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LangChain vs LangGraph: Choosing the Right Framework for Your AI Project

Kumar Gauraw
LangChain-Vs-LangGraph-Choosing-One-Vs-Other

If you’ve been building AI applications with large language models or LLMs, you’ve probably encountered LangChain just as I did and everyone else does—the popular framework that has become such an important part of Agentic AI development since its launch in 2022. But as you may have already realised, there’s a new player in the game: LangGraph, launched in early 2024 by the same team behind LangChain.

We are going to discuss and compare LangChain and LangGraph in this post and see which one is a better fit for different real-world situations. While these frameworks share DNA, they solve fundamentally different problems. In this comprehensive guide, we’ll explore what makes LangGraph unique, how it differs from LangChain, and most importantly, which one you should choose for your next AI project.

The Evolution: From Chains to Graphs

To understand LangGraph, we first need to understand what LangChain couldn’t do. Please feel free to refer to my earlier post about LangChain if you are not yet fully familiar with that framework or would like to refresh your understanding before moving forward with this post.

LangChain: The Linear Orchestrator

LangChain revolutionized how developers build LLM applications by providing modular building blocks that could be “chained” together. Think of it as a conveyor belt where data flows from one step to the next in a predictable sequence:

  1. Retrieve data from a database
  2. Summarize the content
  3. Answer the user’s question

This linear, Directed Acyclic Graph (DAG) approach works brilliantly for straightforward workflows where each step follows the previous one. LangChain excels at:

LangChain is reactive. It moves in the flow of execution in a linear fashion based on the output of the previous step for the most part. But what happens when your AI application needs to:

This is where LangChain’s linear architecture starts to show its limitations—and where LangGraph enters the scene.

What is LangGraph?

LangGraph is a stateful orchestration framework built on top of LangChain for creating complex, non-linear AI workflows. Instead of thinking in chains, LangGraph models applications as directed graphs where:

Launched in early 2024, LangGraph quickly became the go-to framework for building production-ready AI agents. Companies like LinkedIn, Replit, Elastic, and Uber have successfully deployed LangGraph-based systems that handle millions of interactions.

The Core Philosophy

LangGraph was designed with lessons learned from LangChain’s real-world deployments. The team identified three critical needs that weren’t well-served by linear chains:

  1. Cycles and Loops: Real AI agents need to iterate, retry, and refine their approach
  2. State Management: Complex applications need to remember context across steps and sessions
  3. Human-in-the-Loop: Production systems need human oversight at critical decision points

LangChain vs LangGraph — Key Differences

A concise comparison focused on architecture, state, control flow, use cases, and learning curve.

LangChain vs LangGraph — Key Differences

A concise comparison focused on architecture, state, control flow, use cases, and learning curve.

Aspect LangChain LangGraph
Workflow Architecture Linear Chains (DAG)
  • One-way flow: start → finish
  • Steps depend on previous output
  • Great for predictable, sequential processes
  • Difficult to implement loops or backtracking
 Cyclical Graphs
  • Supports loops, branches, conditional flows
  • Nodes can be revisited multiple times
  • Dynamic routing based on runtime conditions
  • Natural fit for agent-like behaviors
State Management Implicit State
  • Data passes automatically between chain links
  • Memory must be manually wired between steps
  • Requires external memory classes for persistence
  • Limited persistence across sessions
 Explicit State
  • Centralized state object accessible to all nodes
  • Built-in persistence and checkpointing
  • State can be inspected, modified or rolled back
  • Long-term memory across sessions
Control Flow Sequential 
# LangChain example
chain = (
    retrieval_chain
    | summarization_chain
    | answer_chain
)
result = chain.invoke(query)
 Graph-Based 
# LangGraph example
graph = StateGraph(AgentState)
graph.add_node("retrieve", retrieve_data)
graph.add_node("summarize", summarize_data)
graph.add_node("answer", generate_answer)
graph.add_conditional_edges(
    "retrieve",
    should_retry,
    {"retry": "retrieve", "continue": "summarize"}
)
Use Case Focus Rapid Prototyping
  • Fast development cycles
  • Simple, predictable workflows
  • Quick demos and MVPs
  • Single-pass operations
 Production Agents
  • Complex, long-running processes
  • Multi-agent coordination
  • Robust error handling
  • Enterprise-scale deployments
Learning Curve Beginner-Friendly
  • Intuitive modular design
  • Straightforward chain composition
  • Extensive docs & examples
  • Lower barrier to entry
 Advanced but Powerful
  • Requires understanding of state machines
  • Graph-based thinking takes time to master
  • More complex initial setup
  • Greater control and flexibility

Tip
Use LangChain for quick prototypes and single-pass LLM workflows. Choose LangGraph when you need persistent state, loops, or production-grade agent orchestration.

 

LangGraph’s Powerful Features

Built-in State Persistence

LangGraph automatically manages state persistence, meaning your agent can:

Human-in-the-Loop Workflows

Production AI systems need human oversight. LangGraph makes it easy to:

Multi-Agent Orchestration

LangGraph excels at coordinating multiple AI agents:

Streaming and Real-Time Updates

Unlike batch processing, LangGraph supports:

When to Choose LangChain

Choose LangChain when you need:

✅ Quick Prototyping

✅ Simple, Linear Workflows

✅ Straightforward Applications

✅ Learning LLM Development

Example Use Cases:

When to Choose LangGraph

Choose LangGraph when you need:

✅ Complex Agent Systems

✅ Stateful Applications

✅ Non-Linear Workflows

✅ Multi-Agent Coordination

✅ Production-Grade Reliability

Example Use Cases:

Real-World Success Stories

LinkedIn: SQL Bot

LinkedIn deployed an internal AI assistant called SQL Bot that transforms natural language questions into SQL queries. Built on LangGraph, it:

Replit: AI Coding Agent

Replit’s agent, built with LangGraph, serves millions of users by:

Uber: Code Migration at Scale

Uber’s Developer Platform AI team uses LangGraph for:

The Best of Both Worlds

Here’s an important insight: It’s not LangChain vs LangGraph—it’s LangChain AND LangGraph.

These frameworks are designed to work together:

  1. Use LangChain components within LangGraph
    • Leverage LangChain’s 700+ integrations
    • Use chains, tools, and prompts as LangGraph nodes
    • Build on existing LangChain expertise
  2. Start with LangChain, evolve to LangGraph
    • Prototype quickly with LangChain
    • Migrate to LangGraph when complexity grows
    • Reuse your LangChain components
  3. Mix and match as needed
    • Use LangChain for simple subsystems
    • Use LangGraph for complex orchestration
    • Choose the right tool for each part of your application

Decision Framework: A Practical Guide

Ask yourself these questions:

Question 1: Does your workflow need loops or retries?

Question 2: Do you need to maintain context across sessions?

Question 3: Will multiple AI agents need to coordinate?

Question 4: Is this a production system with real users?

Question 5: Does the workflow have conditional branching?

Question 6: What’s your team’s experience level?

The Future of LangChain and LangGraph

Both frameworks are actively evolving:

LangChain’s Roadmap

LangGraph’s Roadmap

Recent LangGraph Updates (2024-2025):

Complementary Tools in the Ecosystem:

LangSmith

Monitor, evaluate, and debug your applications

LangGraph Platform

Deploy and scale LangGraph applications

LangGraph Studio

Visual development environment

Migration Path: From LangChain to LangGraph

If you’ve built something with LangChain and need LangGraph’s capabilities:

  1. Identify pain points: Where are you manually managing state or implementing workarounds?
  2. Map your workflow: Convert your chain into a graph with nodes and edges
  3. Define state schema: What information needs to persist?
  4. Reuse LangChain components: Your chains, tools, and prompts can become nodes
  5. Add graph features: Implement loops, conditional edges, and state management
  6. Test incrementally: Migrate one section at a time

Conclusion: Which Should You Choose?

The answer isn’t one-size-fits-all. Here’s the TL;DR:

Choose LangChain if:

Choose LangGraph if:

Use Both when:

Remember: LangGraph is not replacing LangChain—it’s extending it for more sophisticated use cases. As your AI applications grow in complexity, LangGraph provides the infrastructure to build reliable, maintainable, and scalable agent systems.

The future of AI development is agentic, and both LangChain and LangGraph are evolving to support that future. Choose the framework that matches your current needs, but don’t be afraid to evolve as your requirements grow.

Ready to get started? Here are a few resources for your reference on these topics:

What’s your experience with LangChain and LangGraph? Are you building agents for production? Share your thoughts in the comments!

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