LangChain vs LangGraph: Which One Should You REALLY Use?

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In the world of AI development, creating applications powered by large language models (LLMs) has become a key focus. Two names that frequently appear are LangChain and LangGraph. While they come from the same family, they serve distinct purposes.

LangChain provides the essential tools for building LLM-powered apps, while LangGraph offers a specialised way to construct more controlled and complex agentic systems. Understanding the difference between LangChain vs LangGraph is vital for any developer looking to build the next generation of AI solutions.

This article will break down both frameworks. We will look at their core features, explore their main differences, and give clear guidance on when you should choose one over the other for your projects.

What is LangChain?

LangChain is a software framework designed to make it easier to build applications that use large language models. Launched in October 2022 by Harrison Chase, it began as an open-source project that quickly gained huge popularity among developers. The project attracted hundreds of contributors on GitHub and saw significant investment, including a $10 million seed round and a later funding round that valued the company at over $200 million.

At its heart, LangChain simplifies connecting LLMs to other data sources and computational tools. It acts as a bridge, allowing you to create applications that can reason about the world and perform complex tasks like document analysis, code generation, and creating advanced chatbots.

Core Features of LangChain

LangChain's power comes from its flexible and modular design. It provides a set of building blocks that developers can piece together to create customised AI workflows.

Modular Architecture: LangChain is built on the idea of modularity. Developers can mix and match various components like language model interfaces, data loaders, and output parsers. This allows for great flexibility, letting you swap out a model or a data source without rebuilding the entire application.

Extensive Integrations: The framework boasts over 600 integrations with a wide range of models, databases, APIs, and other tools. This means you can easily connect your application to the services you already use with minimal engineering effort.

Chains: A core concept in LangChain is the “chain.” Chains allow you to link together a sequence of calls, whether to an LLM or another utility. The LangChain Expression Language (LCEL), introduced in 2023, provides a clear, declarative way to compose these chains.

Agents: LangChain enables the creation of agents, which are systems that use an LLM to decide on a sequence of actions to take. The LLM acts as the reasoning engine, figuring out which tools to use to accomplish a goal.

Memory Management: For applications like chatbots, context is key. LangChain includes robust features for memory management, allowing agents to remember and refer to previous parts of a conversation.

Prompt Engineering Tools: It offers tools to help manage and optimise prompts. This includes prompt templates that help structure the input sent to an LLM, leading to more consistent and reliable responses.

LangChain's main strength lies in its versatility. It gives developers a comprehensive toolkit to build and experiment with all kinds of LLM-powered applications, from simple question-answering bots to more intricate systems that interact with external data.

What is LangGraph?

LangGraph is a library that extends the capabilities of the LangChain ecosystem. It is specifically designed for building stateful, multi-agent applications. While LangChain is great for creating sequences of actions (chains), LangGraph introduces a more powerful way to control the flow of logic, especially for complex tasks. It was created to help developers add more precision and control into their agentic systems, making them more reliable for real-world use.

The core idea behind LangGraph is to represent workflows as a graph, composed of nodes and edges. This structure allows for more sophisticated control flows than the linear chains typically found in LangChain. It is inspired by technologies like Apache Beam and NetworkX.

Core Features of LangGraph

LangGraph offers a structured approach to building agents, which makes complex interactions easier to manage and debug.

Graph-Based Workflows: Instead of a simple line of steps, LangGraph organises tasks as a graph. The nodes in the graph represent components like an LLM or a function, while the edges define how data and control flow between them. This visual representation makes it easier to understand and manage complex interactions.

Cyclical Graphs: A key feature that sets LangGraph apart is its support for cycles. This means the workflow is not restricted to moving in one direction. It can loop back, repeat steps, or make decisions based on previous outcomes. This is essential for tasks that require iteration, like refining a piece of code or conducting multi-step research.

State Management: LangGraph has robust, built-in state management. The state of the application is passed between the nodes in the graph and can be updated at each step. This persistent state allows for features like pausing and resuming a task or maintaining a detailed history of a conversation.

Human-in-the-Loop: The ability to create cycles and manage state makes it easy to incorporate human intervention. You can design workflows that pause at a certain point and wait for a human to review, approve, or provide input before continuing. This is vital for applications in customer support or other sensitive areas.

Seamless Integration: LangGraph is not a replacement for LangChain but an extension of it. It integrates smoothly with LangChain components and works with LangSmith for detailed monitoring, debugging, and tracing of your agent's performance.

LangGraph is the tool of choice when you need to build agents that can handle complicated logic, collaborate with other agents, or require human oversight.

LangChain vs. LangGraph: Key Differences

While LangChain and LangGraph work together, they are designed for different kinds of problems. The main difference lies in their approach to structuring and controlling an application's workflow.

Feature	LangChain	LangGraph
Framework Type	A flexible and modular framework for building a wide range of LLM-based applications.	A specialised library for orchestrating complex, stateful agent workflows using a graph structure.
Control Flow	Primarily linear, using “chains” to execute a sequence of steps. Control flow is often managed by the LLM itself in agents.	Cyclical and graph-based, allowing for loops, conditional branching, and explicit control over the workflow.
State Management	Memory components must be explicitly configured and managed within the application's logic.	Features built-in, persistent state management where the state is passed between nodes in the graph.
Development Complexity	The flexibility can lead to a steeper learning curve when orchestrating complex, multi-step logic manually.	Simplifies the development of complex logic by making the flow explicit and visual through the graph structure.
Core Use Case	Rapid prototyping, building standard applications like RAG and chatbots, and integrating various components.	Building reliable multi-agent systems, workflows needing iteration, and applications requiring human-in-the-loop control.
Ease of Use	Generally easier for simple, linear applications but can become complex to manage as workflows grow.	More intuitive for designing and debugging complex, non-linear workflows with many decision points.

LangChain provides the fundamental building blocks, while LangGraph provides a more advanced structure for orchestrating those blocks into reliable, controllable agents.

When to Use LangChain

LangChain remains the go-to framework for a wide variety of LLM application development tasks. Its strength is its flexibility and its huge library of integrations.

You should choose LangChain when:

Building simple, linear workflows: If your application follows a straightforward sequence of steps—for example, taking user input, formatting it with a prompt, sending it to an LLM, and parsing the output—LangChain is perfect.

Rapid prototyping and experimentation: LangChain's modular design makes it ideal for quickly testing different models, prompts, or data sources. You can easily swap components to find the best combination for your needs.

Developing standard RAG applications: For most Retrieval-Augmented Generation (RAG) use cases, where you retrieve documents and feed them to an LLM for context, a standard LangChain chain is often sufficient.

You need broad integrations: If your project relies on connecting to many different APIs, databases, or vector stores, LangChain's library of over 600 integrations is a massive advantage.

You are creating foundational tools: When you are building the individual tools or components that an agent might use (like a function to search the web or query a database), LangChain provides the wrappers to make these components easily accessible to an LLM.

In short, if your application's logic is relatively straightforward and can be represented as a sequence, LangChain provides the quickest and most flexible path to a solution.

When to Use LangGraph

LangGraph shines when the complexity of the task moves beyond a simple linear sequence. It is designed for scenarios where control, reliability, and statefulness are critical.

You should choose LangGraph when:

Building complex, multi-agent systems: If your application involves multiple agents that need to collaborate, delegate tasks, or review each other's work, LangGraph provides the structure to manage these interactions effectively.

Your workflow requires cycles or iteration: For tasks that need to be repeated until a certain condition is met, LangGraph's support for cycles is essential. Examples include an agent that writes code, tests it, and then refines it based on the test results, or a research agent that gathers information iteratively.

You need human-in-the-loop control: Any application that requires a human to approve a step, edit a result, or provide guidance will benefit from LangGraph. The ability to pause and resume the graph makes this integration natural.

Creating highly reliable and controllable agents: When you cannot afford for an agent to fail silently or go down the wrong path, LangGraph allows you to define the exact flow of logic with explicit conditions and branches.This moves away from letting the LLM have complete control and adds a layer of deterministic logic.

Developing advanced, stateful chatbots: For conversational agents that need to handle complex, multi-turn dialogues with branching paths and a deep memory of the conversation, LangGraph's state management is a powerful tool.

LangGraph is for when you are moving from a prototype to a production-grade agent that needs to perform complex tasks reliably and predictably.

How LangChain and LangGraph Work Together

It is vital to understand that the choice is not always “either/or.” LangGraph is part of the LangChain product suite and is designed to work with LangChain's components. They form a powerful combination for building sophisticated AI systems.

A common development pattern is to use:

LangChain to create and wrap the individual tools your agent will use. For example, using LangChain's integrations to build a tool for searching a specific database or another tool for calling an external API.
LangGraph to define the high-level logic that orchestrates how and when these tools are used. The graph structure would define the decision-making process, handle the state, and manage any necessary loops or human interventions.
LangSmith to monitor, debug, and evaluate the entire system. LangSmith is framework-agnostic and provides visibility into every step of your application, whether it is built with LangChain chains or a LangGraph graph.

This layered approach allows you to leverage the strengths of both frameworks: LangChain for its vast integrations and component library, and LangGraph for its robust control and orchestration capabilities.

Conclusion

Choosing between LangChain and LangGraph comes down to the complexity and control requirements of your AI application.

LangChain is your versatile toolkit. It's the ideal choice for building a wide array of LLM-powered applications quickly, especially those with a linear flow. Its strength lies in its modularity and extensive integrations.

LangGraph is your specialised director. It's built for creating complex, stateful, and reliable agentic systems. When your application needs loops, multi-agent collaboration, or human oversight, LangGraph provides the necessary structure and control.

As AI agents become more capable, the need for precision and reliability will only grow. While LangChain provides the essential building blocks, LangGraph offers the framework to assemble those blocks into robust, production-ready systems. By understanding the unique strengths of each, developers can select the right tool for the job and build more powerful and dependable AI solutions.