Open-source AI orchestration framework by @deepset_ai. Build context-engineered agents & RAG systems in Python. Discord for support → discord.gg/AH8a5Tm8vb

Haystack 3.0 is here 🚀 Agents move to the center of the framework: 🤖 Pre-built agents: deep research + advanced RAG, ready out of the box 🪝 Hooks to control the agent loop 🧰 First-class skills with progressive disclosure 📉 A leaner core: we deleted 3 lines for every 1 we added And this is just Day 1. New drops every day this week at 3 PM CEST. Full announcement 👇 haystack.deepset.ai/blog/hay…
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Haystack 3.2 is here 🚀 This release keeps agents lean and in control as conversations grow. Three highlights: 🧵 SummarizationCompactor for smarter context compaction Pair SummarizationCompactor with CompactionHook (shipped in v3.1) to progressively summarize long-running Agent conversations. 💰 TokenBudgetHook caps what a run can spend A ready-made hook that ends the run at "token_budget_exceeded" once you hit your token ceiling through the stop_run hook point. 🛠️ Faster Pipeline building New add_components() and connect_many() let you add and wire up multiple components in one call. 💙 70 contributors made this release happen; huge thanks to everyone! 🔗 Full release notes: haystack.deepset.ai/release-…
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Full-text search without semantic understanding leaves gaps. Dense vector search without keyword matching misses precision. @ApacheSolr is now available as a Document Store and retriever in Haystack. Use SolrDocumentStore with SolrHybridRetriever to combine BM25 full-text search and dense vectors in one pipeline - getting both lexical and semantic matching without extra infrastructure. Solr has been a trusted open-source search engine for over a decade, and its hybrid retrieval approach fits naturally into Haystack pipelines whether you're prototyping locally or scaling to production. 🐍 pip install solr-haystack 🔗 haystack.deepset.ai/integrat…
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📣 We're hosting our next unconference in Berlin with @prior_labs, exploring open-weight models and open-source agents! Come join us if you'd like to discuss: → Structured data in agentic workflows → When agents should hand off to TabPFN for prediction → Benchmarking tabular models vs. LLMs → Why orchestration matters for production agents → What owning your stack end-to-end actually requires → Open-weight vs. closed models in practice Or anything else that's been challenging you lately! 📍 @deepset_ai HQ, Berlin 🗓️ September 24th, 6PM-9PM Register: luma.com/haystack-priorlabs
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If you are a @mariadb user, you can now store your embeddings right alongside your documents and metadata in the same system - no need for a separate vector database. MariaDBDocumentStore and MariaDBEmbeddingRetriever let you build semantic search pipelines that keep all your data unified and easy to manage. Embed your content, persist vectors in the same MariaDB instance where your source data lives, and retrieve similar results in seconds. Everything stays in one place. 🐍 pip install mariadb-haystack 🔗 haystack.deepset.ai/integrat…
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Haystack retweeted
🗽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…
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Haystack 3.1 is here 🚀 This release is all about smarter context management with token accounting, context compaction & precision. Three highlights: 🪝 Context compaction for agents New CompactionHook automatically shortens long-running conversations before they blow through the context window. 🪙 New token counters for estimating usage Three new counters, from a zero-dependency approximation to an exact, OpenAI API-backed count, so you know when to compact before you hit the wall. 🛠️ AgentTool for multi-agent systems Wrap any Agent as a Tool so another Agent can delegate to it. Only the final reply crosses back, the coordinator's context stays clean. 💙 42 contributors made this release happen; huge thanks to everyone! 👉 Full release notes: haystack.deepset.ai/release-…
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HetznerChatGenerator brings European sovereign cloud LLMs to Haystack. Process text and images through @Hetzner_Online infrastructure while keeping your AI workloads within EU borders. 🐍 pip install hetzner-haystack 🔗 haystack.deepset.ai/integrat…
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We are thrilled to be among the ten finalists for The Spark – The Technology Award. For eleven years, @handelsblatt and @McKinsey have been recognizing Germany’s most innovative technology startups. This year’s theme is "Scaling Deep Tech", focusing on technologies that are already making a real-world impact today and shaping the future of industry and the economy. Now, we need your support. Vote by September 7th to help us reach the Top 3! Your vote counts directly toward the jury's decision. Vote for us here: cmk.handelsblatt.com/cms/art…
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Finally, @Linkup_platform joins Haystack's growing search component library. Fetch real-time web results, get ranked documents with source links, and integrate them into your pipeline in a few lines. Same philosophy as other Haystack components - simple, direct, pluggable. haystack.deepset.ai/integrat…
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📷 DDGS (Dux Distributed Global Search) is now available in Haystack. Query Google, Bing, Brave, Yahoo, Yandex and more from your pipelines and agents. Multi-source web search without the infrastructure overhead. pip install ddgs-haystack haystack.deepset.ai/integrat…
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Haystack retweeted
I have been exploring the latest release of the @Haystack_AI v3: github.com/deepset-ai/haysta…, experimenting with it, and trying to create use cases around it. ✨ Primary features include: - Prebuilt agents - Hooks-driven execution - Agentic Skills - Cost Monitoring
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Haystack 3.0 Live Office Hours - New Features, Migration & Q&A nitter.net/i/broadcasts/1qKVmmjkb…
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Please welcome Mirage by @struktoai to the Haystack integrations family. Create isolated virtual filesystems with multiple mounts, such as in-memory storage backed by cloud resources like S3, and give your agents the ability to explore and analyze files using bash commands without exposing your infrastructure. Agents can now run grep, cat, head, wc, and other shell utilities against mounted data sources, treating them as a unified filesystem. Control which commands are allowed and keep sensitive resources read-only while giving agents just the access they need. 🐍 pip install mirage-haystack 🔗 haystack.deepset.ai/integrat…
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Haystack 3.0 shipped this month. We spent launch week showing you what it can do. Now here's what it took to build it 👇 Since @Haystack_AI 2.0: 2,400+ pull requests merged, 195 contributors, ~170 of them making their first-ever contribution along the way. 17,258 CI runs since the v3 branch was cut, roughly one every five minutes, around the clock, for eight weeks. Most-edited file of the whole cycle: agent.py. Of course it was. Migrating to 3.0? The full guide covers all breaking changes: docs.haystack.deepset.ai/doc… Or skip straight to haystack-v2-to-v3, an agent skill that migrates your v2 pipelines and agents for you. 💙 Huge thanks to our open-source team at @deepset_ai, and to our community contributors who shipped fixes and features for v3 alongside us! Haystack 3.0 is yours as much as it's ours. Got questions about migrating, or curious why we made a specific call? Join us at Haystack 3.0 Office Hours next week, August 4, with @LukawskiKacper and @bilgeycl 👉🏼 Register: luma.com/haystack-3
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Day 5 of Haystack 3.0 Launch Week: Human-in-the-Loop from Terminal to Production For today's drop, we implemented a production agent that pauses before anything risky and waits for a human to say go. The approval shows up right where the conversation is happening, making the process intuitive for the users. We built the whole thing as a real, deployable service (Hayhooks + @Redisinc + @OpenWebUI), so it's not just another notebook trick or a simple terminal demo Set up with one command and run all services with @Docker 🐳 🎥 Watch it in action ↓
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📬 Day 4 of Haystack 3.0 Launch Week: a fully local agent, running on @ollama with no API key, that reads skills, saves tokens with the caveman skill, and uses a real bash tool to control your machine, with a human approving every step. Skills only expose a name and one-line description upfront; with progressive disclosure, the full instructions load only when needed. That keeps context and token cost small, even with a large skill library. The agent then acts on a real machine through a custom bash tool, with a human approving every call before it runs, using hooks. code link below ↓ piped.video/watch?v=vefpGVFw…
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