Introduction
The rise of large language models (LLMs) has led to the development of powerful frameworks for building AI-powered applications. Two of the most popular tools in this space are LangChain and LangGraph, both designed to streamline the creation of LLM-driven workflows.
But what’s the difference between them? And which one should you use for your next project?
In this post, we’ll compare LangChain vs. LangGraph, explore their key features, and help you decide which framework is best for your AI development needs.
What is LangChain?
LangChain is an open-source framework that simplifies the development of applications powered by LLMs like GPT-4, Claude, and Llama 2. It provides modular components for:
✔ Prompt management – Structured templates for LLM inputs.
✔ Memory – Retaining context across interactions.
✔ Chains – Linking multiple LLM calls for complex workflows.
✔ Agents – Dynamic decision-making with tools.
✔ Retrieval-augmented generation (RAG) – Enhancing responses with external data.
Use Cases for LangChain
- Chatbots & virtual assistants
- Document question-answering systems
- Automated content generation
- Data analysis & summarization
What is LangGraph?
LangGraph is a newer framework from the LangChain team, designed for building stateful, multi-agent workflows. Unlike LangChain, which focuses on linear chains, LangGraph introduces cyclic graphs to model complex, looping processes.
Key Features of LangGraph
✔ Stateful execution – Maintains context across steps.
✔ Cyclic workflows – Supports loops (e.g., self-correcting AI agents).
✔ Multi-agent collaboration – Enables agents to work together.
✔ Built on LangChain – Integrates seamlessly with existing LangChain tools.
Use Cases for LangGraph
- Autonomous AI agents (e.g., AutoGPT-style applications)
- Self-improving AI workflows
- Multi-agent simulations
- Complex decision-making systems
LangChain vs. LangGraph: Key Differences
| Feature | LangChain | LangGraph |
|---|---|---|
| Workflow Type | Linear chains | Cyclic graphs |
| Statefulness | Limited | Fully stateful |
| Multi-Agent Support | Basic | Advanced |
| Best For | Simple LLM pipelines | Complex, looping AI agents |
| Learning Curve | Moderate | Steeper |
When to Use LangChain?
✅ You need a simple, modular way to integrate LLMs.
✅ Your workflow follows a linear sequence (e.g., RAG pipelines).
✅ You want an established framework with extensive documentation.
When to Use LangGraph?
✅ You need looping, self-correcting AI agents.
✅ Your application requires multi-agent collaboration.
✅ You’re building autonomous AI systems (e.g., AutoGPT).
Which One Should You Choose?
- For most LLM applications, LangChain is the safer choice.
- For advanced agentic workflows, LangGraph is the future.
If you’re just starting out, LangChain is easier to learn. But if you’re building autonomous AI agents, LangGraph provides the flexibility needed for dynamic decision-making.
Final Thoughts
Both LangChain and LangGraph are powerful tools for AI development. While LangChain remains the go-to for most LLM applications, LangGraph unlocks next-gen agentic AI with stateful, looping workflows.
Want to dive deeper? Check out the official docs:
🔗 LangChain Documentation
🔗 LangGraph Documentation