RAG framework
Haystack
VerifiedOpen-source Python orchestration framework for modular RAG pipelines and agent workflows, with explicit components for retrieval, routing, memory, and generation.
What is Haystack?
Haystack is an open-source Python orchestration framework for building production-ready LLM applications. It provides modular components and pipelines for retrieval, routing, memory, generation, and agent tool use. Teams use Haystack when they want explicit control over RAG and agent workflows while keeping model, vector-store, and deployment choices open.
Current version
Haystack 2.29.0 is the current stable release as of May 12, 2026. Install the 2.x package with pip install haystack-ai.
- Haystack 1.x reached end of life on March 11, 2025.
- Do not install farm-haystack and haystack-ai in the same Python environment; the official installation guide warns that the packages can conflict.
- Verify the latest patch release at github.com/deepset-ai/haystack/releases before pinning production dependencies.
Pricing
Framework and managed-platform pricing, USD, as of June 2026 - verify current plans at deepset.ai/pricing.
| Offering | Price | Included |
|---|---|---|
| Haystack open-source framework | $0 | Apache 2.0 Python package; self-hosted |
| deepset Studio | $0 | 1 workspace, 1 user, 100 pipeline hours, 50 files, 2 development pipelines |
| deepset Enterprise | Custom | Unlimited workspaces, users, development pipelines, and production HA pipelines; cloud or custom deployment |
LLM-provider, embedding, vector-store, and infrastructure charges are separate from the open-source framework license. Enterprise pricing is custom.
Key insights
Concrete technical or product signals.
- Haystack 2.x organizes application logic as explicit components connected in pipelines; agents can combine adaptive tool use with deterministic pipeline steps.
- The core haystack-ai package is model-agnostic. Model-provider, embedding, vector-store, and infrastructure charges remain separate from the framework license.
- Haystack 1.x reached end of life on March 11, 2025. New projects should use the haystack-ai package rather than farm-haystack.
Use cases
Where this shines in production.
- Enterprise RAG pipelines with retrieval, re-ranking, and generation steps
- Semantic search and question-answering systems
- Agent workflows that need explicit orchestration and custom components
- Self-hosted Python applications that need control over model and data-provider choices
Limitations & trade-offs
What to watch for.
- Haystack is a Python framework, not a hosted inference service; teams still choose, operate, and pay for model providers and retrieval infrastructure.
- The managed deepset Enterprise plan uses custom pricing. Validate deployment, support, and governance requirements directly with deepset.
- Do not install farm-haystack and haystack-ai in the same Python environment; the official installation guide warns that the packages can conflict.
Models referenced
Declared model dependencies or integrations.
No explicit model references yet.
Haystack is model-agnostic. Choose model providers, embedding models, and vector stores per application; verify the required integration packages before deployment.
Related prompts
Hand-picked or latest prompt templates.
Prompt
RAG Pipeline System Prompt Template
A production system-prompt template for retrieval-grounded answers with citation, access-control, and empty-retrieval handling rules.
Prompt
Model Evaluation Rubric for Production LLMs
A repeatable rubric for comparing production LLM candidates across quality, latency, cost, tool use, safety, and operational fit.
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Vector Embedding Pipeline for Enterprise RAG
A design template for enterprise embedding pipelines covering chunking, metadata, tenancy, indexing, refreshes, and retrieval evaluation.
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API Error Triage Workflow
A structured approach to identifying, categorizing, and resolving API errors in production systems.
Looking for a tighter match? Search the prompt library.
Haystack vs LangChain - quick decision guide
| Dimension | Haystack | LangChain |
|---|---|---|
| Primary abstraction | Explicit components connected in pipelines; agents can use components, pipelines, and tools | Prebuilt agent architecture and model or tool integrations; LangGraph provides lower-level graph orchestration |
| RAG workflow style | Strong fit for inspectable retrieval pipelines with explicit routing and component boundaries | Modular retrieval building blocks for 2-step, agentic, and hybrid RAG patterns |
| Agent workflow style | Combine deterministic pipelines with adaptive agent flows inside one framework | Start with LangChain agents and drop into LangGraph state, nodes, and edges for deeper control |
| Best fit | Python teams prioritizing explicit production RAG pipelines and open deployment choices | Teams prioritizing a broad integration ecosystem and LangGraph-based agent orchestration |
| Core framework price | $0, Apache 2.0 | $0, MIT |
Choose Haystack when your architecture review centers on explicit RAG components, pipeline inspection, and self-hosted control. Choose LangChain when its larger integration ecosystem or LangGraph runtime is the stronger fit. Prototype the same retrieval path in both before committing.
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