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…

Haystack is coming to @NVIDIAGTC Berlin 💚💙 🌃 Tomorrow, 21 Oct, 18:00: Hack Night at our Berlin office, part of our House of Sovereignty series. Get hands-on with the Haystack Enterprise Platform, take on a challenge, and meet the team. Only 30 spots, so apply here: → luma.com/hep-hack-night 🎙️ Thursday, 22 Oct, 10:00: @malte_pietsch and @Julian_Risch join Oleg Sudakov from @nvidia for "Evolve Within Bounds: Secure, Self-Improving Agents for On-Prem AI" → nvidia.com/en-eu/gtc/session… 📍 Booth 3071, both expo days: come meet the team and grab your Hay-swag

Haystack 3.4 is here, and it focuses on agents that can run for long periods. Agents that call search, file, or API tools can quickly fill the context with large results. 3.4 adds the experimental ToolResultOffloadCompactor for CompactionHook. When compaction triggers, it writes older tool results to a store and replaces them with references. The agent reads them back only when it needs them. Context is shorter, but nothing is lost. Other highlights: 🔍 Better observability: failed tool calls carry a haystack.tool.error tag in tracing spans, so they’re no longer confused with tools that return error dicts 🐍 Python 3.15 support (Python 3.10 is no longer supported because it reaches EOL this month) 📄 Converter improvements: DOCX text boxes are extracted, HTML encodings are detected automatically ⚠️ Before you upgrade ⚠️ The default OpenAI and Azure embedding model is now text-embedding-3-small. It’s cheaper and stronger, but its embeddings aren’t compatible with ada-002. If you have an existing index, pin the old model or re-embed. 💻 pip install -U haystack-ai Full release notes: haystack.deepset.ai/release-…

Most of what we ask LLMs to do inside pipelines isn't generation. It's decisions. @typesafeai System One models are built for exactly that, and they're now in @Haystack_AI The new integration gives you two components: 🏷️ TypeSafeDocumentClassifier answers your questions about each document and stores the results in its metadata. 🔀 TypeSafeTextRouter sends text to the right branch, with an optional low_confidence output so uncertain cases can fall back to an LLM or a human. Use them with TypeSafe's hosted Jev model, or keep everything on your own infrastructure with Ollaya, which serves open decision models like Laya locally through the TypeSafe API. 🐍 pip install typesafe-haystack 👉 haystack.deepset.ai/integrat…

Europe has a new open-weight model for sovereign AI 🇪🇺 @Aleph__Alpha's Kolibri works with @Haystack_AI from day one. Developed and trained in Europe, Kolibri is a German-English Mixture-of-Experts model released under Apache 2.0 and built for demanding use cases across public administration and industry. With Haystack, you can run Kolibri on infrastructure you control and build it directly into your own Agents and pipelines, without making an external model API a dependency. Open framework. Open weights. Your infrastructure. Your AI stack. haystack.deepset.ai/integrat…

🌐 Building an AI agent that needs web intelligence? @youdotcom brings semantic web search and real-time retrieval directly into your Haystack agents! Add YoucomWebSearch to your agent (or your pipeline), configure your You.com API credentials, and your agent can search the live web mid-run, pulling in content, snippets, and metadata that provide enough ground for downstream tasks. Think of an agent that researches a topic in real time instead of working off its index. 🐍 pip install youcom-haystack 🔗 haystack.deepset.ai/integrat…

📊 @rhesisai is an open-source platform for collaboratively testing and evaluating LLM applications and agents. It is now available in @Haystack_AI via RhesisConnector. Constructing the connector installs the tracer, so every pipeline run, agent step, tool call, and specialist handoff arrives in Rhesis as a single trace, grouped by session. The connector covers both sides of a release. During testing, traces show why a case failed and where in the pipeline it failed. In production, they show the same for real user sessions, and a session that went wrong becomes the next test case. 🐍 pip install rhesis-haystack 🔗 haystack.deepset.ai/integrat…

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-…

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…

📣 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

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…

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-…

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…

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…

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…