Compare
AI Agent Frameworks Compared (2026)
LangGraph, LangChain, CrewAI, the OpenAI Agents SDK, Google's Agent Development Kit, Microsoft's AutoGen, LlamaIndex, Pydantic AI — which AI agent framework should you learn, and do you need one at all? A plain comparison of what each is for, where it shines, and the order a beginner should learn them.
By Abdul Rahman Azam, founder of AI Season · Updated
Key facts
| Best first framework for most learners | LangGraph, with LangChain components — after building one agent by hand |
|---|---|
| Lightest-weight option | OpenAI Agents SDK (few primitives, quick to read) |
| Quickest multi-agent prototypes | CrewAI (role-based crews of agents) |
| Google Cloud / Gemini projects | Google Agent Development Kit (ADK) |
| Heavy document and RAG work | LlamaIndex |
| Do you need a framework? | Not for simple agents — a model, tools and a loop are enough |
Do you even need a framework?
An AI agent is a model, some tools and a loop — you can write one in about fifty lines of Python, and you should, once, to understand what frameworks are doing for you. Our step-by-step tutorial does exactly that.
Frameworks earn their place when the agent needs more than a loop: saving state between sessions, branching and retries, streaming progress to a UI, pausing for a human to approve an action, several agents cooperating, tracing every step, or dozens of ready-made integrations. If you need two or more of those, use a framework rather than rebuilding it.
The frameworks at a glance
| Framework | Best for | Style | Languages |
|---|---|---|---|
| LangGraph | Production agents that need control: state, branching, retries, human approval | Graph of steps with shared state | Python, JavaScript |
| LangChain | Getting started fast; hundreds of model, tool and retriever integrations | High-level components; agents run on LangGraph | Python, JavaScript |
| CrewAI | Multi-agent teams with clear roles; fast prototypes | Role-based crews plus event-driven flows | Python |
| OpenAI Agents SDK | Lightweight agents with handoffs and guardrails | A few small primitives | Python, TypeScript |
| Google ADK | Multi-agent systems on Google Cloud and Gemini | Code-first agents and workflow agents | Python, Java |
| AutoGen / Microsoft Agent Framework | Conversational multi-agent systems in the Microsoft ecosystem | Agents that talk to each other | Python, .NET |
| LlamaIndex | Agents over large document collections (RAG) | Data framework with agents and workflows | Python, TypeScript |
| Pydantic AI | Type-safe, well-structured Python agents | Typed agents and structured outputs | Python |
Frameworks in this space release new versions every few weeks. The trade-offs below are stable; exact APIs change — always check each project's current documentation.
LangGraph
LangGraph, from the LangChain team, describes an agent as a graph: nodes are steps (call the model, run a tool, check a result), edges decide what runs next, and a shared state object flows through. That makes the agent's behaviour explicit — you can see and control every path — and adds the things production systems need: checkpoints to resume a run, human-in-the-loop pauses, streaming and retries.
Choose it when you want control and reliability more than speed of setup. Watch out for a steeper first week — thinking in graphs takes practice. It is the framework AI Season uses for stateful workflows; see LangChain vs LangGraph.
LangChain
LangChain is the toolbox: a common interface to chat models from many providers, prompt templates, output parsers, document loaders, text splitters, retrievers and a large catalogue of integrations. Its agent helper runs on LangGraph underneath, so the two are designed to be used together.
Choose it when you want to move fast, swap model providers easily, or need a ready-made loader or vector-store integration. Watch out for layers of abstraction that can hide what is happening — learn the raw API first.
CrewAI
CrewAI organises work as a crew: agents with a role, a goal and tools, assigned tasks that they complete in sequence or under a manager agent. It also offers flows for event-driven orchestration. The mental model — "a researcher, a writer and an editor working together" — is easy to grasp, which makes it popular for demos and content or research pipelines.
Choose it when your problem naturally splits into roles. Watch out for multi-agent setups being slower and costlier than one well-designed agent; add agents only when a single one genuinely struggles.
OpenAI Agents SDK
OpenAI's Agents SDK keeps the surface small: agents (a model plus instructions and tools), handoffs so one agent can pass the conversation to another, guardrails that validate inputs and outputs, sessions for memory, and built-in tracing. It is open source and can be pointed at other providers.
Choose it when you want minimal abstraction and clear code, especially with OpenAI models. Watch out for building complex branching workflows yourself — that is where graph-based frameworks help.
Google Agent Development Kit (ADK)
Google's ADK is a code-first toolkit for building agents and multi-agent hierarchies, with ready-made workflow agents (sequential, parallel and loop), tool integrations and a path to deployment on Google Cloud. It is optimised for Gemini but not locked to it.
Choose it when you build on Google Cloud or Gemini, or want structured multi-agent patterns out of the box.
AutoGen and Microsoft Agent Framework
Microsoft Research's AutoGen popularised conversational multi-agent systems — agents that solve problems by messaging each other, with humans able to join the conversation. Microsoft has since brought AutoGen's ideas together with Semantic Kernel in Microsoft Agent Framework, so new projects in the Microsoft and .NET ecosystem should start there.
Choose it when you work in Microsoft's stack or research multi-agent conversation patterns.
LlamaIndex and Pydantic AI
LlamaIndex began as a data framework for connecting LLMs to documents and databases, and its strength is still retrieval: loaders, indexes and query engines, with agents and workflows on top. Pick it when most of your agent's work is finding the right information in a large collection — see RAG for AI agents.
Pydantic AI, from the team behind the Pydantic validation library, brings type hints and validated, structured outputs to agent code. Pick it when correctness of data flowing in and out of the model matters most.
Protocols, not frameworks: MCP and A2A
Two open standards sit underneath the frameworks and work with all of them. The Model Context Protocol (MCP) standardises how an agent connects to tools and data — write a tool server once and any MCP-capable agent can use it. The Agent2Agent (A2A) protocol standardises how agents built by different teams discover each other and hand off work. Learning both is a good investment whichever framework you choose; start with the MCP guide.
Which framework should a beginner learn first?
- No framework — build one agent by hand with the raw API, so you know what a tool call and the loop really are.
- LangChain components — models, prompts, retrievers — for speed and integrations.
- LangGraph — for state, branching, retries and human approval; this is where agents become dependable.
- One specialist tool when you need it — CrewAI for role-based teams, LlamaIndex for heavy RAG, ADK or the OpenAI Agents SDK if your employer's stack uses them.
Concepts transfer between frameworks far better than code does. Once you understand tools, state, memory, retrieval and evaluation, picking up a new framework takes days, not months.
What about no-code tools?
Visual automation tools such as n8n, Make and Zapier can call AI models and are excellent for business automations — lead capture, email triage, spreadsheet updates. They are a practical choice for freelancers and small businesses. For custom agents with complex logic, testing and deployment, code-first frameworks give you more control and are what most engineering roles ask for.
Learn the frameworks live
AI Season's bootcamp teaches the raw loop first, then LangChain and LangGraph, MCP and agent SDK patterns, security guardrails and deployment — in 12 live sessions over 6 weeks, explained in Urdu with English code. See the curriculum. Cohort 02 starts 1st January 2027; early-bird fee PKR 3,000.
Frequently asked questions
What is the best AI agent framework in 2026?
There is no single best. LangGraph is a strong default for controllable production agents; the OpenAI Agents SDK is the lightest; CrewAI is quickest for role-based multi-agent prototypes; Google ADK fits Gemini and Google Cloud; LlamaIndex is strongest for document-heavy RAG.
Is LangChain still worth learning?
Yes, as a toolbox: its model interface, loaders, splitters and integrations save time, and its agents run on LangGraph. Learn the raw API first so the abstractions make sense.
LangGraph or CrewAI — which should I learn?
Start with LangGraph if you want to build dependable single or multi-agent systems with explicit control. Try CrewAI when a problem naturally splits into roles and you want a fast prototype.
Can I switch frameworks later?
Yes. The concepts — tools, state, memory, retrieval, evaluation — are the same everywhere, and MCP lets tools you build work across frameworks.
Which framework does AI Season teach?
The raw agent loop first, then LangChain and LangGraph, plus MCP and agent SDK patterns. See the curriculum.