AI Agent Frameworks Compared: How to Choose in 2026

TL;DR

• An AI agent framework gives a language model the tools, state, and control it needs to plan tasks and decide what to do next.
• Narrow your options by asking 4 questions. What language do you use, which cloud do you work with, how many agents do you need, and how well does the workflow need to hold up after a restart.
• Top AI agent frameworks you can adopt, even as an enterprise, include Mastra, LangGraph, Pydantic, and Microsoft Agent.
• Frameworks can handle orchestration well and still fail in production when the agent receives blocked, incomplete, or incorrect web data.

What is an AI agent framework?

An AI agent framework is an implementation that empowers a language model to run a full loop of planning tasks, keeping state across steps, and choosing its next action based on what previous steps returned.

The key difference between an agent and a chatbot is runtime decision-making. In a chatbot or fixed workflow, the developer defines the sequence of steps in advance. In contrast, an agent can decide which tool to call, what to do with the result, and which step should come next as the task unfolds.

Without a framework, developers would need to build that control layer themselves, whereas the ultimate goal of agentic engineering is to have quality sessions and results with minimal human interaction.

The terms agentic framework and AI agent framework are commonly used interchangeably. Both describe the runtime loop and orchestration layer that let an agent act across multiple steps. For a deeper look at this, see our guide on AI agent orchestration.

What are the building blocks of an AI agent?

AI agents are generally built around 4 core components. Together, they let an agent reason, act, remember what happened, and decide what to do next.

The 4 pillars

• Model. The model is the reasoning engine. It interprets the task, examines the available context, and decides what should happen next. A good example is the recently released GPT-6 Astra.
• Tools. These are the functions the agent can call to interact with external systems, such as APIs, databases, code, or the web. Web access is one of the most common tool categories, but it’s also one of the most failure-prone when an agent depends on live data.
• Memory and state. This is the short-term working context that keeps track of previous steps, plus any information that needs to persist across sessions or process restarts.
• Orchestration. Orchestration coordinates the other 3. It handles routing, retries, handoffs, error handling, and the reason-act-observe cycle that keeps the agent moving toward its goal.

The 5 classic agent types

These 5 agent types come from traditional AI, but they still help explain how modern agents make decisions.

1. Simple reflex agents. These react directly to the current input using predefined rules.
2. Model-based reflex agents. They maintain an internal representation of their environment before acting.
3. Goal-based agents. Actions are chosen based on whether they move closer to a defined goal.
4. Utility-based agents. Each possible outcome is weighed and the one with the highest expected value is chosen.
5. Learning agents. Behavior improves over time using feedback or experience.

A good number of modern LLM agents closely resemble goal- and utility-based agents. They work toward an objective, evaluate available actions, and decide what to do next as the task unfolds.

Developers also group agent systems by how they’re structured, namely single-agent systems, multi-agent systems, and human-in-the-loop systems, where execution can pause for review or approval.

Do you really need an AI agent framework?

The short answer is not always. If your application makes one model call, uses a few tools, and doesn’t need to keep state between steps, it’s simpler to write the loop yourself.

A framework starts making sense when you need branching logic, retries across multiple steps, persistent state, several agents working together, or human approval gates. These are the problems frameworks solve.

However, there is quite a huge trade-off you should know before going ahead with frameworks, which is an added abstraction. This is bound to happen when more dependencies are added, and they lead to failures that can be harder to trace. Fast release cycles can also introduce breaking changes between minor versions.

For engineers running production-level code, we recommend you pin and test your dependencies. But if you don’t want to write the orchestration layer yourself, low-code tools are another option you can explore. See our OpenClaw vs n8n comparison, or our guide to AI web scraping with Python if you’re building the data collection side yourself.

The 9 AI Agent Frameworks Compared

At-a-glance comparison

The frameworks below all cover the basics of agent orchestration, but they take different approaches to how agents coordinate tasks, manage state, connect to tools, and recover across runs. The table gives you a quick view of those differences:

Python teams have the broadest choice, with options like LangGraph/LangChain, CrewAI, LlamaIndex Workflows, PydanticAI, OpenAI Agents SDK, Google ADK, and smolagents.

TypeScript teams have fewer purpose-built choices, which is where Mastra stands out, while OpenAI Agents SDK, LangChain, and Google ADK also support JavaScript or TypeScript.

For .NET teams, Microsoft Agent Framework is the clearest fit, especially for teams already building in C# or heavily invested in the Microsoft stack.

LangGraph and LangChain

LangChain is the higher-level toolkit for building LLM applications, while LangGraph handles the lower-level orchestration behind stateful, multi-step agents. Together, they give Python teams a wide integration ecosystem for building agents. LangChain agents are built on LangGraph, so the 2 increasingly work as parts of the same stack.

LangGraph’s checkpointing is super useful for agents that need to save state and resume after an interruption. However, the downside is complexity. The combination gives you a lot of abstractions and dependencies, which can make edge cases harder to debug than they would be with a smaller framework.

Quick setup

Start with Python 3.10+ and install LangChain, LangGraph, and the package for your model provider.

pip install -U langchain langgraph langchain-openai

From there, set your model API key and create your agent with create_agent(). If you need more control over state, checkpoints, or routing, you can work directly with LangGraph’s graph APIs instead.

CrewAI

CrewAI is a standalone Python framework built around a role-based mental model. You define agents with a role, a goal, and tools, group them into crews, and add flows for event-driven control. It carries no LangChain dependency.

The trade-off is that provider behavior isn’t always consistent as there’s friction with non-OpenAI providers. If you plan to use models outside the most common provider paths, test the exact combination early without assuming every adapter behaves the same way.

Quick setup

CrewAI recommends using its CLI. Install uv first if you don’t already have it, then install CrewAI and scaffold a project.

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

Add your model API key to the generated .env file, define your agents and tasks, then run the crew with:

crewai run

CrewAI now uses a JSON-first project structure by default, with agents stored in agents/*.jsonc and the main crew configuration in crew.jsonc.

Microsoft Agent Framework

Microsoft Agent Framework brings Microsoft’s earlier AutoGen and Semantic Kernel agent work into a single framework for building agents and graph-based workflows.

It’s a natural fit for .NET teams and organizations already working heavily with Microsoft’s AI and cloud stack, while Python support keeps it relevant outside C# projects.

Its workflow model supports checkpointing, events, agent handoffs, and human input, giving teams more explicit control over long-running execution. The main consideration is support outside Microsoft’s ecosystem.

Quick setup

For Python, install the Agent Framework package and the provider you plan to use. The full package gives you the easiest starting point:

pip install agent-framework

Then configure your model provider, create an Agent, and call agent.run() with a prompt. Microsoft also provides separate packages for OpenAI, Microsoft Foundry, and other providers if you want a slimmer installation.

Google ADK

Google’s Agent Development Kit, or ADK, combines agent orchestration, workflow primitives, development tools, and integrations with Google’s AI ecosystem. It works especially well for teams already using Gemini or Google Cloud, but its language support now extends beyond Python to JavaScript, Go, and Java-based development.

ADK also comes with useful development tooling, including a CLI and debugging interface. However, persistence is where you need to pay attention. Its in-memory session services are convenient during development, but that state disappears when the process restarts.

Quick setup

Google ADK also comes with a CLI, so you can scaffold a basic agent project instead of setting everything up by hand.

pip install google-adk
adk create my_agent

Once the project is created, add your model settings and tools in agent.py. You can then run the agent locally and use ADK’s development tools to test it before moving to a persistent setup.

OpenAI Agents SDK

OpenAI Agents SDK keeps the agent model intentionally small. The main pieces are agents, tools, handoffs, guardrails, sessions, tracing, and a runner, which makes the execution flow easier to follow than in some larger frameworks.

That simplicity is its biggest advantage. MCP tools are built in, and sessions can preserve working context between agent turns.

But persistent context isn’t the same as durable execution. If a workflow needs to hold up after a worker crash, remain paused for a long period, or resume after an infrastructure restart, you’ll still need an external durable execution system.

Quick setup

Install the Python SDK and set your OpenAI API key:

pip install openai-agents
export OPENAI_API_KEY="your_key"

Create an Agent with a name and instructions, then run it with Runner. Tools and handoffs can be added as the workflow grows. TypeScript teams can use @openai/agents instead:

npm install @openai/agents zod

LlamaIndex Workflows

LlamaIndex Workflows uses event-driven orchestration for multi-step AI applications. It fits particularly well with LlamaIndex’s wider focus on retrieval, documents, ingestion, and RAG, making it a strong option when the agent is part of a data-heavy pipeline rather than a standalone assistant.

Its workflow context can carry state alongside LlamaIndex’s memory and retrieval components. That said, complex tool workloads can be unreliable. The higher-level agent-workflow abstraction has documented handoff failures – after one agent hands off, the receiving agent can stop responding and the workflow halts.

Quick setup

If you only need the workflow layer, you can install it on its own.

pip install llama-index-workflows

From there, define each step in the workflow with the @step decorator and pass data between steps using events. Run the workflow with workflow.run() and add LlamaIndex’s retrieval or model packages only when you need them.

PydanticAI

PydanticAI takes a validation-first approach to agent development. It brings Pydantic’s type-safe style into model outputs, dependencies, tools, and application state, which makes it especially attractive for Python teams that want their agent contracts to remain explicit.

That tighter structure can make agents easier to validate and debug. However, unlike bigger names and frameworks, the Pydantic ecosystem is still smaller than older frameworks such as LangChain.

Durable execution also relies on integrations with external workflow systems rather than being the main job of the core agent runtime.

Quick setup

PydanticAI has a simple Python setup.

pip install pydantic-ai

After installing it, connect your model provider and create an Agent. You can then add tools as Python functions and define typed outputs when you want the agent’s responses to follow a specific structure.

Mastra

Mastra is built specifically for TypeScript teams and combines agents, workflows, memory, MCP support, observability, and a development environment in the same ecosystem. Its workflows can branch, loop, suspend, and persist state, which gives product teams a fairly complete stack without moving orchestration into a separate Python service.

The flip side is that Mastra makes more architectural decisions for you, which can feel restrictive. Teams should also look closely at the operational cost of hosted memory as the memory layer generates costs beyond the subscription, which is easy to miss when budgeting.

Quick setup

Mastra can scaffold a TypeScript project for you through its CLI.

npm create mastra@latest

The setup walks you through the project name, model provider, API key, and the components you want to include. Your agents, tools, and workflows then live under src/mastra/, and you can run the project locally while building and testing it.

smolagents

Hugging Face’s smolagents takes the opposite approach and keeps the framework intentionally lightweight. Its CodeAgent can express actions as Python code, while ToolCallingAgent uses more conventional structured tool calls. That makes it useful for learning, experimentation, research, and smaller agents where you want to see most of the execution loop yourself.

It also supports MCP tools without requiring a large integration layer. The limitation, however, is production durability; smolagents isn’t designed as a durable workflow engine for long-running jobs that must hold up if a process restarts. Also, some parts of the API are still experimental, which might not be too suitable if your team is more mature and heavy on reliability.

Quick setup

For a basic smolagents project, install the library with its default toolkit.

pip install "smolagents[toolkit]"

Then choose a model and create either a CodeAgent or ToolCallingAgent. Pass in the tools you want the agent to use, then call agent.run() with the task. If you also need MCP support, install the mcp extra instead.

Which AI agent framework should you choose?

There isn’t one guaranteed best AI agent framework for every project. Most of the options in this article can handle tools, state, workflows, and multi-agent patterns. So, the better way to go about it is to consider the constraints your team already has.

These 4 questions usually narrow down what you should consider:

• What language does your team work with? Python gives you the widest range of choice such as Pydantic. TypeScript points more strongly toward Mastra and similar options, while .NET makes Microsoft Agent Framework an obvious choice.
• Are you already committed to a cloud ecosystem? Teams already deep in Microsoft or Google infrastructure may get more value from their respective frameworks. However, staying cloud-neutral keeps more options open.
• Do you need 1 agent or several cooperating agents? CrewAI makes role-based multi-agent systems easy to model, while LangGraph gives you more control over how agents and steps are connected.
• Does the work need to survive a process restart? If yes, look closely at checkpointing and durable execution rather than memory alone.

If you’re looking for the top 10 AI agent frameworks in 2026, the main options to consider are LangChain, CrewAI, Microsoft Agent Framework, Google ADK, OpenAI Agents SDK, LlamaIndex Workflows, PydanticAI, Mastra, smolagents, plus Haystack as an honorable mention.

The best choice for your team still comes down to language, cloud environment, multi-agent needs, and how well the workflow holds up after a restart.

Connecting your agent to data beyond its training set

An agent needs tools to work with information outside the model itself. Model Context Protocol (MCP) is the standard interface between an agent and the tools or data outside its own codebase. An MCP server exposes a set of tools once, and any compatible framework or client can call them.

There’s no need to build custom glue code for each integration. The same documentation server, browser tool, or data source can work across different MCP-compatible frameworks. So, if you switch frameworks later, you can keep the same tools instead of rebuilding every connection from scratch.

Most of the frameworks in this article now support MCP either directly or through an adapter, which makes it much easier to connect agents to external tools and live data.

Which servers are worth adding

That depends on what your agent needs to do. Documentation servers can pull current library references, issue-tracker servers can provide context around bugs and development work, and browser-automation servers can let an agent interact with interfaces that aren’t exposed through an API.

When the agent needs current information from websites rather than relying on its training data, a web data server can expose that information as another tool.

For more examples, see our guide to the top 10 MCP servers for AI workflows. And if you’re setting one up for the first time, our tutorial on how to set up an MCP server covers everything you need to know.

Why agents fail in production: the data layer

Choosing the right framework solves only part of the problem. All the frameworks in this guide can route tasks, retry failed steps, and keep track of state. The uncertainty shows up when the agent reaches outside the framework and tries to get live data from the web.

What really breaks?

A failed web request doesn’t always look like a failure. Most times, a website might return a challenge page, send an empty page, or return a rate-limit message instead of popping an error. The agent still receives text, assumes it got the right page, and starts reasoning with the wrong information.

As you might have also discovered, simply retrying also doesn’t help as each new attempt uses more tokens, but the result may stay the same because the problem isn’t with the agent’s reasoning, but with the data coming back from the tool. 

The 4 Failure Modes

• Anti-bot challenges. Some websites detect automated requests and return a challenge or verification page, often with a 200 status. If the web tool doesn’t recognize that response, the agent may treat the challenge text as useful data and continue the task.
• Geo-restricted content. Location can change what a website returns. An agent requesting data for one region may be redirected or shown a different version of the page based on its IP address. This is why it is often smart to empower your agent with strong residential or mobile proxies.
• Client-side rendering. Many websites load important content with JavaScript after the first page response. A basic HTTP request can therefore return an HTML page without the desired content.
• Rate limiting by IP. Sending too many requests from the same IP can trigger rate limits or temporary blocks. Also, retrying the request from that same IP usually doesn’t solve the problem.

These issues are becoming more important as automated traffic grows. Decodo’s bot traffic vs. human traffic report found that bots accounted for 57.4% of web requests in June 2026, overtaking human traffic. Agentic AI traffic also grew about 7,851% year over year, making it one of the fastest-growing sources of automated web activity.

Where managed infrastructure takes over

Once a website starts returning challenges, limiting requests by IP, serving content based on location, or requiring JavaScript rendering, changing the prompt or framework won’t fix it. At that point, the problem has moved from orchestration to infrastructure.

A managed web data layer handles those problems before the result reaches the agent. Instead of working with raw or incomplete HTML, the agent can receive cleaner output such as JSON or Markdown and continue with the task. You can connect it through MCP, the LangChain integration, or a direct HTTP request from your agent’s code. For more on how this works, see our guides to web scraping at scale and AI scraping.

This is where Decodo’s Web Scraping API comes in. It handles JavaScript rendering, proxy rotation, retries, and web access through a pool of 125M+ IPs across 195+ locations.

Limitations and aspects to watch out for

Fast updates

AI agent frameworks change quickly. Even a small update can affect defaults, flags, or cause breaking changes. Pin the version you use in CI and test updates before moving them into production.

Vendor consolidation

The ecosystem is still settling and frameworks are still changing shape. For example, Microsoft Agent Framework is now the forward path for work that previously sat across AutoGen and Semantic Kernel, so older tutorials may describe frameworks or patterns that have since changed. Always remember to check the publication date.

Observability

Tracing support isn’t consistent across frameworks. Some support OpenTelemetry or detailed tracing natively, while others need extra setup. This is key when something goes wrong and you need to follow the agent’s model calls, tool calls, retries, and handoffs back to the cause.

Benchmarks

Most of these frameworks now offer similar core features, so a simple benchmark table can be misleading. In practice, the model, tools, prompts, state setup, and workload often matter more than the framework itself.

If your agent will work with live web data, it’s also worth checking the target before building around it. See Decodo’s guide on how to check whether a website allows scraping.

Final thoughts

Most AI agent frameworks now cover the same core orchestration needs, so the better choice usually comes down to language fit, durability, and observability.

The 4 questions in this guide should help you narrow down your options. Consider what language you use, which cloud you already work with, whether you need multiple agents, and how well the workflow holds up after a restart.