🗽In NYC tonight? @DylanCouzon & @qdrant_engine are hosting a debate night, and @deepset_ai is teaming up Hear some hot AI takes, get a chance to win prizes, and learn about @Haystack_AI and @qdrant_engine along the way! luma.com/nyc-meetup-qdrant-0…

↔️ MCP works in both directions with @Haystack_AI Your Haystack agent can consume tools from external MCP servers. And your Haystack pipelines/agents can be exposed as MCP tools for any MCP-compatible assistant. I wrote a guide covering three approaches: 🔹 Haystack as an MCP client — use MCPTool, MCPToolset, or SearchableToolset to give your agent access to external tools 🔹 Haystack as an MCP server with Hayhooks — self-host your pipelines and agents and expose them to Claude, ChatGPT, Cursor, or any MCP-compatible client 🔹 Haystack as a managed MCP server on Haystack Enterprise Platform — production-grade MCP support with access control and observability built in. This is also how we built the Haystack Docs MCP Server at @deepset_ai Read the full article here 👇 haystack.deepset.ai/blog/mcp…

Can't wait to be back in Bologna for @pyconit this week! I'm talking about why agents fail and how to fix them, with a live demo of our itinerary agent built using context-engineering patterns and @Haystack_AI! See you all there 🙌

🥳 My talk from @aiDotEngineer Europe is live on YT!! this was my first ever talk on sovereignty so I'm quite excited to share 💗 I broke down AI sovereignty into 4 pillars to make it clearer and I shared a sovereign @Haystack_AI architecture + a checklist for AI systems

WHAT AN EVENT!! 🎉 Last week we hosted an unconference as @deepset_ai with @NVIDIA at @balderton office in London. 🎙️ We kicked off with two short talks. @LukawskiKacper demoed a multi-agentic post-event report creator built with @Haystack_AI and Nemotron 3 Super, @KarinSevegnani walked us through Nemotron 3's performance and benchmarks. 💬 Then we split into three discussion groups and I led a session on memory and long running agents. One of the most interesting conversations I've had in a while. We started with memory and second brain concepts. People shared how they're building personal agents by transcribing YouTube videos and creating knowledge bases following @karpathy's wiki idea. We talked RAG vs filesystems, MCPs vs skills, AI sovereignty and security. At the end of the day, you don't want to lock your whole identity to an API, privacy and freedom of choice matter. Thank you to every attendee for your energy, openness, and contributions. I'm already looking forward to coming back to London 💙 See you at Unconference #4 👀

Looking forward to @aiDotEngineer next week! 🇪🇺 AI Engineer Paris was one of the best conferences I attended last year, and I'm so excited to be speaking at AI Engineer Europe this year alongside so many brilliant minds. I'll be talking about something that doesn't get enough attention in engineering conversations: sovereignty constraints for technical folks. I'll share real engineering trade-offs and what building under these constraints actually looks like in practice, drawing from my work at @deepset_ai. Hope to see you there, come say hi if you're around! 👋

📚 It had been a while since we published a technical blog post, and Gemini Embedding 2 from @GoogleDeepMind team felt like a good reason to write one together with @theanakin87 💙 It's Google's first fully multimodal embedding model, meaning you can embed text, images, audio, video, and PDFs into a single shared vector space. What’s exciting about multimodal embedding models is that they enable cross-modal retrieval. For example, you can search a product catalog with a text query like “grey running shoes with white soles” 👟, or upload a photo of a monument to retrieve relevant articles describing it 🗽 In the post, we show how to use Gemini Embedding 2 with @Haystack_AI for both text and multimodal data, supported from day 0 🎉 Blog: haystack.deepset.ai/blog/mul…

“Open source” doesn’t mean much if you can’t actually understand or extend the code. That’s one of the questions I’d ask before choosing any orchestration framework…and it’s not the only one. Here are some of the things I’d consider TODAY: - Is it *truly* open source? Not just public code, but understandable and extensible? - Is the documentation actively maintained and reliable? - Do the core concepts feel intuitive, or do you have to fight the abstractions? - Is there a clear path from “this works” to production and scaling? Those things don’t matter much when you’re experimenting, but they matter a lot once the system starts to grow. To make the evaluation easier, we wrote a guide that maps the LangChain/LangGraph ecosystem to the @Haystack_AI ecosystem. It’s a concept-by-concept breakdown, plus why Haystack might be the right fit for you. If you’re choosing an orchestration framework (or thinking about switching), this might save you a few days 👇 🔗 docs.haystack.deepset.ai/doc… I’d love to hear how you think about choosing a framework and what matters most for you 🙌 P.S. The guide is open for contributions. If something is missing or you’d add a different perspective, you’re welcome to help us improve it.