questions our stakeholders have. We’d love to hear what you think! Please feel free to try our demo, check out the code, read the research paper, and share thoughts or suggestions with us. Your feedback will help shape where we take cognee from here!
Does the same job
all alternatives →- CTCognee – Turn RAG and GraphRAG into custom dynamic semantic memory2025 · github.com · ▲6
Hey there HN! We’re Vasilije, Boris, and Laszlo, and we’re excited to introduce cognee, an open-source Python library that approaches building evolving semantic memory using knowledge graphs + data pipelines Before we built cognee, Vasilije(B Economics and Clinical Psychology) worked at a few unicorns (Omio, Zalando, Taxfix), while Boris managed large-scale applications in production at Pera and StuDocu. Laszlo joined after getting his PhD in Graph Theory at the University of Szeged. Using LLMs to connect to large datasets (RAG) has been popularized and has shown great promise.…
More ai this month
the category →
I trained a 125M-parameter transformer to autocomplete piano performances in real time (~108 notes/sec on an iPhone 15). The idea is basically GitHub Copilot or Tabnine, except instead of prompting it with code, you prompt it by playing a few notes on a MIDI piano. The model then continues what you played, entirely on-device. The app is free if anyone wants to try it. Happy to answer questions about the model, training, Core ML, or the many things that didn't work.
AI · 17d ago · simedw.com
Astute▲585Automate your B2B brand going viral, with new media creators
AI · 18d ago · company-app.joinastute.com
Hey HN, Henry from Cactus here! We previously released Cactus Needle, a 14MB agentic LLM for tool call, device use, and structured extraction for phones, wearables, smart homes, small robots and microcontrollers. We got really great feedback here, and have now incorporated the suggestions to release Needle 2. The whole model is a single 14MB binary that runs a full session in 28MB of RAM; 45m parameters at 2bit compression. Needle hits 500 tokens/sec decode speed on a Raspberry Pi 5, sits between 400-1,500 tokens/sec on VR devices like Meta Quest 3S and Apple Vision Pro, and ranges…
AI · 26d ago · cactuscompute.com
Launched alongside, June 2025
the whole month →- IM
Hey HN! Pilots everywhere are required to keep a logbook of all their flying hours, aircraft, airports, and so on. Since I track everything digitally (some people still just use paper logbooks!), I put together some data visualizations and a few 3D globes to show my flying history. This globe is probably my favourite so far: https://jameshard.ing/pilot/globes/all If you’ve got ideas for other graphs or ways to show this kind of data, I’d love to hear them!
Life & fun · 2025 · jameshard.ing
Stunning presentations with AI. No design skills required.
AI · 2025 · chroniclehq.com
Build native mobile apps for iOS and Android without code.
Dev tools · 2025 · bble.io
All your connections, across all your socials.
AI · 2025 · pally.com
- WC
I was the main contributor to workout.lol, an open-source fitness app to easily build a workout routine. The project had traction (1.4k GitHub stars, 95 forks, ~20K visits/month), but was eventually sold due to video licensing hurdles. The new owner stopped maintaining it, and the repo went abandoned. Over the next 9 months, I sent 15 emails to try to save it : no replies. Feature requests & issues were ignored. The community was left with a "broken" tool let's say. I couldn't just let it die So I built the new version from scratch with the same open-source spirit, but a better…
Dev tools · 2025 · github.com