cognee

A lot of devs building with AI are hitting the same wall where the context just falls apart. We're excited to see more teams reaching for cognee as a solution. Here are some recent use cases we’ve seen: - A legal-tech demo grounded in a knowledge graph to extract and reason over complex documents. - A healthcare assistant that synthesizes pathology, scan reports, referrals, and clinical notes across systems and formats. - A feedback analysis engine that turns 10,000+ job reviews into searchable sentiment and emerging trends. - Internal-facing chatbots where employees can only query data they’re allowed to see. - An AI agentic platform where cognee helps continuously improve knowledge accuracy. - A language-learning tool that builds a personal vocab list from YouTube videos. If you’re building something with #cognee, #AIMemory, or want to learn more, share it in our Discord!

We’re bringing the best minds in AI together for an agent context panel at Redis Released: San Francisco.    Join Sam Crowder, Product Manager of LangGraph at LangChain; Rotem Weiss, Co-founder & CEO of Tavily; and Vasilije Markovic, Co-founder of Cognee for a session with Andrew Brookins, Principal Applied AI Engineer at Redis and the builder of the Redis Agent Memory Server. Don’t miss this discussion. Register here: https://xmrrwallet.com/cmx.plnkd.in/gJE7VbY2

AI Memory Insights — August 2025 issue is live! What’s inside: ✅ Context engineering, decoded. We distill the Reddit + Medium buzz into four field-tested rules every memory stack needs. ✅ Speed meets scale. See how AWS S3 Vectors + OpenSearch and Qdrant Edge keep hybrid search < 800 ms—and why Cognee’s new S3 backend matters for multi-node clusters. ✅ Newest research. HippoRAG 2, NeedleChain, Memory-as-a-Service—you’ll get the TL;DR and links. ✅ Community pulse. r/AIMemory is shifting from prompt packs to schema design; we highlight the best comments and resources. ✅ One-metric challenge. Tell us the single KPI that proves your context stack beats vanilla RAG—top answer featured next month. 👉 Skim it in 5 minutes and subscribe (link: in the comments)

Yesterday we merged PR # 1167, contributed by Vinay, adding dynamic resizing to cognee’s graph visualizer. A concise, well-tested patch that immediately improves workflows for everyone. This is exactly why we love open source: iterative wins delivered by passionate engineers worldwide. To every maintainer, reviewer, and first-time contributor—thank you for making cognee better than we could ever build alone. To be part of the story, check our open issues, ship your first contribution and join our community. We review fast, we give thoughtful feedback, and we love spotlighting new contributors. Find the links below.

RAG can fish out a quote. We build the whole mind-map. Spin it up with `pip install cognee` and a few lines of Python; the graph viz comes for free. Full write-up and code snippets are below.

Context engineering—where the gains are happening After a month of debates in Reddit threads, Discords, and Slacks we captured practices teams keep asking for: 🔹Long- vs short-term memory 🔹Graph-plus-vector retrieval 🔹Token budgeting that doesn’t hurt quality If you’re already experimenting, this guide will help your workflow get better. The link is in the comments 👇🏼

Cogwit is LIVE! cognee gets a platform offering: - Managed graph + vector memory API - Early-bird price $25/mo during beta - No setup & quick-start = instant results Early adopters invited. Let’s give AI a real memory 🧠

Did you know cognee already plugs into 14 different databases? Relational, Vector, Graph and hybrids in between—are all first-class citizens in our stack. 🔍 Where are you keeping your data right now? Drop your stack below 👇 benchmarks, migration wins, hybrid surprises welcome. Let’s compare notes and level up our memory architectures together!

cognee × Redis: A blazing fast memory stack for AI agents 🌪️ Cognee now speaks Redis! Why Redis? - Millisecond-level retrieval for real-time agents - Built-in hybrid & full-text search, short-term context - Declarative schemas with RedisVL keep setup friction near zero Same ECL pipeline, broader choice of backends. Dive deeper from the link below 👇🏼

Build faster AI memory... by combining cognee's innovative AI memory solutions with Redis's ultra-fast vector database and semantic caching, developers can now build smarter, more responsive AI agents—at scale and with lower costs. Check it out! https://xmrrwallet.com/cmx.plnkd.in/gs79qByw Tyler Hutcherson Kyle Banker Jeremy Plichta Manvinder Singh Mike Moss Megan Boone Katie Dunn Jim Allen Wallace #memory #agents #AI