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LangGraph vs LangChain: Key Differences and When to Use Each

LangGraph vs LangChain: Key Differences and When to Use Each

Q: 9. LangGraph vs LangChaim, Which Is Better for RAG Applications?

Both frameworks support RAG applications.LangChain is a good choice for standard RAG applications that retrieve information and use it to generate answers.LangGraph is useful when a RAG workflow needs extra steps, such as checking results, trying another search, or requesting human review.

By Sriram

Updated on Nov 01, 2026 | 9 min read | 3.83K+ views

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LangGraph vs LangChain: Key Differences
What Is LangChain?
What Is LangGraph?
LangGraph vs LangChain: Which One Should You Choose?

Quick Insights:

The main difference between LangGraph vs LangChain is how they manage tasks in an AI application. Both frameworks help developers build AI applications but they work in different ways.
LangChain is an open source framework for building applications that use large language models (LLMs).
LangGraph builds on LangChain. It helps developers manage AI tasks with multiple steps and gives them more control over how those tasks run.
This blog covers the key differences between LangGraph vs LangChain, their features, common use cases, and when to choose each framework.

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LangGraph vs LangChain: Key Differences

LangChain and LangGraph are different from each other in how they build and manage AI workflows. The following table compares their key features, workflow structure, and use cases.

Aspect	LangChain	LangGraph
Architecture and workflow	Runs AI tasks in a set order.	Lets AI tasks follow different paths and repeat steps when needed.
Loops and decisions	Supports common agent workflows and loops.	Gives more control over loops and decisions.
State management	Helps manage chat history and information an agent needs.	Keeps track of workflow data and saves progress.
Human involvement	Allows human approval with the right setup.	Can pause a workflow for human review and continue after approval.
Retries and recovery	Supports retries and error handling.	Can retry failed steps and resume work from saved checkpoints.
Multi-agent support	Supports agent systems that use tools and work with other agents.	Helps manage multiple agents and coordinate their tasks.
Learning curve	Usually easier to learn and get started with.	Takes more time to learn because it offers more control.
Best use cases	Chatbots, RAG applications, document summaries, and simple LLM tasks.	Complex AI agents and workflows that need repeated steps or human approval.

LangChain and LangGraph work well together, but they work for different purposes. LangChain gives tools to build AI applications and LangGraph gives developers more control on how each step runs.

Also Read: Difference Between Agentic RAG and Agentic AI and LangSmith vs Langfuse

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What Is LangChain?

LangChain is an open-source framework for building applications that use large language models (LLMs). It gives developers ready made tools to connect AI models with data, prompts, and external tools.

Key Features of LangChain

Model integration: Connects different AI models like OpenAI, Anthropic, and Google.
Prompt management: Helps developers create and organise instructions given to AI models.
Tool integration: Allows AI applications to access external tools, APIs, and databases.
Information retrieval: Helps applications find relevant information from documents and data sources to answer questions.
Ready made agent setup: Provides a starting point for building AI agents that can choose tools and complete tasks.

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Here are some common ways developers use LangChain to build AI applications:

LangChain features and use cases, including prompt chaining, RAG applications, tool integration, and AI agents.

Common LangChain Use Cases

Chatbots: Build chatbots that answer user questions in a conversational way.
RAG applications: To build AI tools that retrieve useful information from documents or knowledge bases before answering questions like RAG agents.
Document summarisation: Turn long reports, articles, and documents into shorter summaries.
AI agents: Make AI applications that use tools to complete tasks, like searching for information or querying a database using SQL.
Semantic search:To help users find relevant information based on meaning, even when their search words do not match the content.

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What Is LangGraph?

LangGraph is an open source framework for building AI agents that can handle tasks with many different steps. It gives developers more control on what happens at each step and what the AI should do next. It is very useful when a task needs repeated steps, human approval, or saved progress.

Key Features of LangGraph

Flexible workflows: Lets developers decide the order of tasks. AI can follow different paths or return to a step it took before.
State management: Tracks information of workflow, so the AI can use previous results in next steps.
Saved progress: Can save workflow progress and resume from a saved point if a task is interrupted.
Human review: Allows a workflow to pause for human feedback or approval before continuing.
Control over AI tasks: Lets developers combine fixed steps with tasks where the AI makes decisions.

Read About: LangGraph Tools

These features show how LangGraph helps developers manage more complex AI workflows:

LangGraph features, including graph structure, saved progress, branching logic, and multi-agent coordination.

Common LangGraph Use Cases

Multi step AI agents: Build agents that work through several steps to complete a task, such as researching a topic and preparing a report.
Human-reviewed workflows: Create systems that ask for approval before taking an important action.
Multi agent systems: Build workflows where many AI agents handle different parts of a task.
Custom RAG applications: Build question-answering systems that need more control over how information is retrieved and used.
Long-running AI tasks: Build workflows that save progress and continue later instead of starting again after an interruption.

Also read: Agentic AI vs Generative AI: Key Differences

LangGraph vs LangChain: Which One Should You Choose?

The right choice depends on how your AI application needs to work. LangChain is a good starting point for standard AI tasks, while LangGraph is useful when you need more control over each step.

When to Choose LangChain

Choose LangChain when you want to build an AI application quickly using ready made tools and agent patterns.

You need to build a chatbot or a RAG application.
Your AI application follows a standard workflow, such as searching for information and summarising it.
You want ready-made integrations for AI models, databases, and external tools.

When to Choose LangGraph

Choose LangGraph when your AI application needs to handle more complex tasks and you want greater control over how each step works.

Your workflow needs to make decisions, follow different paths, or repeat steps.
Your application needs to pause for human approval and continue afterwards.
Your AI agent needs to save its progress and resume a task later.

Also Read: Difference Between LLM and Agentic AI

Can I Use LangChain and LangGraph Together?

Yes, you can use both frameworks together. LangChain provides tools, model integrations, and ready-made agent patterns and LangGraph lets you control how different steps fit together in a workflow. You can also use LangGraph on its own when you need more control over AI application.

Infographic showing how LangChain and LangGraph work together in an AI customer support workflow.

Frequently Asked Questions (FAQs)

1. LangChain vs LangGraph: Which Is Better?

We can not say which is better because the right choice depends on what we want to build.

Choose LangChain to build AI applications quickly using ready-made tools and agent features.
Choose LangGraph when you need more control over complex workflows, repeated steps, and decisions.
You can also use both together.

2. What Is the Main Difference Between LangGraph vs LangChain?

LangChain provides tools and ready-made features for building AI applications. LangGraph helps developers control how the different steps of an AI workflow run, including when tasks should repeat or follow different paths.

3. What Is LangChain Used For?

LangChain helps developers build applications that use large language models (LLMs). Common uses include:

Building chatbots and AI agents
Creating RAG applications
Summarising documents
Finding information using semantic search
Connecting AI models to external tools and databases

4. What Is LangGraph Used For?

LangGraph is used to build and manage AI workflows that involve multiple steps and decisions. Common uses include multi-step AI agents, workflows that need human approval, and applications that must save progress and resume later.

5. Can LangChain and LangGraph Be Used Together?

Yes. LangChain provides tools, model integrations, and agent features, while LangGraph gives developers more control over how workflows run. LangChain's agent implementations also use LangGraph building blocks, so the two frameworks can work together.

6. Is LangChain Easier to Learn Than LangGraph?

Generally, yes. LangChain offers ready-made components and agent features that help beginners get started. LangGraph requires a better understanding of workflow steps, state, and how tasks connect, so it may take longer to learn.

7. Can LangChain Build Complex AI Agents?

Yes. LangChain supports agents that use tools and perform tasks in multiple steps. When developers need more direct control over workflow decisions, branching, and state, they can build custom workflows using LangGraph.

8. Can LangGraph Work Without LangChain?

Yes. LangGraph can be used on its own to build AI workflows. Developers can also use LangChain components within a LangGraph workflow when they need features such as model integrations, prompts, or data retrieval.

9. LangGraph vs LangChaim, Which Is Better for RAG Applications?

Both frameworks support RAG applications.

LangChain is a good choice for standard RAG applications that retrieve information and use it to generate answers.
LangGraph is useful when a RAG workflow needs extra steps, such as checking results, trying another search, or requesting human review.

10. Which Is Better for Building Multi-Agent Systems?

LangGraph is useful when developers need detailed control over how multiple AI agents work together, share information, and pass tasks between them. LangChain also provides agent features and patterns that can be used in multi-agent applications.

11. How Do LangGraph and LangChain Differ in State Management?

State refers to the information an AI workflow needs to keep track of while completing a task.

LangChain: Its agents support state and conversation history, using LangGraph capabilities in their implementation.
LangGraph: Gives developers direct control over workflow state and checkpoints, which can help save progress and resume tasks.

12. Can LangGraph Pause a Workflow for Human Approval?

Yes. LangGraph can pause a workflow so a person can review information, approve an action, or provide input. The workflow can then resume from its saved state.
This is useful for tasks such as reviewing an AI-generated response before sending it or approving an action before it is completed.

13. Do LangChain and LangGraph Work With Different LLM Providers?

Yes. Both can work with models from different providers. The available options depend on the model integrations and libraries used in the application, rather than the choice between LangChain and LangGraph alone.

14. Do LangChain and LangGraph Affect LLM Performance?

Neither framework changes a language model's underlying capabilities. However, the way an application uses a model can affect response time, cost, and answer quality. For example, extra model calls or repeated retrieval steps can increase processing time and cost.

15. Should Beginners Learn LangChain or LangGraph First?

Beginners can start with LangChain to learn how AI models, prompts, tools, and data retrieval work together. Once they understand the basics, they can learn LangGraph to build workflows that need more control over steps, decisions, and saved progress.

Sriram

695 articles published

Sriram K is a Senior SEO Executive with a B.Tech in Information Technology from Dr. M.G.R. Educational and Research Institute, Chennai. With over a decade of experience in digital marketing, he specia...

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