Here’s the distilled version of what everyone has been talking about “context engineering”. Straight talk on memory layers, GraphRAG and why prompt hacks alone won’t cut it. Give it a read 👉🏼 dub.sh/context_engi...

Hello, world—with context! 🧠 🚀 We are launching “Insights into AI Memory”—a monthly signal on the tech, tools & people teaching AI to remember. Grab the initial post & subscribe 👉 aimemory.substack.com

We’re days away from opening the cognee SaaS beta 🚀 where you’ll bring your knowledge graphs & LLM workflows to life without the infra pain. 🔍 Built for everyone who cares about clean data, speed, and reproducibility. Want in on day 1? Join the waitlist → dub.sh/beta-saas-co...

Tired of asking brilliant questions and getting “meh” answers from your LLM? We just shipped “The Art of Intelligent Retrieval”—a deep dive into how Cognee layers semantic search, vector DBs & knowledge-graph magic across specialized retrievers (RAG, Cypher, CoT, more)

Why file-based AI Memory will power next-gen AI apps? We break down how a simple folder in the cloud becomes the semantic backbone for agents & copilots. Let’s unpack it 🧵👇

Woke up to 🚀 cognee hitting 5000 stars! Thank you for the trust, feedback and code you pour in. Let’s keep building 🛠️

find us on @github.com trending!

Yesterday, we released our paper, "Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoning" We have developed a new tool to enable AI memory optimization that considerably improve AI memory accuracy for AI Apps and Agents. Let’s dive into the details of our work 📚

We're ramping up our r/AIMemory channel for broader discussions on AI Memory and to share in the conversation outside of cognee. We'd love to see you share what you're working on, your thoughts and questions on AI memory, and any resources that would benefit the broader community.

Join the conversation at r/AIMemory. dub.sh/ai-memory

We just dropped a follow-up to last week’s graph-DB explainer—this one dives into vector databases and why they’re the workhorse behind semantic search. Quick recap: Graph DB → “How are things connected?” Vector DB → “What *feels* like this thing?” 🙂

🚨 4 Big Updates to cognee MCP Server (and what devs need to know): If you're building with LLMs, graphs, or agentic applications - keep reading. 👇

Relational tables thrive on set operations. Document stores shine with nested data. But when you need to ask “How are things connected?” you step into graph territory.

Got featured in @neo4j.com ’s weekly wrap-up! From the links below, you can find us chatting on GraphRAG + cognitive science (a small demo included) + fresh Neo4j news (Aura Analytics, NODES CFP, MCP hacks). Don’t miss it out.

🧵 Insights from Data Council 2025, viewed through my lens:

2k Github stars! And, even more important, telemetry shows people running cognee in production!

Curious if small Ollama-hosted LLMs (e.g., Phi-4, Gemma-3) can reliably create knowledge graphs? Our 3-minute video answers exactly that: - small local model limitations - tips (schema optimization, YAML/HTML requests) - need for larger models (Llama3.3, 14B) watch here: youtu.be/P2ZaSnnl7z0

We benchmarked cognee, Mem0 and Zep/Graphiti using HotPotQA! Cognee topped the charts, boosted further by Dreamify, our optimized pipeline 🚀. Graphiti shared their scores with us and we'll verify them soon! Dive in the details 👇🏼

Join us at @pyconde.bsky.social in Darmstadt - this is your opportunity to dive into open-source, network with passionate devs, and shape the future of AI and Python ecosystems! We are sprinting with @cognee.bsky.social alongside 9 other awesome open-source projects.

TLDR; we launched on ProductHunt! After months packed with community-driven features our AI memory tool @cognee.bsky.social has evolved to new heights. Finally AI agents meet the memory they deserve - structured, accurate, and reliable - in 5 lines of code.

First time presenting our 89,4% accuracy on HotPot QA at a meetup and it was great. We showed up as almost the whole @cognee.bsky.social team, answered great questions, met amazing people. For the evals, go and check our repo 👇

Exciting news friends! Our latest @cognee.bsky.social update is live, featuring: • Ontology support • Direct relational DB imports • Dreamify, our shiny new hyperparameter tuning system • Big performance boosts Plus, Pinki our dog has finally conquered the indoor puke challenge.

Give LLM your rules? Your definition of a session, GBV or the details of your accounting practices. Yes. With ontologies! Connecting related concepts across different documents is often necessary but traditional search methods can’t help since they treat each paper as an isolated document.

Short read, big insights 👇 We took knowledge graphs up a notch with formal ontologies. You’ll find how we merge domain expertise + graph structures for more detailed insights at @cognee.bsky.social in the below blog. Check it out.

How do you run your own local AI stack? In 4 minutes, you can setup Ollama with @cognee.bsky.social to go local with Phi-4 and Mistral Going local mostly means: - Better privacy - Reduced costs - More control & experimentation But.. beware:

🧵 7 Common Misconceptions About Knowledge Graphs (and what devs should actually know) If you’re working with data, LLMs, or building anything agentic —you’ll want to read this.

Want your AI to catch sneaky data errors and go beyond basic vector search? We just released a blog on using knowledge graphs for smarter QA. Check it out! ps: a sneak peek from the blog

What’s the deal with Knowledge Graphs? TL;DR: They don’t just store facts. They connect them. They power AI memory, enable deep reasoning, and help LLMs process data with context - but what are they, really?

Everyone’s hyped about MCP now, but cognee was already there. It hooks your LLM into your data - text, code, you name it - so you can cut hallucinations and keep context. I use it with Anthropic’s Claude Desktop, plus dev tools like Cursor, Cline, Continue and the difference is huge.

What’s the deal with Knowledge Graphs? TL;DR: They don’t just store facts and relations. They power AI memory, enable deep reasoning, and help LLMs process data with context-but what are they, really? Checkout our blog "The Building Blocks of Knowledge Graphs: Cognee's Approach" below

In San Francisco for a month. So much hustle. If anyone has suggestions on where to go, happy to hear

Let's turn your data into graph/vector representation! We will cognify it. Inspired by #graphrag and #rag, we have a better approach! Why? - Increase retrieval accuracy by 30% (measured on hotpotqa) - Get the data you need - Reduce the number of LLM calls - Extract the data from your own data

Here is our design of the evaluation system for #graphrag and #aimemory system that we built It will be merged today Curious how others approach #llm evals

RAG evals shouldn't be a guessing game. With DeepEval + cognee, you get clear, structured insights into how your retrievals are performing. No more blind spots.

Just dropped our second video on: - understanding graphs & knowledge graphs - using cognee as a unified layer between your data & AI - building knowledge graphs with it - deploying with Modal Check it out: youtu.be/86SWVdI5K0Y

MCP is the “USB-C port for AI apps” as highlighted by @tobilutke.bsky.social, enabling seamless integration of tools and systems. At @cognee.bsky.social, we've embraced this by bringing memory to the mix.

Attention Developers & Graph Enthusiasts! I have exciting news: We've integrated Graphiti's temporal graph capabilities into cognee 🎉

🔍 Smarter RAG, simplified - the developer-first way. Cognee integrates graph and vector stores for better data workflows - and when we needed speed and simplicity, LanceDB delivered: ✅ Runs locally = isolated, repeatable tests. ✅ Zero infrastructure headaches ✅ Apache Arrow-powered speed.

📢 Attention, Open Source Enthusiasts! @cognee.bsky.social has landed on Docker Hub! 🛳️ Simplify your AI memory with cognee docker image. Run -> docker pull cognee:latest. ✨ Build smarter, context-aware AI. ⚡ Scale effortlessly - just like the open-source way.

How can LLMs become smarter and handle more complex interactions? I had a chat with Tobias Macey about it. Tobias, the host of the AI Engineering Podcast and a veteran in data and AI systems.

🚀 Open Source Synergy Calling all OSS developers! I am happy to announce a seamless integration between cognee and Milvus: 🔗 Transform raw data into structured vectors and knowledge graphs. 📊 Deliver accurate, context-aware responses. ⚡ Scale effortlessly with fully open-source pipelines.

Regular RAG hates David Beckham. Here’s why (step-by-step demo included) 👇 Traditional RAG systems work with fragments of data - retrieving local context but missing broader connections. Picture this: Victoria Beckham is reminiscing about her working-class upbringing.

What’s your go-to approach? I’m here to share, learn and maybe make a few friends along the way. 🌱

So... this is my second post here (hi, again!), and I figured I’d talk about something I’ve been working on: AI memory. Here’s a question for anyone building with LLMs: have you ever worked with RAG and felt like you were swimming in technical debt?

entering bluesky with this amazing photo just dropped off some cognee merch for the dlthub crew let's make data processing great again! @matthausk.bsky.social @datateam.bsky.social