Haystack vs LangGraph
Side-by-side comparison of features, pricing, and ratings
At a glance
| Dimension | Haystack | LangGraph |
|---|---|---|
| Best for | Teams deploying production RAG in regulated environments; search/IR specialists migrating to LLM-powered answers; platform teams needing declarative pipeline configs. | Engineers building production agents; teams needing human-in-the-loop approval flows; applications with long-running multi-step workflows. |
| Pricing | Open source Apache 2.0 (free) with deepset Cloud custom pricing for managed hosting. | Open source MIT (free) with LangGraph Platform from $39/mo (Plus) for durable hosted runtime. |
| Setup complexity | Moderate: requires understanding of typed pipelines and YAML serialization; explicit wiring of components. | Moderate: requires understanding of graph state machines and node/edge concepts; easier with LangGraph Studio. |
| Strongest differentiator | Declarative, component-based pipeline model with YAML serialization for cloud-agnostic deployment and built-in evaluation (RAGAS, SAS). | Durable state persistence with time-travel debugging, human-in-the-loop checkpoints, and parallel branches for complex agent orchestration. |
Haystack vs LangGraph serve fundamentally different primary use cases. Haystack is the stronger choice for teams building production RAG pipelines with explicit, observable components and YAML-based deployment—especially in regulated environments. LangGraph dominates for stateful, multi-step agent workflows requiring durability, human-in-the-loop, and time-travel debugging. If your core need is search-augmented generation with evaluation, go with Haystack. If you are orchestrating complex agent loops with branching and persistence, LangGraph wins.
Open-source framework for building production-ready RAG, agents, and AI applications with explicit pipeline composition.
Visit WebsiteGraph-based orchestration framework for stateful, multi-step LLM agents from the LangChain team.
Visit WebsiteFeature-by-feature
Core capabilities: Haystack vs LangGraph
Haystack enforces a component-based pipeline model where typed inputs/outputs are validated at build time. Pipelines can be serialized to YAML and deployed cloud-agnostically with no Python glue code. It includes built-in evaluation metrics like RAGAS, answer correctness, and SAS. LangGraph, by contrast, is a graph-based state machine for agents. Nodes are functions/LLM calls/tools, edges define transitions (conditional or not), and state persists across steps. LangGraph provides durable execution, time-travel debugging, and human-in-the-loop checkpoints. Haystack wins for RAG pipelines requiring evaluation and reproducible deployments; LangGraph wins for complex agent orchestration with error recovery.
AI/model approach: Haystack vs LangGraph
Both are model-agnostic. Haystack integrates with 110+ services including OpenAI, Anthropic, Gemini, Cohere, HuggingFace, Ollama, and Mistral. LangGraph also supports major providers (OpenAI, Anthropic, Gemini, Ollama, Azure OpenAI, AWS Bedrock). Haystack's typed component model allows seamless swapping of LLM components within a pipeline. LangGraph's graph approach allows conditional routing to different models per node. Haystack offers a standardized generator interface for conversational AI. LangGraph provides long-term memory primitives and a library of prebuilt agent templates. For teams that need to mix retrieval and generation in a deployable pipeline, Haystack has the edge; for flexible multi-model agent flows, LangGraph is superior.
Integrations & ecosystem: Haystack vs LangGraph
Haystack integrates with over 110 services, including major vector stores (Elasticsearch, OpenSearch, Pinecone, Weaviate, Qdrant, Chroma, Milvus, AstraDB, Azure AI Search) and monitoring tools (Arize Phoenix, Arize AI). deepset Cloud provides a visual pipeline builder and enterprise hosting. LangGraph integrates with LangChain components, LangSmith for observability, and the LangChain ecosystem. It also connects to vector stores via LangChain's abstractions. LangGraph Platform offers a hosted durable runtime, schedules, cron, and an Assistants API with 30+ endpoints. Haystack's wider native integration list makes it better for teams with existing search infrastructure. LangGraph's tighter LangChain ecosystem is advantageous for those already invested in LangChain.
Performance & scale: Haystack vs LangGraph
Haystack is designed for production deployments with YAML serialization and Kubernetes-ready guides. Pipelines can be deployed as REST APIs via Hayhooks. It supports streaming and async execution. deepset Cloud provides managed scaling. LangGraph emphasizes durable execution: state persists across steps, enabling recovery from failures. LangGraph Platform offers scheduled runs and cron agents. Time-travel debugging allows replaying runs from any step. Haystack scales well for high-throughput RAG services; LangGraph scales for long-running, stateful agent workflows. For pure throughput on RAG, Haystack likely wins; for reliability in multi-step agents, LangGraph wins.
Developer experience: Haystack vs LangGraph
Haystack provides a declarative pipeline builder with typed components, YAML serialization, and a visual builder in deepset Cloud. It includes built-in evaluation and context engineering tools. LangGraph offers LangGraph Studio (a local visual debugger) and a Python-first API for defining graphs. The learning curve for both is moderate. Haystack's pipeline model is familiar to engineers who have worked with data pipelines; LangGraph's state machine model is familiar to those who build stateful systems. LangGraph's time-travel debugging and human-in-the-loop checkpoints are powerful for debugging agent failures. Haystack's community-contributed custom components lower the barrier for extending the framework. For teams prioritizing reproducibility and observability in RAG, Haystack is better; for debugging agent workflows, LangGraph is better.
Pricing compared
Haystack pricing (2026)
Haystack is open source under Apache 2.0, completely free for self-hosted use. The Open Source tier includes the full framework, all integrations, YAML pipeline serialization, and community support. deepset Cloud is a managed platform with custom pricing (no published tiers), offering visual pipeline builder, SSO, and enterprise support. There is no free cloud tier. Overage fees or contract terms are not published.
LangGraph pricing (2026)
LangGraph is open source under MIT, free for self-hosted use. The Open Source tier includes the full framework and LangGraph Studio (local debugger). LangGraph Platform has a Plus plan at $39/month, which includes a durable hosted runtime, scheduled runs, human-in-the-loop API, and cron agents. Higher tiers likely exist but details are not in the data. No mention of overage fees or contract terms. LangSmith for observability has separate pricing.
Value-per-dollar: Haystack vs LangGraph
Both frameworks offer generous open-source licenses. For teams purely deploying RAG pipelines, Haystack's free tier is sufficient, and deepset Cloud's custom pricing may be worthwhile for managed hosting. For teams building production agents, LangGraph's free open-source tier is also sufficient; the $39/mo Plus plan adds durable hosting. Haystack likely provides better value for search-centric teams, while LangGraph provides better value for agent-centric teams. Neither has hidden fees from available data, but LangGraph's platform pricing is transparent while deepset Cloud requires a sales call.
Who should pick which
- Search/IR team migrating to LLM answers with ElasticsearchPick: Haystack
Haystack integrates natively with Elasticsearch and other vector stores, provides built-in RAGAS evaluation, and supports YAML-based deployment, making it ideal for production RAG on existing indices.
- Platform team needing declarative pipeline configsPick: Haystack
Haystack's YAML serialization and component-based pipeline model allow deploying pipelines as code, with no Python glue, suiting platform teams that need reproducible, cloud-agnostic deployments.
- Engineer building a customer service agent with human-in-the-loopPick: LangGraph
LangGraph's durable state persistence, human-in-the-loop checkpoints, and time-travel debugging directly support agent workflows that pause, escalate, and resume without losing context.
- Developer creating a multi-tool research agent with branchingPick: LangGraph
LangGraph's graph-based state machine with parallel branches and join nodes enables complex agent orchestration that Haystack's linear pipeline model does not support natively.
Frequently Asked Questions
Is Haystack free for commercial use?
Yes, Haystack is open source under Apache 2.0, which allows commercial use without restrictions. deepset Cloud, the managed platform, requires a custom subscription.
Is LangGraph free for commercial use?
Yes, LangGraph is open source under MIT, which is permissive for commercial use. The LangGraph Platform (hosted runtime) starts at $39/month.
Does Haystack integrate with my existing vector store?
Haystack integrates with over 110 services including major vector stores: Elasticsearch, OpenSearch, Pinecone, Weaviate, Qdrant, Chroma, Milvus, AstraDB, and Azure AI Search.
Does LangGraph support human-in-the-loop workflows?
Yes, LangGraph has built-in human-in-the-loop checkpoints that allow an agent to pause execution, wait for human input, and resume from the same state.
Which tool is better for RAG evaluation?
Haystack has a built-in evaluation framework with metrics like RAGAS, answer correctness, and SAS, making it the better choice for teams that need to measure RAG quality.
Can I use Haystack and LangGraph together?
Yes, you can embed a LangGraph agent as a component within a Haystack pipeline or vice versa. However, their architectures differ, so integration requires custom glue.
What is the learning curve for Haystack?
Moderate. You need to understand typed pipelines, component wiring, and YAML serialization. Haystack's documentation and community-contributed components help.
What is the learning curve for LangGraph?
Moderate. You need to understand graph state machines, nodes, and edges. LangGraph Studio provides a visual debugger to ease development.
Can I deploy Haystack pipelines on Kubernetes?
Yes, Haystack provides Kubernetes-ready guides and YAML serialization for cloud-agnostic deployment.
Does LangGraph have a visual debugger?
Yes, LangGraph Studio is a local visual debugger that lets you step through agent execution and inspect state.
Last reviewed: May 12, 2026