From simple chains to advanced agents—understand how these tools support different stages of LLM development.

If you've been exploring AI agents, RAG pipelines, and LLM orchestration, you've likely encountered both LangChain and the newer LangGraph. While they’re part of the same ecosystem, they serve distinct roles and come with different philosophies. So, which one is right for you?

Quick Intro: What Are They?

LangChain

LangChain is a Python (and JS) framework for building context-aware applications powered by LLMs. It provides tools to:

• Chain prompts and tools together
• Manage memory
• Use agents to decide actions dynamically
• Integrate with vector stores, tools, APIs, and more

Think of LangChain as the Swiss Army knife for LLM application development.

LangGraph

LangGraph is a graph-based orchestration framework for building stateful and multi-agent LLM systems.

• Based on event-driven computation
• Each node is a function (often with an LLM inside)
• Can create cyclic, branching, and dynamic workflows

Think of LangGraph as the Airflow/Prefect of LLMs—tailored for LLM-native workflows.

Key Differences

Feature	LangChain	LangGraph
Programming Model	Sequential / agent loop / chain	DAG or cyclic graphs
Use Case	Prototyping, agents, RAG	Multi-agent, complex stateful logic
State Handling	Limited / memory objects	Built-in state transitions
Concurrency	Not native	Supports async and parallel
Debuggability	Simple tracing	Full event-based traces
Best For	Quick LLM apps	Production-grade workflows

Example Scenarios

Use LangChain when:

• You want to quickly prototype a chatbot
• Chaining a few prompts and tools
• Building a RAG system
• Building an agent that picks the next action

Use LangGraph when:

• You need multiple agents to interact
• Your app has stateful, branching logic
• You want full control over flow, retries, memory per node
• You care about observability in production

Can You Use Them Together?

Absolutely! LangGraph is built on top of LangChain. You can use all the tools, chains, retrievers, and agents from LangChain inside LangGraph nodes.

Final Verdict

• New to LLMs and want to build something quick? Use LangChain.
• Ready for production-ready, robust LLM workflows? Use LangGraph.
• Building multi-agent or cyclic systems? Use LangGraph.
• Want fine-grained control over state and flow? Use LangGraph.
• Prototyping or using LangSmith for tracing? Both work well.

TL;DR:

• LangChain = Toolbox for chaining prompts, tools, agents, retrievers
• LangGraph = Framework for building complex, stateful LLM workflows
• Use them together for the best of both worlds.