In the rapidly evolving world of Large Language Models (LLMs), building complex and dynamic AI applications requires more than just calling an OpenAI or Anthropic API. Developers need tools that help structure prompts, manage memory, call tools, handle multi-step reasoning, and coordinate multiple agents.
Two major frameworks that have emerged to help with this are LangChain and LangGraph. While they’re often used together, they serve different purposes.
In this post, we’ll break down:
What is LangChain?
What is LangGraph?
Key differences between LangChain and LangGraph
Real-world use cases
When to use which?
What is LangChain?
LangChain is a powerful framework designed to simplify the development of LLM-based applications. It provides a modular set of tools and abstractions to handle common tasks such as:
Prompt templating
Memory management
Chains: Sequencing multiple LLM calls or function executions
Tool usage (e.g., search, calculator, APIs)
Agents: LLMs that decide which actions to take
Document loading, splitting, embedding, and retrieval (RAG)
LangChain makes it easy to build and manage pipelines that involve multiple steps, like retrieving documents and generating summaries or handling conversations with context.
Example LangChain Use Case:
Building a customer support chatbot that:
Takes a question
Searches your documentation
Summarizes relevant results
Responds with a friendly, accurate answer
LangChain makes this linear flow straightforward with its chain and agent abstractions.
What is LangGraph?
LangGraph is a graph-based extension of LangChain that introduces a new programming paradigm: building stateful, multi-path applications using a graph of nodes and edges.
LangGraph is ideal for:
Multi-agent systems
Conditional workflows
Loops and branching
State management across steps
More control over execution flow
It uses a state machine architecture, where each node can be an LLM, tool, or even another chain. Nodes are connected based on logic or conditions, allowing flexible, dynamic flows.
LangGraph is especially helpful when building applications that aren't strictly linear — where decisions must be made based on previous steps, or where different agents interact.
Example LangGraph Use Case:
A document analysis system where:
Agent A reads the document
Agent B verifies facts
Agent C rewrites it in simple language
If Agent B finds inconsistencies, loop back to Agent A
LangGraph makes this non-linear flow manageable and readable using a visual and code-based graph abstraction.
LangChain vs LangGraph: Key Differences
When to Use LangChain
Use LangChain if you are:
Building your first LLM application
Creating linear flows like:
RAG pipelines (search ➝ summarize ➝ respond)
Prompt engineering experiments
Simple chatbots with memory
Wanting to quickly prototype with modular components
When to Use LangGraph
Use LangGraph if you are:
Building multi-agent systems (e.g., multiple LLM personas working together)
Creating apps that require looping, branching, or state-dependent decisions
Coordinating complex workflows (e.g., verifying steps, retrying failures)
Building systems where state must be shared across many steps or agents
Can You Use Them Together?
Yes — and in fact, LangGraph is built on top of LangChain. You can use LangChain to define your tools, chains, and agents, and then connect them using LangGraph to create a powerful stateful application.
For example:
Define a summarization chain using LangChain
Define a fact-checking agent using LangChain
Use LangGraph to orchestrate how they interact and loop until all steps pass
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