Skip to main content
Submit

Haystack

Open-source LLM framework for RAG and NLP pipelines — production-ready by deepset

LLM FrameworksFree

Haystack is an open-source Python framework for building production-grade NLP and LLM applications, developed by deepset. It provides a pipeline-based architecture for RAG systems, question answering, semantic search, and document intelligence. Haystack 2.0 introduced a composable component system supporting any LLM provider — used by 15,000+ teams including Airbus, Netflix, and Nvidia.

Visit site
Key specs
26,997 GitHub stars source
as of 2026-11-12

Maker of Haystack? Get your db.fyi badge

What we know

LicenseApache-2.0source, checked 2026-11-12
Open sourceYessource, checked 2026-11-12
PricingFreefrom our original listing, not yet re-checked
GitHub stars26,998source, checked 2026-11-12

Why it matters

  • 16K+ GitHub stars and enterprise adoption (Airbus, Netflix, Nvidia) validates production-grade reliability beyond research prototypes.
  • Type-safe pipeline architecture catches integration errors at component connection time — not at runtime — making pipelines more reliable.
  • deepset's enterprise background means Haystack is built for production: observability, error handling, and performance optimization are first-class concerns.
  • Provider-agnostic design (any LLM, any vector DB, any embedder) via a composable component system prevents vendor lock-in.

Key capabilities

  • Pipeline architecture: Connect components (retrievers, embedders, LLMs, rankers) into data flow graphs.
  • RAG pipelines: Complete retrieval-augmented generation with document ingestion, embedding, retrieval, and generation.
  • Multi-provider: OpenAI, Anthropic, Cohere, HuggingFace, Azure, AWS Bedrock, Ollama via unified component interface.
  • Vector database integrations: Weaviate, Pinecone, Qdrant, Milvus, Elasticsearch, pgvector, In-Memory.
  • Document processing: Converters for PDF, DOCX, HTML; chunking; metadata extraction.
  • Evaluation: Built-in evaluation framework for RAG quality metrics.
  • Type safety: Component inputs/outputs are typed; pipeline graph validates connections.
  • Custom components: Build reusable custom components following the component interface.

Technical notes

  • License: Apache 2.0 (open source)
  • GitHub: github.com/deepset-ai/haystack (16K+ stars)
  • Install: pip install haystack-ai
  • Python: 3.8+
  • LLMs: OpenAI, Anthropic, Cohere, HuggingFace, Ollama, Azure, Bedrock, and more
  • Vector DBs: Weaviate, Pinecone, Qdrant, Milvus, pgvector, Elasticsearch
  • Company: deepset; Berlin, Germany; founded 2018; raised $30M+ (GV, 42CAP)

Ideal for

  • Engineering teams building production RAG systems who value explicit, type-safe pipeline definitions over LangChain's flexibility.
  • European teams with GDPR considerations who prefer a German-founded company's data handling approach.
  • Organizations building document intelligence systems (contracts, reports, manuals) where reliable extraction and retrieval are critical.

Not ideal for

  • Quick prototyping — Haystack's structured approach requires more setup than LangChain's quick-start patterns.
  • Teams who need the broadest integrations ecosystem — LangChain has more third-party integrations.
  • Agent-heavy workflows where dynamic, self-modifying pipelines are needed — Haystack is optimized for defined pipeline graphs.

See also

  • LangChain — Most popular LLM framework; more flexible, larger community.
  • LlamaIndex — RAG-focused with strong document loading and indexing features.
  • DSPy — Programmatic LLM pipeline optimization; different paradigm from Haystack.

FAQ

What is Haystack?

Haystack is an open-source framework for building LLM and NLP applications in Python, built by deepset. It uses a pipeline architecture: you connect components (document retriever, embedder, LLM, ranker) into a graph, and Haystack handles the data flow. Haystack 2.0 supports any LLM (OpenAI, Anthropic, Cohere, HuggingFace), vector databases (Weaviate, Pinecone, Qdrant, Milvus), and embedding models.

Is Haystack free?

Haystack is completely free and open source (Apache 2.0). The haystack-ai package is on PyPI (`pip install haystack-ai`). deepset Cloud is the managed platform for running Haystack pipelines with enterprise features — it has a free tier and paid plans. The core framework has no costs beyond your LLM API usage.

How does Haystack differ from LangChain?

Both are LLM application frameworks. Haystack has a more structured, type-safe pipeline approach — components have defined inputs/outputs with type validation. LangChain is more flexible but can be harder to debug. Haystack's production-first design (developed by deepset who deploys it for enterprise customers) tends to produce more reliable pipelines. LangChain has a larger ecosystem and more integrations. Many teams find Haystack's explicit pipeline graph easier to reason about.

What is deepset?

deepset is the German AI company that created Haystack. They offer deepset Cloud (managed RAG platform) and use Haystack internally for enterprise NLP projects across industries. deepset has been building NLP infrastructure since 2018 — Haystack reflects years of production experience. They're based in Berlin with $30M+ raised from investors including GV (Google Ventures).

Alternatives

Integrations

None listed.

Built on

None listed.

Good for these tasks

Related tools

Listed 2026-02-28 · updated 2026-09-11