AI Agent Frameworks Comparison 2026: 7 Picks, 1 to Avoid

For most small teams in late 2026, the right agent framework is the one built by the company whose model you already pay for: the OpenAI Agents SDK if you run on GPT, the Claude Agent SDK if you run on Claude. Pick LangGraph only when you need long, stateful workflows that pause for a human and survive a crash. Everything else is a niche choice, and one popular name, AutoGen, shouldn’t be on your list at all anymore.

That’s the short version of our AI agent frameworks comparison 2026. Below, we go through seven frameworks worth your time and one to avoid, with current versions, install commands you can paste, a cost example for a real workload, and the production gaps none of them close for you.

One note on how we checked. Every version number and release date here comes from the framework’s own PyPI page (the public Python package registry), read on 9 November 2026. Prices come from vendor pricing pages on the same day. If a number has no link, we left it out.

What an agent framework actually does for you

An AI agent is a program that gets a goal, decides its own next step, calls a tool (a search, a database query, an email send), looks at the result and repeats until it’s done. The loop itself is maybe 40 lines of code. So why use a framework?

Because the loop is the easy part. The hard parts are everything around it: keeping track of what happened so far (state), saving that state so a crash at step 9 doesn’t restart from step 1 (persistence or checkpointing), stopping to ask a person before a risky action (human-in-the-loop), passing work between several agents (handoffs), and recording every model call so you can see why the agent did something odd (tracing).

A framework gives you those pieces pre-built. The trade is that you learn its way of thinking, and you inherit its bugs and its release schedule.

Microsoft’s own docs put the rule of thumb bluntly: “If you can write a function to handle the task, do that instead of using an AI agent.” We agree. A lot of what gets sold as an agent is a fixed sequence of steps with one model call in the middle, and a plain workflow handles that better. If you’re unsure which you have, our guide to single-agent vs multi-agent architecture walks through the difference.

Frameworks also split into two families. Graph frameworks (LangGraph, Microsoft’s workflows) make you draw the steps and the arrows between them. Loop frameworks (the OpenAI and Claude SDKs, Pydantic AI) give the model the tools and let it choose the path. Graphs are more predictable. Loops are faster to build and better at messy, open-ended jobs.

Seven frameworks worth a look, and one to avoid

Here’s each option in plain terms: what it is, who it fits, and the catch. They’re in the order we’d consider them for a typical small or mid-sized business, not by popularity.

1. OpenAI Agents SDK: the lightest way to start on GPT

OpenAI’s SDK is small on purpose. Its docs list five building blocks: agents, handoffs between agents, guardrails that check inputs and outputs, sessions for memory, and built-in tracing (OpenAI Agents SDK docs). You can learn the whole thing in an afternoon.

It first shipped on 4 March 2025 and is at version 0.24.0, released 15 October 2026 (PyPI: openai-agents). The version still starts with a zero, which means the API can still change between releases. Pin your version.

The docs also list adapters for non-OpenAI providers through LiteLLM and Any-LLM, so you aren’t strictly locked in. In practice, tracing and hosted tools work best with OpenAI models, and that’s where it shines. We’d pick it for a GPT shop building a support or sales agent with two or three handoffs.

2. Claude Agent SDK: Claude Code as a library

Anthropic’s SDK gives you the same tools, agent loop and context management that power Claude Code, in Python and TypeScript. That means file reading and editing, shell commands, web search, hooks (your own code that runs before or after a tool call), subagents, MCP connections, permissions and resumable sessions come built in.

That’s the big difference from every other option here. Other frameworks give you a loop and ask you to write the tools. This one ships with working tools that already handle the dull edge cases. The Python package is at 0.2.179, released 6 November 2026 (PyPI: claude-agent-sdk), and releases land almost weekly.

Two catches. It runs Claude models only, through Anthropic’s API, Amazon Bedrock, Google Cloud or Microsoft Foundry (Agent SDK quickstart). And Anthropic doesn’t allow third-party products to use claude.ai logins or subscription limits, so you pay API rates. We’d pick it for agents that work with files, documents or code, or that run long jobs unattended. Our step-by-step Claude Code agent build shows the same approach from the CLI side.

3. LangGraph: the production pick for complex, stateful work

LangGraph calls itself “a low-level orchestration framework and runtime for long-running, stateful agents.” You define a graph of steps that share state. It can checkpoint that state and resume after a failure, pause for a person to inspect or edit it, and stream progress as it runs.

It’s model-agnostic, MIT-licensed and mature: first released in January 2024, now at 1.2.20 from 5 November 2026 (PyPI: langgraph). The 1.x number matters. It signals a stable API, which none of the vendor SDKs above can claim yet.

The cost is effort. You design the orchestration yourself, and the learning curve is real. We’d pick it for an approvals process, a multi-day onboarding flow, or anything where an auditor might ask “what exactly happened at step 6?” If you’re new to it, start with our plain-English LangGraph explainer.

4. Microsoft Agent Framework: the AutoGen and Semantic Kernel successor

Microsoft merged its two agent projects into one. The docs describe Agent Framework as “the next generation of both Semantic Kernel and AutoGen”, built by the same teams. It runs in .NET and Python, with Go in public preview, and supports Microsoft Foundry, Azure OpenAI, OpenAI, Anthropic and Ollama models.

It offers single agents, graph-based workflows with explicit paths, and a “harness agent” with planning, file access, memory and tool approval for long tasks. The Python package first appeared on 1 October 2025 and is at 1.25.0, released 7 November 2026 (PyPI: agent-framework).

This is the obvious choice for a .NET team or a company that already lives in Azure. Outside that world, the Azure-first examples and setup add friction you don’t need.

5. Google ADK: the most languages, built for Google Cloud

Google’s Agent Development Kit runs in Python, TypeScript, Go, Java and Kotlin, which is the widest language spread here (ADK docs). It works with Gemini out of the box and with other models through adapters, including local ones. Deployment targets include Cloud Run, GKE and Google’s Agent Platform, and it has first-class support for the A2A protocol (a standard for agents from different vendors to talk to each other).

It’s at 2.14.0, released 7 November 2026 (PyPI: google-adk). We’d pick it for a Java or Go shop, or a company already on Google Cloud. If neither applies, there’s no strong reason to choose it over the options above.

6. CrewAI: fastest route to a working multi-agent demo

CrewAI organises work two ways. Crews are teams of role-playing agents (“researcher”, “writer”, “editor”) that collaborate. Flows are the structured backbone that handles state, branching and control (CrewAI docs). The docs recommend a Flow for structure with a Crew inside the steps that need agents to collaborate.

It ships often: version 1.15.39 went out on 9 November 2026 (PyPI: crewai). The role-based model clicks quickly for non-engineers, which is why it demos so well.

The catch is debugging. When a crew of five agents produces a wrong answer, finding which agent went wrong is harder than reading a graph. We’d use it to prove an idea fast, then decide whether to keep it. Our CrewAI tutorial for beginners gets you to a first crew.

7. Pydantic AI: typed, plain Python with no magic

Pydantic AI describes itself as “a typed, extensible agent loop”. Every output is checked against a type you define, so you get a real Python object back, not a string you have to parse. It works with almost any model provider, and it can run inside durable execution engines such as Temporal, DBOS and Prefect so long runs survive restarts.

It’s at 2.66.0 from 4 November 2026 (PyPI: pydantic-ai). It’s our pick for Python developers who want an agent to slot into an existing app the way any other library would, with tests and type checks.

The one to avoid: AutoGen

AutoGen still shows up in a lot of “best frameworks” lists. Don’t start a new project on it. Its GitHub README now says “This project has been archived and is no longer actively maintained by Microsoft” and that Microsoft isn’t providing bug or security fixes. The last release of its main package was 0.7.5 on 30 September 2025 (PyPI: autogen-agentchat).

If you already run AutoGen, plan a move to Microsoft Agent Framework, which has an official migration guide. Our older AutoGen vs CrewAI comparison is still useful for understanding the design ideas, but the maintenance picture has changed since.

Our AI agent frameworks comparison 2026, in one table

This table is the one we’d print out. Versions and dates are from each package’s PyPI page on 9 November 2026. “Model choice” means how freely you can swap the underlying model without fighting the framework.

Two things jump out. First, the vendor SDKs (OpenAI, Claude) are still on 0.x versions while LangGraph, Microsoft and Google are past 1.0. Second, everything except AutoGen shipped a release in the past four weeks. This field moves weekly, so any comparison older than a few months, including this one eventually, will have stale details.

Sources for the table: openai-agents, claude-agent-sdk, langgraph, agent-framework, google-adk, crewai, pydantic-ai and autogen-agentchat.

Model lock-in decides more than features do

Feature lists make these frameworks look more different than they are. All of them can call tools, keep memory and connect to MCP servers (MCP is a shared standard for plugging AI into business tools; see our MCP explainer). The question that actually splits them is how tied you are to one model company.

Here’s why that matters. Model prices and quality shift every few months. Today on Anthropic’s price list, Claude Sonnet 5.5 costs $2 per million input tokens and $10 per million output tokens, while Claude Haiku 5.5 costs $0.10 and $0.50 for prompts under 100,000 tokens (Claude API pricing). That’s a 20x gap inside one vendor. Across vendors the gaps can be just as wide, and they move.

A model-agnostic framework (LangGraph, CrewAI, Pydantic AI) lets you switch when the numbers change. A vendor SDK ties you to one company, but gives you that company’s best features first and with the least glue code.

Our view: lock-in is fine if you’re honest about it. A five-person company shouldn’t pay a framework tax to keep options open that it’ll never use. A 200-person company with procurement rules and a multi-cloud contract should. Decide which one you are before you look at features.

One practical hedge works either way. Keep your tools (the functions that touch your CRM, inbox or database) in plain code or behind MCP servers, separate from the framework. Then moving frameworks means rewriting the loop, not the integrations, and the loop is the cheap part.

Build the same small agent in four frameworks

The fastest way to choose is to build one tiny agent in your top two candidates and see which one your team can read. Below are the real install commands and starter code from each project’s docs. Every example needs an API key set as an environment variable first.

OpenAI Agents SDK

Install it and run the hello-world from the official docs:

pip install openai-agents
export OPENAI_API_KEY=sk-...

from agents import Agent, Runner

agent = Agent(name="Assistant", instructions="You are a helpful assistant")
result = Runner.run_sync(agent, "Write a haiku about recursion in programming.")
print(result.final_output)

That’s a working agent in four lines. Add tools with plain Python functions and a handoff to a second agent, and you have most of what a support bot needs.

Claude Agent SDK

The SDK bundles the Claude Code binary, so one install is enough (quickstart). It needs Python 3.10+ or Node.js 18.14+.

python3 -m venv .venv
source .venv/bin/activate
pip install claude-agent-sdk
export ANTHROPIC_API_KEY=your-api-key

import asyncio
from claude_agent_sdk import query, ClaudeAgentOptions

async def main():
    async for message in query(
        prompt="Review utils.py for bugs that would cause crashes. Fix any issues you find.",
        options=ClaudeAgentOptions(
            allowed_tools=["Read", "Edit", "Glob"],
            permission_mode="acceptEdits",
        ),
    ):
        print(message)

asyncio.run(main())

Notice allowed_tools. That list is your safety boundary: the agent can only use what you name there without asking. Start read-only (Read, Glob, Grep) and add Edit or Bash only when you trust the job. The SDK also doesn’t load .env files on its own, which trips people up in the first ten minutes.

LangGraph

pip install -U langgraph

LangGraph’s starter example builds a graph with one node wired from START to END. You then add nodes for each step, edges between them and a checkpointer so state is saved. Expect a day, not an hour, before the model clicks for your team. That upfront cost is what buys you the pause-and-resume behaviour later.

CrewAI

CrewAI uses its own command-line tool to scaffold a project (installation docs). It needs Python 3.10 to 3.13.

uv tool install crewai
crewai create crew my_project
cd my_project
crewai run

The scaffold gives you YAML files for agents and tasks, which non-developers can read and edit. That’s a real advantage in a mixed team.

Mistakes we see in the first week

• Not pinning the version. With 0.x SDKs releasing weekly, an unpinned install can break on Monday. Pin an exact version in requirements.txt.
• Giving the agent every tool on day one. Start with read-only tools and widen the list as the logs earn your trust.
• Building multi-agent first. One agent with good tools beats three agents passing notes in most business tasks. Add a second agent only when one prompt can’t hold the job.
• Skipping a cap on steps. Set a maximum number of turns or tool calls, or a confused agent can loop and burn tokens. Our guide to debugging multi-agent loops covers the usual causes.

What running an agent framework really costs

Every framework on this list is free to download. The bill comes from three other places: model tokens, monitoring, and hosting. Here’s a worked example with the assumptions spelled out, so you can swap in your own numbers.

Picture a 15-person company running an inbox triage agent. It handles 3,000 emails a month. Each run reads about 20,000 input tokens (the email, a few CRM lookups and instructions) and writes about 2,000 output tokens. Those token counts are our assumptions for a typical triage run, not measured figures.

On Claude Sonnet 5.5 at $2 per million input tokens and $10 per million output tokens (Claude API pricing), that’s 60 million input tokens ($120) plus 6 million output tokens ($60), so about $180 a month. On Claude Haiku 5.5 at $0.10 and $0.50, the same volume comes to $6 plus $3, about $9 a month. Same framework, same code, a 20x difference from one setting.

Monitoring is next. LangSmith, LangChain’s tracing tool, is free for one seat with 5,000 traces a month, and its Plus plan is $39 per seat per month with 10,000 traces included (LangChain pricing). At 3,000 runs a month, the free tier would cover one person. Two people on Plus would be $78.

Hosting varies most. A small agent can run on a server you already pay for. Managed options charge by usage: CrewAI’s free Basic plan includes 50 workflow executions a month, with Enterprise priced on request (CrewAI pricing), and LangSmith’s deployment hosting bills compute by the vCPU-hour.

The biggest cost isn’t on any price list: the hours to build, test and maintain the thing. For most small teams that dwarfs the token bill. That’s why we weight “can my team read this code in six months?” above any feature. For a deeper breakdown with multi-agent setups, see our guide to the cost of running multi-agent systems.

Gaps no framework closes for you

Every framework here will get you a demo. None of them, on its own, gets you an agent you can leave running on customer data. Four gaps stay your job whichever one you pick.

Evaluation. You need a set of 30 to 50 real examples with known good answers, and you need to rerun them every time you change a prompt, a model or the framework version. Without that, every “small tweak” is a gamble. Some tools help with this, but the examples have to come from your own business.

Permissions. Decide what the agent may do without asking and what needs a human click. The Claude Agent SDK has this built in with allowed tools and permission modes. LangGraph can pause for approval. With lighter loops, you write the approval step yourself. Either way, money, deletions and outbound messages to customers should need a person at first.

Observability. You’ll want a trace of every run: which tools it called, what came back, what it decided. OpenAI’s SDK and Pydantic AI emit traces natively (Pydantic AI through OpenTelemetry, an open tracing standard), and LangGraph pairs with LangSmith. Set this up before launch, not after the first strange email goes out.

Ownership. Someone has to read the logs weekly and update the tools when your CRM changes a field. Agents don’t fail loudly. They drift. If nobody owns that, the agent gets quietly worse and people stop trusting it.

A real example: how this blog’s own agent is built

Here’s a case we can describe exactly, because it’s this blog. Every post on thinkpeak.ai, including this one, is researched, written, checked and published by Claude Code running as a scheduled task from a git repository. No framework from this list is involved.

The “agent” is a set of plain-text instructions (skills, in Claude Code’s terms), a handful of small Node.js scripts for the WordPress API, image creation and internal links, and a check script that blocks publishing if a post breaks the house rules. Each run picks a keyword from a queue, does the research, writes, runs the checks, posts and commits a record back to the repo.

Why no framework? Because the job is one agent doing one long task with tools, which is exactly what the Claude Code loop already does. A graph would have added code without adding safety. The safety comes from the check script, the git history and the rule that a post failing the quality gate twice goes out as a draft.

That’s the lesson we’d pass on. Before you choose between LangGraph and CrewAI, ask whether an existing agent tool plus a few scripts and a checklist already covers the job. Often it does, and the result is easier to maintain than anything you’d build from a framework.

When you don’t need a framework at all

A framework is the right tool for a developer building a custom agent. It’s the wrong tool for a lot of the jobs people bring to us. Here are the alternatives, and when each one wins.

A no-code workflow tool with an AI step. n8n, Make and Zapier all have agent or AI nodes now. If your process is “when a form comes in, classify it, look up the customer, draft a reply, wait for approval”, that’s a workflow with one model call, and a workflow tool handles it with less risk. Our comparison of n8n, Zapier and Make costs helps pick one.

The model API directly. For a single tool-calling loop, the vendor’s client library is enough. Anthropic’s client SDKs include a tool runner that drives the loop for you (Anthropic’s comparison of its own tools). No framework, fewer moving parts.

A hosted agent. If you don’t want to run servers at all, Anthropic’s Managed Agents and Microsoft’s and Google’s hosted runtimes run the loop for you. You trade control for not having to operate anything.

An off-the-shelf agent product. For common jobs like support, scheduling or lead response, a ready-made product with an AI agent built in may beat anything you’d build. Check before you code.

The test we use: if you can draw the process as a fixed flowchart, use a workflow tool. If the path genuinely depends on what the agent finds, use a framework.

Our pick for each kind of team

Here’s where we’d land, assuming you’ve decided you need custom code.

• Small team, already paying OpenAI: OpenAI Agents SDK. Smallest learning curve, good tracing, handoffs cover most multi-step jobs.
• Small team, already on Claude, or work centred on files and documents: Claude Agent SDK. The built-in tools save weeks.
• Regulated process, approvals, long-running jobs: LangGraph. Checkpoints and pause-for-human are the reason to accept the steeper learning curve.
• .NET shop or deep in Azure: Microsoft Agent Framework.
• Java or Go shop, or on Google Cloud: Google ADK.
• Need a convincing multi-agent demo this week: CrewAI, then reassess before production.
• Python developers adding an agent to an existing app: Pydantic AI.

Who shouldn’t pick any of them? A team without a developer who’ll own the code after launch. A framework agent with no owner is a liability. In that case, a workflow tool or a ready-made product is the honest answer, or bring in help. That’s the kind of call our guide to AI agent development services helps with.

If you only remember one thing: pick the framework that matches your model and your team’s language, build a small version in a week, and spend the saved time on evaluation and permissions. Those decide whether the agent works, not the framework.

Checklist before you commit to a framework

Run through this before anyone writes production code. Each line takes minutes to check, and skipping one is a common way agent projects stall after the demo.

• We confirmed the task needs an agent, not a fixed workflow with one AI step.
• We know which model company we’re on for the next year, and whether lock-in is acceptable.
• The framework supports our team’s main language (Python, TypeScript, .NET, Java or Go).
• Its latest release is recent, and the project isn’t archived or in maintenance mode.
• We pinned an exact version and know where to read its changelog.
• Tools live in plain code or MCP servers, separate from the framework.
• There’s a step or turn limit so a looping agent can’t run up a bill.
• Risky actions (payments, deletions, customer emails) need human approval.
• Every run is traced, and someone has looked at a trace end to end.
• We have 30 to 50 test cases with known answers and rerun them after every change.
• We estimated monthly token cost on two model tiers, not one.
• A named person owns the agent after launch and reviews it weekly.

FAQ

Is LangChain the same as LangGraph?

No. LangChain is the company and its original library of building blocks for model apps. LangGraph is its separate framework for stateful agents built as graphs. You can use LangGraph without the rest of LangChain, and for agent work the docs point you to LangGraph.

Which AI agent framework is best for beginners?

The OpenAI Agents SDK has the smallest surface: a working agent is four lines. CrewAI is also friendly because its agents and tasks live in readable YAML files. Both are fine for learning. Just don’t confuse “easy to start” with “easy to run in production”.

Can I switch frameworks later?

Yes, if you plan for it. Keep your tools and business logic outside the framework, ideally as plain functions or MCP servers, and keep your test cases. Then a switch means rewriting the loop and prompts, which is usually days of work, not months.

Do these frameworks work with local, open-source models?

Most do. LangGraph, CrewAI and Pydantic AI work with almost any provider, Google ADK supports local models through adapters, and Microsoft Agent Framework supports Ollama. The Claude Agent SDK is the exception: it runs Claude models only. Our guide to setting up CrewAI with local LLMs shows one way to do it.

Do I need multiple agents?

Usually not at the start. One agent with well-built tools handles most business tasks and is far easier to debug. Split into several agents when one set of instructions gets too long to follow reliably, or when parts of the job need different permissions.