Open-Source AI · LLM / RAG framework

LangChain vs Haystack

LangChain vs Haystack compared for 2026 — features, license, ease of use, performance and which one to choose. Compose chains, tools and agents vs Production pipelines for search and RAG.

Updated regularly · curated by olud.ai

Choose LangChain for developers building tool-using LLM apps. Choose Haystack for teams wanting production search pipelines.

LangChain vs Haystack at a glance

SpecLangChainHaystack
CategoryLLM / RAG frameworkLLM / RAG framework
TypeLLM app frameworkNLP / RAG framework
LicenseMITApache-2.0
Runs locallyCloud-optionalCloud-optional
Primary languagePython / JSPython
Ease of useIntermediateIntermediate
Best fordevelopers building tool-using LLM appsteams wanting production search pipelines
GitHub stars142.3k26k

Feature comparison

FeatureLangChainHaystack
Python
JavaScript / TS
Agents
RAG
Streaming
Many integrations

How LangChain and Haystack score

🏆 Overall edge: LangChain — 4.4 vs 4.1 / 5
CriterionLangChainHaystack
Popularity5.03.5
Maintenance5.05.0
Ease of use3.53.5
Privacy3.53.5
License freedom5.05.0

Scores are computed automatically from public signals — GitHub stars (popularity), recent commit activity (maintenance), license type (freedom), local-first design (privacy) and onboarding complexity (ease of use). Indicative, not a verdict.

What each one is

LangChain

LLM app framework · MIT

LangChain is a framework for building LLM applications by composing prompts, models, tools, memory and agents, with a vast ecosystem of integrations.

  • Huge ecosystem of integrations
  • Building blocks for chains, tools and agents
  • Python and JavaScript support
See the LangChain page →

Haystack

NLP / RAG framework · Apache-2.0

Haystack by deepset is a production-oriented framework for building search and RAG pipelines with a clear, composable component model.

  • Production-first, composable pipeline model
  • Strong document search and retrieval
  • Apache-2.0 with enterprise backing
See the Haystack page →

Key differences

LangChain is lLM app framework, while Haystack is nLP / RAG framework. Their licenses differ (MIT vs Apache-2.0), which matters if you ship a commercial product. In short, LangChain fits developers building tool-using LLM apps, and Haystack fits teams wanting production search pipelines.

Which should you choose?

Choose LangChain for developers building tool-using LLM apps. Choose Haystack for teams wanting production search pipelines.

There is rarely one winner — many setups use both. The right pick depends on your hardware, your team's skills, and whether you value simplicity or control.

Frequently asked questions

Is LangChain or Haystack easier to use?

Both sit at a similar level (Intermediate). Your choice should come down to fit rather than difficulty.

Are LangChain and Haystack free?

LangChain is free and open source (MIT), and Haystack is free and open source (Apache-2.0). Neither charges for the core software.

Can I run LangChain and Haystack locally?

LangChain: cloud-optional · Haystack: cloud-optional. Both can be used without sending your data to a third-party cloud where their setup allows.

LangChain vs Haystack — which should I pick in 2026?

Choose LangChain for developers building tool-using LLM apps. Choose Haystack for teams wanting production search pipelines.

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