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Head-to-head · LLM Application Development Frameworks

LangChain vs LangGraph

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score LangChain #1 in LLM Application Development Frameworks 9.4/10 Free plan Free plan✓ 0 of 5 features Visit LangChain
LangGraph #2 in LLM Application Development Frameworks 9.2/10 Free plan Free plan✓ 0 of 5 features Visit LangGraph

LangChain leads on 0 checks, LangGraph on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreLangChain · 9.4/10
  • Free planboth

LangChain scores higher on our rubric for llm application development frameworks: 9.4 against 9.2 out of 10; our editors rank them #1 and #2.

LangChain is the better fit for general-purpose LLM app and agent development. LangGraph is the better fit for developers building durable, stateful agent workflows.

  • LangChain fits best

    General-purpose LLM app and agent development

  • LangGraph fits best

    Developers building durable, stateful agent workflows

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Side by side

Feature LangChain 9.4/10 Visit ↗ LangGraph 9.2/10 Visit ↗
At a glance
Editor score 9.4 9.2
Ranking #1 in LLM Application Development Frameworks #2 in LLM Application Development Frameworks
Best for General-purpose LLM app and agent development Developers building durable, stateful agent workflows
Pricing model Free Free
Starting price Not published Not published
Free plan ✓ ✓
Free trial — —
Deployment Self-hosted Self-hosted, Cloud
Integrations 1,000+ integrations 2 integrations
Built for Solo, Small business, Mid-market, Enterprise Solo, Small business, Mid-market, Enterprise
Features LangChain 0/5 · LangGraph 0/5
RAG support Not published Not published
Agent and tool use Not published Not published
Workflow graphs Not published Not published
Prompt optimization Not published Not published
JavaScript/TypeScript SDK Not published Not published
Specs
Primary language Not published Not published
Open-source license Not published Not published
Our review
Pros
  • Connects applications to a broad range of models, tools, databases, and data sources
  • Provides document-processing and retrieval components for RAG workflows
  • Supports human approval and durable agent execution through LangGraph
  • Combines deterministic code and LLM-driven steps in graph workflows
  • Supports durable execution, checkpointing, and resumption after failures
  • Offers memory, streaming, and human review of agent state
Cons
  • Requires developers to run it in their own application environment
  • Its broad set of abstractions may exceed the needs of a narrowly scoped app
  • Tracing, evaluation, and deployment workflows are associated with separate LangSmith
  • Low-level control requires developers to define workflow behavior
  • Does not prescribe a high-level agent architecture
  • Verified integrations are narrower than LangChain’s
Our verdict

LangChain is an open-source framework for developers building LLM applications and agents. It is aimed at a wide range of project sizes, from solo developers to enterprise teams, and runs self-hosted in application code. Its scope covers…

Read the review →

LangGraph is an open-source framework and runtime for developers building long-running, stateful agents and workflows. Its graph model combines deterministic code with model-driven steps, and supports single-agent, multi-agent, and…

Read the review →
  1. LangChainLLM Application Development Frameworks 9.4Free plan
  2. LangGraphLLM Application Development Frameworks 9.2Free plan

Strengths and trade-offs

  • LangChain — where it wins

    • Connects applications to a broad range of models, tools, databases, and data sources
    • Provides document-processing and retrieval components for RAG workflows
    • Supports human approval and durable agent execution through LangGraph

    Where it doesn't

    • Requires developers to run it in their own application environment
    • Its broad set of abstractions may exceed the needs of a narrowly scoped app
    • Tracing, evaluation, and deployment workflows are associated with separate LangSmith

  • LangGraph — where it wins

    • Combines deterministic code and LLM-driven steps in graph workflows
    • Supports durable execution, checkpointing, and resumption after failures
    • Offers memory, streaming, and human review of agent state

    Where it doesn't

    • Low-level control requires developers to define workflow behavior
    • Does not prescribe a high-level agent architecture
    • Verified integrations are narrower than LangChain’s

More comparisons

All LLM application development frameworks comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update