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Open-source Python framework from deepset for building production LLM pipelines, RAG, and agents.
Open-source Python framework from deepset for building production LLM pipelines, RAG, and agents.
Haystack is deepset's open-source framework for LLM apps, built around composable Pipelines — directed graphs of components (retrievers, generators, rankers, tools, custom Python) that make production RAG and agents predictable and testable. Where many frameworks add abstractions on top of the LLM call, Haystack keeps components small and the pipeline explicit, which pays off when you need to debug why one retrieval step is dragging down accuracy or swap a reranker without rewriting the world. Haystack 2.x supports all major LLMs (OpenAI, Anthropic, HuggingFace, Cohere, local via Ollama), 25+ vector stores, hybrid retrieval, structured output, function calling, and tool-using agents. The framework is used in production at companies like Airbus and Deutsche Telekom, and deepset also runs a paid enterprise platform (deepset Cloud) with a visual pipeline builder, evaluation dashboards, and managed deploy. The Haystack ecosystem includes native integrations for MCP — you can plug MCP servers in as tools inside a pipeline agent, so any MCP-exposed capability becomes available to a Haystack Agent. It's a strong pick when you want a mature, opinionated framework that doesn't hide the plumbing and gives you full control over evaluation and observability.
Haystack is a mature, production-focused framework for building RAG and search pipelines with excellent documentation. Its pipeline abstraction is clean but less flexible than LangChain for general-purpose agent workflows.
Pipelines are directed graphs of Components with typed input/output sockets. Connections are validated at build time for type compatibility rather than failing at runtime. Components are self-contained, independently testable, and reusable across pipelines.
Use Case:
Building a modular RAG system where you can swap the retriever from BM25 to embedding-based without modifying any other part of the pipeline.
Dedicated components for document ingestion including FileTypeRouter for format detection, converters for PDF/DOCX/HTML/Markdown, DocumentCleaner for noise removal, DocumentSplitter for chunking with overlap, and DocumentLanguageClassifier for language routing. Handles the messy reality of enterprise corpora.
Use Case:
Processing a corporate knowledge base of mixed format documents into clean, chunked, deduplicated documents ready for embedding and indexing.
Supports combining sparse retrieval (BM25) with dense retrieval (embedding similarity) using DocumentJoiner and reciprocal rank fusion. Reranking components from Cohere, Hugging Face cross-encoders, and others refine candidate sets before generation.
Use Case:
Building a legal document search system that combines keyword matching for exact statute references with semantic search for conceptual queries, then reranks the top 50 results down to the 5 most relevant for the LLM.
Entire pipelines can be serialized to YAML and deserialized back with Pipeline.dumps() and Pipeline.loads(). This enables pipeline-as-code practices: version control, environment-specific configs via templating, and sharing pipeline definitions without distributing Python code.
Use Case:
Deploying the same RAG pipeline across dev, staging, and production with YAML configs that only differ in document store endpoints, API keys, and model names.
Built-in components measuring retrieval metrics (recall, MRR, MAP, context relevance), generation quality (faithfulness, answer relevance), and end-to-end performance. Supports automated evaluation with LLM judges via LLMEvaluator and integrates with human annotation tools in deepset Cloud.
Use Case:
Running nightly evaluation benchmarks against a golden test set of 500 question/answer pairs to detect pipeline regressions when updating embedding models or prompts.
Managed platform from Haystack's creators offering a visual pipeline editor, evaluation tools, file management, annotation interfaces, and production monitoring. Pipelines built in code can be deployed to deepset Cloud, and pipelines built visually can be exported as Haystack code.
Use Case:
Enabling domain experts and developers to collaboratively build and deploy RAG pipelines using a visual editor for prompt and component tuning while maintaining code-level control through Git-tracked YAML.
Free
From $99/month (Starter)
Custom
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We believe in transparent reviews. Here's what Haystack doesn't handle well:
Haystack continues to expand its agentic AI capabilities in 2025-2026, marketing the framework as a foundation for 'agentic, context-engineered AI systems' rather than RAG alone. New offerings include the Haystack Enterprise Platform and Haystack Enterprise Trial, plus partnerships with DataCamp ('Building AI Agents') and DeepLearning.AI ('Building AI Applications') for official courseware. Integrations and cookbook recipes for tool-using agents, multi-modal pipelines, and structured output generation continue to land regularly.
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Pricing and features change often. If anything on this Haystack page is wrong or missing, tell us and include a link to the source. We review every correction.