LangChain vs. LangGraph: Understanding the Differences and When to Use Each

Key takeaways

• The biggest difference between LangChain and LangGraph lies in their applications: LangChain is best suited for developing large language models (LLMs) and integrating them with external tools, while LangGraph excels at building multi-agent, graph-based AI workflows.

• You can use LangChain for LLM tasks that follow a clear, predictable sequence, while you’ll use LangGraph for building complex agentic systems that require multi-step reasoning and iterative looping.

• Learn LangChain first if you’re a beginner to understand fundamental topics like modular LLM components, prompt chaining, and agentic workflows, and then move on to LangGraph.

While it’s not mandatory to use LangChain with LangGraph, using them together can help you combine LangChain’s powerful modular architecture with LangGraph’s dynamic agent orchestration features, providing the best of both worlds.

Discover what differentiates LangChain and LangGraph, and how to decide when to use each. If you’re ready to start learning how to build AI agents, enroll in the IBM RAG and Agentic AI Professional Certificate. You’ll have the opportunity to gain experience with designing and chaining AI tools with LangChain, as well as creating autonomous AI agents using LangGraph, in as little as eight weeks. Upon completion, you’ll have earned a career certificate for your resume.

LangChain overview

LangChain is an AI orchestration framework for building large language model (LLM)-powered applications. LangChain offers Python and JavaScript libraries for connecting LLMs with external application programming interfaces (APIs), search engines, and data sources. Its linear architecture provides a reliable interface for prompt chaining, enabling a clean, sequential pipeline of tasks. This allows developers to create logical workflows in which each step, whether calling APIs or generating text, passes its output to the next, creating a modular structure for rapid prototyping of chatbots and generative AI models.

LangChain use cases

LangChain’s architecture is best suited for linear pipelines that don’t have much looping or feedback. This makes it useful for applications like:

Retrieval-augmented generation (RAG)

You can use LangChain to create systems that fetch data from external knowledge bases to supplement an LLM’s responses, forming the basis of question-answer systems and chat interfaces that provide accurate and contextually relevant responses.

Chatbots

LangChain’s chain-based architecture makes it suitable for building chatbots and conversational systems that retain context and call APIs, search engines, knowledge bases, or other tools based on user input.

Document processing and analysis

You can use LangChain’s sequential structure to develop applications that interact with structured and unstructured data sources and summarize, analyze, classify, or translate large amounts of textual data from research articles, PDFs, reports, and other documentation.

Read more: What Is Agentic RAG? Learn About Retrieval-Augmented Generation in AI

Advantages of LangChain

LangChain offers a number of benefits for projects that have linear workflows:

Flexibility

LangChain supports various programming languages, LLMs, vector stores, and external tools, allowing you to repurpose domain-specific LLMs without retraining through RAG mechanisms. This makes it suitable for diverse development environments and natural language processing (NLP) tasks.

Simple and rapid AI development

LangChain’s step-by-step reasoning framework simplifies the process by breaking complex tasks into manageable steps. Additionally, by abstracting the integration complexities of LLMs, LangChain reduces application development time.

Community support

LangChain is open-source with a large community of developers skilled in the framework and several production-ready tools for RAG, text processing, and chatbot development.

LangGraph compatibility

LangChain’s new agent constructor uses LangGraph’s runtime to provide a prebuilt framework for fast and flexible AI agent development.

Can you use LangGraph without LangChain?

Yes, you can. Although LangChain runs on top of LangGraph’s runtime, you don’t necessarily need to know or use LangChain with LangGraph. You can use LangChain to integrate some components and tools into your workflow, if you wish, but it’s not essential. However, many developers prefer to use LangChain and LangGraph together to leverage LangChain’s modular architecture with LangGraph’s dynamic agent orchestration.

Limitations of LangChain

Despite its benefits in developing LLM applications, you need to be aware of certain challenges you may encounter when working with LangChain.

Debugging challenges

The abstraction layers can limit visibility in the pipeline, which can limit beginners’ understanding of the workflow, ultimately making it difficult to debug complex programs. This forces developers to devise custom or trial-and-error solutions to bypass the framework.

Managing large data sets

When dealing with large volumes of data, LangChain can struggle with efficient memory management, increased processing time, and a lack of native support for big data environments. This often necessitates that developers use batch processing, distributed computing resources, and customized data pipelines to address memory management, data processing, and data environment integration, respectively.

Latency and performance

LangChain’s sequential processing architecture that connects multiple operations with each step depending on a tool or API call, can impact computing and memory resources, increasing latency in large-scale programs and when making multiple requests back-to-back. Developers need to anticipate their application’s load and design their programs accordingly.

LangGraph overview

LangGraph, created by LangChain, is a very low-level orchestration framework for developing and deploying AI agents. LangGraph organizes tasks in a graph-like structure, where the “nodes” are individual functions and the “edges” define the next task's route based on the current state. This allows you to develop flexible AI agents that loop back, retain memory and context, and pause for human review and then pick up where they left off.

LangGraph use cases

LangGraph excels at complex workflows that require human-in-the-loop interventions, conditional branching, and multi-agent collaboration.

Multi-agent systems

LangGraph enables multiple AI agents to collaborate on tasks, where different domain-specific agents operate on a shared state. Agents can work in parallel on different tasks, loop back and optimize decisions, and employ intelligent task routing based on agent capabilities and workload.

Complex workflow automation

LangGraph also enables workflows that require agents to handle multi-step reasoning and conditional logic, such as autonomous AI assistants that can manage business processes, pause for human review, adapt their behavior, and learn context based on prior decisions.

Stateful systems

For business processes that span long periods, LangGraph can preserve state and decision-making context for long-running tasks, ensuring the agent remembers the outcomes of past interactions.

Advantages of LangGraph

LangGraph provides a low-level infrastructure for long-running workflows without abstracting the architecture, while maintaining both long and short-term memory.

Human-in-the-loop

You can program your agent to pause execution at a specific node and wait for human oversight, a feature valuable for enterprise-level applications that require compliance tracking.

Persistence

Agents can survive interruptions and failures and resume the same state after restarting, ensuring long-running workflows recover smoothly.

Visual debugging

LangGraph simplifies the debugging process by integrating with LangGraph Studio and LangSmith, which let you visualize the graph structure and track agent execution paths.

Limitations of LangGraph

LangGraph may require familiarity with graph-based programming, which increases the learning curve for teams without in-depth technical knowledge. Additionally, it might be too complex for smaller workflows that follow linear logic. Setting up a LangGraph multi-agent system requires careful planning and state management, which may make it less ideal for rapid prototyping.

LangChain vs. LangGraph: Comparison of key features

Both LangChain and LangGraph are open-source frameworks from the LangChain company for building AI models. While LangChain shines when developing LLMs and integrating them with external tools and workflows, LangGraph’s strength is in building multi-agent, graph-based AI workflows. Explore the differences between the two in detail below:

When to use LangGraph vs. LangChain

In general, you’ll use LangChain for LLM tasks that follow a clear, predictable sequence and one-time transformations, like fetching and summarizing text. Additionally, you can use LangChain if:

• You need fast prototyping and development of proof-of-concept projects, and require pre-built templates and connectors.

• You want a straightforward, easy-to-manage workflow that doesn’t require adaptive reasoning or complex agentic orchestration.

• Your project could benefit from LangChain’s extensive library of APIs, databases, and LLMs.

• Getting a quick output is more important than complex branching, like in document summarizers and simple chatbots.

In contrast, you’ll use LangGraph when you’re building a complex agentic system that requires multi-step reasoning, retries, conditional logic, and backtracking. Additionally, you’ll use LangGraph if:

• Your workflow requires multiple AI agents with differing roles to work in tandem.

• You need explicit state management to preserve context over multiple sessions, long-running tasks, and dynamic, multi-turn conversations.

• Your project requires complex decision-making agents that iterate through loops until a condition is met, human intervention checkpoints, and automatic retries.

• You’re building a production-grade enterprise system that needs reliability, transparency, control, and human oversight.

Should you learn LangChain or LangGraph first?

If you’re just starting out, learn LangChain first to understand the basics of modular LLM components, prompt chaining, and agentic workflows. Once you’ve grasped the fundamentals, it’ll be easier for you to learn LangGraph, which has a steeper learning curve for beginners. If you already have knowledge of graph-based programming and your project requires multi-agent workflows, you can directly start with LangGraph.

Getting started with LangChain and LangGraph

1. Build programming fundamentals.

Before you start learning LangChain or LangGraph, ensure you have strong programming skills and understand when to use each framework.

2. Learn LLM basics (prompt engineering, tool calls).

Then, you can move on to learning the basics of LLMs, tool calls, prompting, and simple agents.

3. Take an introductory course (e.g. Google Prompting Essentials).

Consider taking an online course, such as the Google Prompting Essentials Specialization for an introduction to LLM prompting, or the Generative AI with Large Language Models for a foundational understanding of how LLMs work

4. Advance with the IBM RAG and Agentic AI Certificate or LangChain Academy.

Once you’ve mastered the fundamentals, you can move on to more advanced topics, such as building agentic AI workflows. Consider the IBM RAG and Agentic AI Professional Certificate, which covers how to design and chain AI tools with LangChain and how to develop autonomous AI agents using LangGraph.

You could also explore LangChain’s official tutorials and step-by-step guidance on its LangChain Docs page, which offers structured guides on core concepts, such as context engineering and graph APIs. You could also browse LangChain Academy, where you’ll find foundational, quickstart, and hands-on project-like courses designed to help you build relevant skills in LangChain products.

Empower your growth with curated learning resources

Explore career paths, assess your skills, and connect with resume guidance while browsing our Career Resources Hub. Then, if you want to learn more about AI and AI agent systems, check out these free resources:

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