LangChain vs LangGraph: Understanding the Difference

saurabhkamal14
Nov 8
3 min read

If you are working in the space of large language models (LLMs) and agent-based workflows, you've likely encountered both LangChain and LangGraph. They come from the same ecosystem, but they have different design goals, abstractions (level and style of conceptual modeling), and use cases.

What is LangChain?

LangChain is described as a framework for developing applications powered by large language models (LLMs). It emphasizes chains of components (like model calls, prompt processing, document retrieval, and tool invocation) that you can string together to build an LLM-driven application.

"Chains of components" = Ready-made AI steps you string together in order, like LEGO, to make something smart.

Model calls -> "Ask the AI a question"
Prompt processing -> "Write the question nicely"
Document retrieval -> "Go find info in a file"
Tool invocation -> "Use a calculator or search the web"

So, LangChain lines them up in order to create a smart app, just like following a recipe.

What problems does it address?

LLMs on their own generate text-based answers. But real applications often need: access to external data (databases, documents, APIs), chains of actions (retrieve -> process -> summarize -> answer), memory, and tools. LangChain helps simplify that.
Speed of iteration: because you can reuse building blocks (retrieval, tools, prompt templates) instead of reinventing for each new app. In other words, you don't have to build a new robot from scratch every time. Just grab the same arms, wheels, and brain you used before - snap them in - and boom, new robot in minutes.

Typical use cases

Retrieval-augmented generation (RAG) workflows: fetching documents, embeddings, passing to LLM for summarization or Q&A.
Chatbots or assistants that need tool use (like calculating something, fetching data, calling an API) via the "agent" paradigm in LangChain.
Rapid prototyping of LLM applications where you need existing building blocks for memory, chains, and tools. In other words, if you want to build a smart robot helper super fast? LangChain gives you a box of ready-made parts - like a memory chip, a tool belt, and instruction cards - so you just snap them together and go!"

What is LangGraph?

LangGraph is described as a framework for building, managing, and deploying complex AI agent workflows built on large language models (LLMs). Unlike simpler chain-based frameworks, LangGraph allows you to define workflows as graphs (nodes + edges) rather than strictly linear sequences. enabling more dynamic cyclic behavior. Essentially, where many LLM-driven apps follow a direct step A -> B -> C, LangGraph allows branching, loops, multi-agent collaboration, and human-in-the-loop interventions, making it suitable for long-running, adaptable workflows.

Core Capabilites:

Graph-based modelling: You build workflows as a network of nodes linked by arrows, and you can have loops and decision-points instead of just a straight line.
Stateful execution: LangGraph keeps and updates a shared "state" across the workflow, allowing it to remember past interactions and work over long sessions.
Human-in-the-loop & observability: You can pause the workflow for human review or intervention, and you get the tools to monitor and debug how agents are behaving.

LangGraph places a strong focus on flexibility, control, and durability. Instead of using a high-level "Chain" of steps, you are given low-level building blocks so you can design workflows that adapt, branch out, persist, and involve multiple actors. It's specially designed for applications that involve many steps, ongoing memory, loops or multiple interacting agents and need oversight.

Typical use cases

Complex agent systems: multiple actors or agents interacting (one agent queries data, another writes output, and another human checks).
Long-running workflows that require context retention over hours, and days.
Scenarios where you need full control over the orchestrating of LLM steps, branching logic, and stateful memory across sessions.
Enterprise workflows with human-in-the-loop, audit trail, and multi-stage actions.