Haystack
Open-source framework for building production-ready RAG pipelines and AI agents with modular components
18K+
GitHub Stars
100+
Integrations
v2.x
Latest Version
Overview
Haystack is deepset's open-source framework for building production-grade RAG (Retrieval-Augmented Generation) pipelines and AI agents. Unlike frameworks that prioritize quick prototyping, Haystack focuses on building reliable, scalable systems with clear pipeline architecture. It uses a modular component-based design where you connect Retrievers, Generators, Readers, and custom components into directed graphs. The framework supports 30+ LLM providers, multiple vector databases, and provides first-class support for document processing, semantic search, and conversational AI applications.
The Verdict
Who Should Use Haystack?
Best For
- Production RAG system builders
- Teams needing pipeline orchestration
- Enterprise document search applications
- NLP engineers building question answering
- Projects requiring reproducible pipelines
Not Ideal For
- Quick prototypes (try LangChain)
- Non-Python developers
- Simple chatbot projects
- Teams wanting managed solutions
What's Great
- Production-first architecture with clear DAG pipelines
- Excellent document processing and indexing
- Strong typing and pipeline validation
- 100+ integrations (vector DBs, LLMs, tools)
- Comprehensive evaluation framework built-in
- Active community and deepset enterprise support
Watch Out For
- Steeper learning curve than LangChain
- Pipeline-centric approach can feel rigid
- Smaller community than competitors
- Documentation can lag behind releases
- Agent capabilities newer than RAG features
Pricing
Open Source
Free
Apache 2.0 license, full framework
deepset Cloud
Contact
Managed deployment, enterprise features
Enterprise
Custom
On-prem, SLA, dedicated support
View all features & details
Core Components
- Document Stores (Elasticsearch, Pinecone, Weaviate, Qdrant, etc.)
- Retrievers (BM25, Dense, Hybrid, Multi-modal)
- Generators (OpenAI, Anthropic, Cohere, local models)
- Readers (Extractive QA, Summarization)
- Converters (PDF, DOCX, HTML, Markdown)
- Preprocessors (splitting, cleaning, embedding)
Pipeline Features
- Directed Acyclic Graph (DAG) architecture
- Pipeline serialization (YAML/JSON)
- Built-in pipeline validation
- Streaming support
- Async execution
- Pipeline visualization
Agent Capabilities
- Tool-calling agents
- Function routing
- Memory components
- Conversation history
- Custom tool integration
Evaluation & Testing
- Built-in evaluation pipelines
- RAGAS integration
- Faithfulness & relevance metrics
- Answer correctness scoring
- Retrieval metrics (MRR, Recall)
How It Compares
| Feature | Haystack | LangChain | LlamaIndex |
|---|---|---|---|
| Primary Focus | Production RAG | Prototyping/chains | Data indexing |
| Architecture | DAG pipelines | Chains/agents | Index + query |
| Document Processing | Excellent | Good | Good |
| Evaluation Built-in | Yes | External only | Basic |
| Learning Curve | Moderate | Easy | Easy |
| Enterprise Support | deepset Cloud | LangSmith | LlamaCloud |
| Community Size | Medium | Largest | Large |
| Agent Maturity | Growing | Mature | Mature |
| Best For | Production RAG | Prototyping | Data-heavy apps |
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