Frameworks · Guide
AI Agent Frameworks: LangChain, LangGraph, CrewAI and More
Pick the right scaffolding — LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, OpenAI Agents SDK.
Agent frameworks give you the scaffolding — tool calling, state management, and orchestration — so you don't rebuild the agent loop by hand. This guide compares LangChain, LangGraph, CrewAI, AutoGen, LlamaIndex, the OpenAI Agents SDK, and Claude tool use, and helps you choose the right one for your project.
Generate your own lesson →What you'll learn
- What Is LangGraph?
- AutoGen and AG2 Multi-Agent Patterns
- AutoGen Multi-Agent Tutorial
- Building Agent Apps with LangChain
- Building Agents with Claude Tool Use
- Choosing an AI Agent Framework
- CrewAI: Multi-Agent Crews
- Designing Role-Based Agents in CrewAI
- Google ADK and Gemini Tool Calling
- LangGraph in Depth
- LlamaIndex Agents and Workflows
- LlamaIndex vs LangChain for RAG
- Semantic Kernel Overview
- The OpenAI Agents SDK
- What Is LangChain?
- LangChain vs LangGraph: Which to Use When
- CrewAI Tutorial: Role-Based Agents
- LangGraph Studio: Visual Agent Debugging
- n8n AI Agents: Automate Workflows
- Using LangChain for RAG
Lessons in this guide (20)
What Is LangGraph?
LangGraph is a low-level orchestration framework that models agentic AI applications as a stateful graph of nodes and edges, enabling cycles, branching, persistence, and human-in-the-loop control that a plain agent loop cannot provide.
AutoGen and AG2 Multi-Agent Patterns
AutoGen (and its community fork AG2) is a framework where multiple agents solve a task by exchanging messages, and its power is in the conversation patterns you compose. The core patterns are two-agent chat, group chat with a manager that p
AutoGen Multi-Agent Tutorial
AutoGen is Microsoft's open-source framework for building applications where multiple AI agents converse with each other and with tools to solve a task. You create agents — such as an AssistantAgent that reasons and a code-executing agent —
Building Agent Apps with LangChain
LangChain is a framework for building LLM applications from composable pieces — models, prompts, tools, retrievers, and memory — wired together into chains or agents. A chain runs a fixed sequence of steps; an agent lets the model choose to
Building Agents with Claude Tool Use
Claude, Anthropic's family of models, builds agents through tool use: you describe tools with a name, description, and input schema, and Claude replies with structured tool-use requests your code executes and returns. Running this in a loop
Choosing an AI Agent Framework
Choosing an AI agent framework comes down to how much control versus abstraction you want, and whether you need a single agent or a coordinated team. Low-level frameworks like LangGraph give explicit control flow; lightweight ones like the
CrewAI: Multi-Agent Crews
CrewAI is an open-source Python framework for orchestrating teams of role-playing AI agents that collaborate to complete a task. You define Agents (each with a role, goal, and tools), Tasks (a description plus the expected output), and a Cr
Designing Role-Based Agents in CrewAI
In CrewAI, the quality of a multi-agent crew depends less on the framework than on how you design each agent's role. An agent is defined by a role, a goal, and a backstory, which together act as a focused system prompt, plus the tools it ca
Google ADK and Gemini Tool Calling
Google's Agent Development Kit (ADK) is an open-source framework for building and deploying agents on top of Gemini, Google's model family, though it works with other models too. Its foundation is Gemini function calling: you declare functi
LangGraph in Depth
LangGraph is an open-source framework from the LangChain team for building stateful, multi-step LLM applications as graphs. You define nodes (functions that read and update a shared state), edges (which node runs next), and conditional edge
LlamaIndex Agents and Workflows
Beyond retrieval, LlamaIndex builds agents that reason over your data and Workflows that orchestrate multi-step processes. A LlamaIndex agent wraps a model with tools — often query engines over your indexes — so it can decide what to look u
LlamaIndex vs LangChain for RAG
LlamaIndex and LangChain are two open-source Python frameworks for building retrieval-augmented generation (RAG). LlamaIndex is a data framework focused on ingesting, indexing, and querying your documents, so retrieval-centric apps come tog
Semantic Kernel Overview
Semantic Kernel is Microsoft's open-source SDK for embedding large language models into conventional applications, with first-class support for C#/.NET as well as Python and Java. It centers on the kernel, a container that holds plugins — r
The OpenAI Agents SDK
The OpenAI Agents SDK is a lightweight Python framework for building agents with a small set of primitives: an Agent (a model plus instructions and tools), tools it can call, handoffs to pass control to another agent, guardrails to validate
What Is LangChain?
LangChain is an open-source framework that gives you a common application layer — models, tools, memory, and chains — so you can build LLM-powered apps without rewriting the same plumbing for every provider or use case.
LangChain vs LangGraph: Which to Use When
LangChain is the right default for most LLM applications — chains, RAG, and simple agents — while LangGraph is the right choice when your control flow itself is the product: branching, resuming, interrupts, and explicit state transitions.
CrewAI Tutorial: Role-Based Agents
Build a working multi-agent CrewAI crew by defining role-based agents, assigning tasks, and wiring them into a sequential or hierarchical workflow that produces a real output.
LangGraph Studio: Visual Agent Debugging
LangGraph Studio is a local visual IDE that lets you connect a running LangGraph agent, step through its graph execution node by node, inspect live state, and pinpoint failures without adding print statements.
n8n AI Agents: Automate Workflows
Build and deploy AI-powered workflow automation by wiring n8n's agent node to LLMs, tools, and error-handling patterns — turning a visual canvas into a production-ready autonomous pipeline.
Using LangChain for RAG
LangChain's document loaders, vector stores, LCEL chains, and LLM integrations give you a composable pipeline to build a production-ready RAG system that answers questions grounded in your own documents.
Frequently asked questions
LangChain vs LangGraph — what's the difference?
LangChain is a broad toolkit of components (models, prompts, tools, chains). LangGraph is its framework for building agents as an explicit, inspectable state graph you fully control — better when you need branching, loops, and durable state.
Which agent framework should I use?
For a simple tool-using loop, start with plain tool calling or LangChain. For complex, stateful, multi-step agents, use LangGraph. For role-based multi-agent teams, CrewAI or AutoGen. Match the framework to how much control and structure you need.
What is CrewAI?
CrewAI is a framework for orchestrating multiple role-based agents (e.g. researcher, writer, reviewer) that collaborate on a task, run sequentially or hierarchically.