This content originally appeared on DEV Community and was authored by Chandrani Mukherjee
From simple chains to advanced agents — understand how these tools support different stages of LLM development
If you've been diving into the world of AI agents, RAG pipelines, and LLM orchestration, you've probably 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?
Let’s break it down 👇
🧠 Quick Intro: What Are They?
LangChain
LangChain is a Python (and JS) framework for building context-aware applications powered by LLMs. It provides all the tools you need 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, by the same team, 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 — but tailored for LLM-native workflows.
🔍 Key Differences
| Feature | LangChain | LangGraph |
|---|---|---|
| Programming Model | Sequential / agent loop / toolchain | DAG (Directed Acyclic Graph) or cyclic graphs |
| Use Case | Prototyping, agents, RAG pipelines | Multi-agent workflows, complex stateful logic |
| State Handling | Limited / memory objects | Built-in state transitions & versioned memory |
| Concurrency | Not native | Supports async and parallel execution |
| Debuggability | Simple tracing, LangSmith support | Full event-based traces, step-by-step node execution |
| Best For | Getting started quickly with LLM apps | Production-grade complex workflows, multi-agent apps |
📦 Example Scenarios
✅ Use LangChain when:
- You want to quickly prototype a chatbot
- You’re chaining together a few prompts and tools
- You want to build a RAG system using OpenAI + Pinecone
- You’re building an agent that picks the next action using tools
🚀 Use LangGraph when:
- You need multiple agents to interact (e.g., planner-executor)
- Your app has stateful, branching logic
- You want full control over flow, retries, memory per node
- You’re going into production and care about observability
🔄 Can You Use Them Together?
Absolutely! 💥
LangGraph is actually built on top of LangChain. You can use all the tools, chains, retrievers, and agents you love from LangChain inside LangGraph nodes.
🔧 Think of LangChain as the toolbox, and LangGraph as the assembly line.
📈 Final Verdict
| If you are... | Go with... |
|---|---|
| New to LLMs and want to build something quick | LangChain |
| Ready to build production-ready, robust LLM workflows | LangGraph |
| Building multi-agent or cyclic systems | LangGraph |
| Want fine-grained control over state and flow | 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
This content originally appeared on DEV Community and was authored by Chandrani Mukherjee
Chandrani Mukherjee | Sciencx (2025-07-17T03:58:02+00:00) Graph or Chain? Choosing the Right Engine for Your AI App. Retrieved from https://www.scien.cx/2025/07/17/graph-or-chain-choosing-the-right-engine-for-your-ai-app/
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