cognee + G.V() → classics like Moby-Dick become queryable memory graphs (NL or Cypher) you can see instantly. Big thanks to @gdotv.bsky.social. Read the full write-up from the below!

gdotv - the graph database IDE@gdotv.bsky.social · 12mo ago

New Tech Spotlight! In our latest article, we check out @cognee.bsky.social - memory for AI agents, replacing RAG with scalable and modular pipelines. Cognee lets you turn dense, complex data into a queryable knowledge graph backed by embeddings. Have a look: gdotv.com/blog/cognee-...

Memory Monday: Graph It Out! Who’s working with memory graphs this week? Share what you’re connecting - entities, intents, sessions - whatever! We love those node clusters

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...

Cognee - Context Engineering: Boost AI Memory for Reliable, Smart LLMs

Master context engineering and AI memory to craft personalized LLM outputs, reduce token costs, and future-proof your AI agents—read the full guide now!

dub.sh

We’ve been jamming on context engineering for weeks. Pulled everything we’ve seen—memory, RAG, graphs, cost—into one post. Give it a read 👉🏼 dub.sh/context_engi...

Cognee - Context Engineering: Boost AI Memory for Reliable, Smart LLMs

Master context engineering and AI memory to craft personalized LLM outputs, reduce token costs, and future-proof your AI agents—read the full guide now!

dub.sh

🧠 Launching AI Memory Monthly in a few hours! One ultra-skimmable email → fresh papers, real-world retrieval tricks & the spiciest AI memory debates. Hit the subscribe button now so Issue #1 lands in your inbox soon.

We’re debating everything from RAG pipelines to memory compression in a spicy SubReddit. Add your hot take! 🔥 Then swing by our Office Hours on Discord today (17:00 CET) to keep the convo rolling.

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...

Improve your AI infrastructure - AI memory engine

Cognee is an open source AI memory engine. Try it today to find hidden connections in your data and improve your AI infrastructure.

dub.sh

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 📚

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

Full write-up → www.cognee.ai/blog/fundame... If you’re exploring how to blend vectors and graphs for richer retrieval, we build exactly that at @cognee.bsky.social - DMs open for a chat!

Cognee - Vector Databases Explained: A Smarter Way to Search by Meaning

Learn vector databases, how vector stores like Pinecone power semantic search and AI applications by indexing embeddings. Maximize their benefits with cognee now!

cognee.ai

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?” 🙂

👉 Ready to deploy? ``` git clone <https://github.com/topoteretes/cognee.git> cd cognee/cognee-mcp brew install uv uv sync --dev --all-extras --reinstall source .venv/bin/activate ``` Full docs: docs.cognee.ai/how-to-guides/deployment/mcp

🚨 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. 👇

and more… Our latest post, “Graph Databases Explained,” walks through the trade-offs, query patterns, and performance internals in plain language. If “deeply connected data” describes your next project, it might save you a few JOIN headaches. Read here → www.cognee.ai/blog/fundame...

Cognee - Graph Databases Explained: A Better Way to Represent Connections

Discover how graph databases like Neo4j build knowledge graphs, fight fraud & boost recommendations. Master nodes & edges with cognee and supercharge your data!

cognee.ai

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:

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

Compare Small Ollama Models in Generating Knowledge Graphs Using cognee

YouTube video by cognee

youtu.be

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.

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?