If you’re building applications with large language models, you’ll likely come across two frameworks: LangChain and LangGraph. Both are open source, both are powerful, and both are designed to help developers create LLM-powered applications. But they’re not the same. So what’s the difference, and when should you use one over the other?
Let’s start with LangChain.
LangChain: Chaining LLM Operations
LangChain is built around the idea of chaining operations. At its core, it’s a framework for executing a sequence of functions in a chain. Think of it as a pipeline where each step depends on the output of the previous one.
For example, imagine you’re building an application that needs to retrieve data from a website, summarize it, and then answer user questions based on that summary. LangChain helps you break this down into three steps: retrieve, summarize, and answer.
To retrieve data, you might use a LangChain component called a document loader. This fetches content from various sources. If the documents are large, you might use a text splitter to break them into smaller, meaningful chunks.
For summarization, you’d use a chain that orchestrates the process. This might involve constructing a prompt to instruct the LLM and passing the request to the model. The answer step would involve another chain, possibly with a memory component to store conversation history and context, along with another prompt and LLM to generate the final response.
One of the strengths of LangChain is its modularity. You can mix and match components to build complex workflows. For instance, the LLM used for answering questions might be entirely different from the one used for summarization. This flexibility makes it a great choice for applications where you know the exact sequence of steps needed.
LangGraph: Stateful, Nonlinear Workflows
LangGraph, on the other hand, is designed for more complex, stateful workflows. It’s a specialized library within the LangChain ecosystem, tailored for building multi-agent systems that handle nonlinear processes.
Consider a task management assistant. The workflow here isn’t linear. It involves processing user input, adding tasks, completing tasks, and summarizing tasks. LangGraph models this as a graph structure, where each action is a node and the transitions between actions are edges.
The central node is the process input node, where user input is received and routed to the appropriate action node. There’s also a state component that maintains the task list across interactions. Nodes like “add task” and “complete task” modify this state, while the “summarize” node generates an overview of current tasks.
The graph structure allows for loops and revisiting previous states, making it ideal for interactive systems where the next step depends on evolving conditions or user input. This flexibility is what sets LangGraph apart. It’s designed for applications that need to maintain context over extended interactions, like virtual assistants or complex task management systems.
Comparing the Two
So how do LangChain and LangGraph stack up against each other? Let’s break it down.
Primary Focus
LangChain is about chaining LLM operations. It’s great for building applications where you know the sequence of steps needed. LangGraph, on the other hand, is designed for stateful, multi-agent systems with complex, nonlinear workflows.
Structure
LangChain uses a chain structure, or directed acyclic graph (DAG), where tasks are executed in a specific order. This works well for processes like retrieve → summarize → answer. LangGraph uses a graph structure that allows for loops and revisiting previous states, making it better for interactive systems.
Components
LangChain relies on components like memory, prompts, LLMs, and agents to form chains. LangGraph uses nodes, edges, and states to build graphs.
State Management
LangChain can pass information through the chain but doesn’t easily maintain persistent state across multiple runs. LangGraph, however, has robust state management. The state is a core component that all nodes can access and modify, enabling more complex, context-aware behaviors.
Use Cases
LangChain excels at sequential tasks, like retrieving data, processing it, and outputting a result. LangGraph is better suited for complex, adaptive systems that require ongoing interaction, such as virtual assistants that need to maintain context over long conversations.
Which Should You Use?
The choice between LangChain and LangGraph depends on what you’re building. If your application involves a clear sequence of steps, LangChain is likely the better choice. Its modular design and focus on chaining operations make it ideal for straightforward workflows.
But if you’re building something more complex — like a virtual assistant or a system that needs to handle multiple, interdependent tasks — LangGraph is the way to go. Its graph structure and robust state management make it perfect for applications that require flexibility and context awareness.
Both frameworks are powerful tools for building LLM applications. The key is to understand the strengths of each and choose the one that best fits your needs. Whether you’re chaining operations or navigating complex workflows, LangChain and LangGraph give you the tools to build something great.
Frequently Asked Questions
1. What is the core purpose of LangChain?
LangChain is a framework designed to simplify the creation of applications powered by Large Language Models (LLMs). It achieves this by allowing developers to chain together various functions and components in a sequential manner. This enables the building of workflows where tasks are executed in a defined order.
2. How does LangChain typically handle data flow within an application?
LangChain uses a chain structure to process data, which is essentially a sequence of function executions. Data moves through this chain, and each step modifies it or extracts new information. For example, data might be loaded from a website, then split into smaller chunks, then summarized, and then used to answer a question. This sequential processing is a core function of LangChain.
3. What is LangGraph’s primary focus compared to LangChain?
While LangChain focuses on chaining LLM operations in a specific sequence, LangGraph is primarily designed for creating stateful, multi-agent systems. It allows for the development of workflows that are more dynamic and interactive than traditional LangChain applications, by focusing on building graph-based systems.
4. What does a “graph structure” mean in the context of LangGraph?
In LangGraph, the graph structure refers to a network of nodes (representing actions) connected by edges (representing transitions between actions). This setup allows for loops and the ability to revisit previous states, meaning the system can handle diverse inputs and navigate through the workflow in a flexible way that isn’t as easy with a linear chain.
5. How does LangGraph handle state management differently from LangChain?
LangChain can pass information between steps in a chain but doesn’t inherently maintain a persistent state across runs. LangGraph, on the other hand, has robust state management as a core component. Each node in a LangGraph application can access and modify the state, which allows for complex, context-aware behaviors that evolve throughout the interaction.
6. What are some typical use cases where LangChain excels?
LangChain is well-suited for applications involving sequential tasks like retrieving data, processing it, and generating outputs. Examples include summarization tools, text analysis pipelines, or question-answering systems with well-defined, linear processes.
7. What kind of applications is LangGraph better suited for?
LangGraph is ideal for complex systems that require continuous interaction and adaptation. This includes applications like virtual assistants that need to maintain conversation context over extended periods and manage various user requests. Its stateful nature and flexible graph structure make it suitable for these scenarios where the flow of the application is not linear.
8. If an application needs to remember past interactions and use that information in the future, which framework, LangChain or LangGraph, would be better and why?
LangGraph is the better choice for applications that need to maintain context and remember past interactions. This is because of its state management, where the state is a core component that all nodes can access and modify. While LangChain can pass data through the chain, it doesn’t have a persistent state feature, making it less suitable for applications requiring memory across multiple runs or dynamic workflow adjustments based on past interactions.
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