HomeCompareLangChain vs LangGraph
Framework Comparison
LangChainVSLangGraph

Which AI Agent Framework Should You Choose?

A detailed comparison of LangChain and LangGraph — features, learning curve, use cases, community, and which has more agencies building with it.

163 LangChain agencies50 LangGraph agencies
163
LangChain Agencies
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VS
50
LangGraph Agencies
Browse →

Side-by-Side Comparison

LangChain
LangGraph
Type
General-purpose LLM/agent framework
Graph-based stateful agent orchestration
Language
Python & JavaScript
Python (built on LangChain)
Learning Curve
Moderate
Moderate-High — requires understanding graph concepts
Best For
Chains, RAG, simple agents
Complex, stateful, long-running agentic workflows
Multi-agent Support
Limited — workarounds needed
First-class — nodes as agent roles
Production Readiness
Very High
High — production-ready with LangGraph Cloud
Community Size
Very large (90k+ GitHub stars)
Medium-large (6k+ stars, growing fast)

When to choose LangChain

  • Your workflow is primarily linear — input, retrieval, LLM call, output — without cycles or conditional branching.
  • You need JavaScript/TypeScript support that LangGraph doesn't offer.
  • You're building RAG applications, chatbots, or simple tool-using agents without complex state management.
  • Your team is already proficient in LangChain and the added complexity of graphs isn't justified.
  • Rapid prototyping is a priority — LangChain's LCEL (LangChain Expression Language) chains are faster to write for simple use cases.
Find LangChain Agencies →

When to choose LangGraph

  • Your agent needs to loop, backtrack, or take conditional paths based on intermediate results — LangGraph handles cycles natively.
  • You need persistent state across steps, with checkpointing and the ability to resume interrupted workflows.
  • You require human-in-the-loop patterns where execution pauses for human review before proceeding.
  • You're building a complex multi-agent system where each agent is a node and control flow between them matters.
  • You need fine-grained control over how state is passed, updated, and persisted between agent steps.
Find LangGraph Agencies →
Frequently Asked Questions
Is LangGraph a replacement for LangChain?+

No — LangGraph is built on top of LangChain, not a replacement. Think of LangChain as the foundation (LLM integrations, retrievers, tools) and LangGraph as a higher-level orchestration layer for workflows that require cycles, state, and conditional branching. An AI agent development company experienced in LangGraph will still use LangChain primitives underneath. You only need to add LangGraph when your workflow complexity demands it.

When should I hire LangGraph developers instead of LangChain developers?+

Hire LangGraph developers when your agent needs to maintain state across many steps, loop back based on evaluation results, or implement human-in-the-loop checkpoints. If your current LangChain chains are getting unwieldy with nested conditionals, an AI agent agency specializing in LangGraph can refactor your workflow into a clean, maintainable graph. For straightforward RAG or single-pass agents, plain LangChain expertise is sufficient.

What is LangGraph Cloud and do I need it?+

LangGraph Cloud is a managed hosting and deployment service for LangGraph agents that provides built-in checkpointing, scalability, and a debugging UI. An AI agent agency building long-running or mission-critical agents will often recommend LangGraph Cloud to reduce operational overhead. If you're self-hosting or have strict data residency requirements, you can run LangGraph open-source on your own infrastructure instead.

How does LangGraph handle long-running agentic tasks?+

LangGraph's checkpointing system persists the full graph state to a backend (SQLite, Postgres, Redis) after each step. This means a long-running workflow can be interrupted, resumed, or rewound to any prior state. This is a critical feature for production agentic systems — any serious AI agent development company building workflows that run for minutes or hours should be evaluating LangGraph's persistence model during architecture design.

Can a single AI agent agency deliver both LangChain and LangGraph?+

Yes, and they should. Because LangGraph is built on LangChain, any competent AI agent agency offering LangGraph development should have deep LangChain expertise as well. When evaluating agencies, ask how they decide when to introduce LangGraph into a LangChain project — a strong answer will center on workflow complexity, state requirements, and the need for cycles rather than defaulting to one framework for every project.

Find LangChain and LangGraph Agencies

Ask prospective LangGraph agencies to walk you through their state schema design process — how they model state, decide on checkpointing backends, and handle failures mid-graph. Weak agencies treat LangGraph as just a fancier chain; strong AI agent agencies use it to build genuinely resilient, resumable workflows that hold up in production.

Which has more agencies?

In our directory, there are currently 163 LangChain agencies and 50 LangGraph agencies. LangChain leads the directory — reflecting its longer history and broader ecosystem adoption. However, LangGraph agency numbers are growing as the framework matures.

163 LangChain Agencies →50 LangGraph Agencies →

Bottom line

LangGraph is what you reach for when LangChain's sequential chain model no longer fits your use case. If your agent needs to loop, maintain rich state, or coordinate complex multi-step workflows with conditional branching, LangGraph is the natural evolution. For most RAG and simple agent use cases, plain LangChain is simpler and faster to ship.

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