Haystack vs LangGraph
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Haystack | LangGraph |
|---|---|---|
| Pricing | Free & open-source (Apache 2.0); paid enterprise tier available | Free & open-source (MIT); LangSmith paid observability |
| Core Focus | Modular RAG pipelines & AI agents with full pipeline visibility | Low-level graph-based stateful agents & multi-agent orchestration |
| Key Differentiator | Serializable pipelines, hybrid retrieval, Jinja-2 templating, multimodal | Human-in-the-loop, built-in memory, token streaming, Rubrics self-evaluation |
| Integrations | OpenAI, Anthropic, Mistral, Hugging Face, Weaviate, Pinecone, Elasticsearch | OpenAI, Anthropic, Google, Mistral, Meta; LangSmith |
| Best For | Production RAG, content generation, multimodal apps, multi-provider setups | Complex stateful agents, multi-agent hierarchies, human oversight |
| Latest Update | v2.30.0 (June 2026): Plain string input for any ChatGenerator | June 2026: Prompt caching, memory guidance, loop engineering techniques |
For teams building production RAG systems with full pipeline control, Haystack is the stronger choice with its modular components, hybrid retrieval, and no vendor lock-in. For developers needing fine-grained stateful agent orchestration with human-in-the-loop and multi-agent hierarchies, LangGraph offers unmatched low-level flexibility. Choose based on whether your priority is retrieval-augmented generation (Haystack) or complex agent workflows (LangGraph).
Feature-by-feature
Haystack and LangGraph both provide open-source frameworks for building AI applications, but they excel in different areas. Haystack focuses on modular, serializable pipelines for RAG, offering hybrid retrieval (dense + sparse), Jinja-2 templating, and multimodal support (image/audio). Its latest 2.30.0 update allows any ChatGenerator to accept plain string input, simplifying agent development. It integrates with major LLM providers and vector databases, promoting flexibility. LangGraph, on the other hand, is a low-level graph-based framework for stateful agents with built-in memory, token-by-token streaming, and human-in-the-loop checks. It supports single, multi-agent, and hierarchical workflows, and includes Rubrics for self-evaluation. The June 2026 news highlights prompt caching in Deep Agents and memory guidance. LangGraph integrates with LangSmith for observability. Both tools support model-agnostic LLM integration, but Haystack emphasizes pipeline visibility and debugging, while LangGraph provides fine-grained control over agent state and flow. Haystack is better for RAG and content generation; LangGraph for complex, stateful agent orchestration. Note that LangGraph has a known RCE vulnerability shared with LangFlow, so security auditing is critical.
Pricing compared
Both Haystack and LangGraph are free and open-source, but their licensing differs: Haystack uses Apache 2.0, while LangGraph uses MIT. Haystack offers a paid enterprise tier with additional features, but no pricing details are specified. LangGraph itself is free, but integration with LangSmith for production observability may incur costs. Neither tool enforces vendor lock-in, but LangGraph's ecosystem leans heavily on LangChain and LangSmith. Haystack's pricing model is freemium, with the open-source version fully functional for most development needs. LangGraph's MIT license is more permissive for commercial use. For budget-conscious teams, both frameworks are cost-effective at the base level; enterprise support may be a consideration for Haystack users.
Who should pick which
- RAG Pipeline DeveloperPick: Haystack
Haystack offers modular components, hybrid retrieval, and serialization for building production-ready RAG systems with full visibility.
- Stateful Agent EngineerPick: LangGraph
LangGraph provides built-in memory, human-in-the-loop, and graph-based state management for complex, stateful agent workflows.
- Multi-Agent OrchestratorPick: LangGraph
LangGraph supports single, multi-agent, and hierarchical workflows with fine-grained control, ideal for multi-agent systems.
- Multimodal Application DeveloperPick: Haystack
Haystack supports image and audio processing in a single framework, making it suitable for multimodal AI applications.
- Content Generation TeamPick: Haystack
Haystack's Jinja-2 template engine and pipeline approach enable customizable content generation workflows.
Frequently Asked Questions
Which framework is better for building a chatbot with RAG?
Haystack is specifically designed for RAG pipelines with hybrid retrieval, making it a strong choice for chatbot RAG. LangGraph can also implement RAG but is more focused on stateful agent workflows.
Can I use both Haystack and LangGraph together?
Yes, you can integrate Haystack's retrieval components within a LangGraph agent, or use LangGraph for agent orchestration while leveraging Haystack for document processing. They are complementary.
Which framework has better streaming support?
LangGraph offers first-class token-by-token streaming for real-time UX, while Haystack also supports streaming but with less emphasis on agent-specific streaming nuances.
Do these frameworks support human oversight?
LangGraph includes built-in human-in-the-loop checks for agent moderation. Haystack does not natively provide this, but it can be implemented through custom pipeline nodes.
Which framework is easier to deploy to production?
Haystack emphasizes Kubernetes-ready deployment with serializable pipelines. LangGraph works with LangSmith for deployment and observability, but may require more custom infrastructure.
Are there any security concerns with LangGraph?
Yes, LangGraph shares a known RCE vulnerability with LangFlow (as per its documentation). Proper security auditing is essential before deploying LangGraph agents.
How do the communities compare?
Both have active communities. Haystack is backed by deepset, while LangGraph is part of the LangChain ecosystem, which has a larger community but also more dependencies.
Which framework supports more LLM providers?
Both are model-agnostic. Haystack integrates with OpenAI, Anthropic, Mistral, Hugging Face, etc. LangGraph supports OpenAI, Anthropic, Google, Mistral, Meta. The choice depends on your specific provider needs.
More Haystack or LangGraph comparisons
Choose Langfuse if your priority is observability, evaluation, and prompt management for production LLM apps—especially if you need self-hosting. Choose LangGraph if you are building complex stateful
Choose LangChain if you're building complex, long-running agents that require deep observability, checkpointing, and human-in-the-loop control. Choose Haystack if you need an open-source, modular fram
If you want a production-ready agent harness with sub-agents, filesystem access, and human-in-the-loop out of the box, DeepAgents is the better choice. If you need fine-grained control to build custom
For developers building streaming chatbots or generative UI with minimal boilerplate, Vercel AI SDK is the clear winner. If you need deep control over stateful, multi-agent workflows with human oversi
LangGraph gives developers full, low-level control over agent state and logic at zero cost, ideal for custom production workflows. CrewAI delivers enterprise governance, discovery, and observability o
For enterprise .NET shops already deep in Azure and Microsoft 365, Semantic Kernel is the natural choice—tight integration with Microsoft Graph and Azure OpenAI makes building Copilot-like agents stra
Explore each tool further
Browse these categories
One email a week — new tools, honest comparisons, no spam.