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Haystack vs LangGraph

HaystackLangGraph

Bottom line: Haystack for teams building production RAG and search; LangGraph for engineering teams building production agents.

HaystackHaystack

deepset's composable open-source framework for RAG and agent pipelines.

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LangGraphLangGraph

Graph-based orchestration for stateful, controllable LLM agents.

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Votes00
PricingFreemiumFreemium
CategoryAgent FrameworksAgent Frameworks
Tags
ragllm-frameworkopen-sourcesearchpipelines
agentsorchestrationllm-frameworkopen-sourceworkflow
Best for
  • Teams building production RAG and search
  • Enterprises wanting self-hostable NLP pipelines
  • Developers who prefer explicit, typed pipelines
  • Engineering teams building production agents
  • Developers needing controllable, resumable workflows
  • Teams already invested in LangChain
Pros
  • Composable, typed pipeline model is clear and flexible
  • Apache-2.0 core, free to self-host with no lock-in
  • Strong retrieval and search heritage
  • Broad integrations with model and vector stores
  • Built-in evaluation tooling
  • Explicit, debuggable control over agent state and flow
  • MIT-licensed core, free to self-host
  • Model-agnostic, not tied to one LLM vendor
  • Strong support for cycles, checkpointing, and human-in-the-loop
  • Backed by the widely used LangChain ecosystem
Cons
  • Engineering framework, not a turnkey app
  • Managed deepset pricing is largely quote-based
  • 2.x rewrite means older 1.x tutorials are outdated
  • Skews toward retrieval more than general agents
  • Requires pipeline-design comfort
  • Steeper learning curve than high-level agent builders
  • Requires comfort with graph and state concepts
  • Paid platform and observability are separate products
  • Documentation and APIs have evolved quickly
  • Can be overkill for simple single-shot LLM tasks

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