AutoGen vs CrewAI vs LangGraph vs PydanticAI vs Google ADK vs OpenAI Agents .... Which Multi-Agent Framework is Best?

And the winner is ...

One of the more common questions I get is “Which Multi-Agent Framework is Best”?

I built this interactive tool to help you compare 6 leading multi-agent frameworks across 10 dimensions (developer experience, async capabilities, state management, event driven design, component serialization etc).

The frameworks covered include:

• AutoGen (the new v0.4 api released in Jan 2025)

• Google ADK

• LangGraph

• LLamaIndex

• Openai Agents SDK

• PydanticAI

The goal is not to provide a perfect assessment (see the caveats and limitations below), but to offer an initial comparison of the capabilities covered by each framework. In this related article, I cover the 10 dimensions in detail and also some guidance on “if you need a framework” at all.

10 Ways to Critically Evaluate and Select the Right Multi-Agent Framework

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Before You Begin - look at Some Code

In addition to reading this post, I highly recommend that you start by looking at code implementations of agents across each frameworks. To make this easy, I have implemented a "Hello World" agent across all 6 frameworks and created an interactive tool to let you compare each implementation.

The tool lets you do a few interesting things:

• View code for each framework

• Pairwise comparison of each framework implementation

• View quantitative metrics like the lines of code to implement the same functionality and runtime for each

Agent HelloWorld
A simple agent that can take an LLM model and a tool (a calculator function) and can respond to a simple task (What is the result of 545.34567 * 34555.34). See tool here.

Compare agent implementations across frameworks here.

How the Analysis is Conducted

To provide a systematic evaluation, each framework is compared across 10 dimensions, with 1-2 specific questions per dimension. All of the data used is available on GitHub. I built a tool here to let you interactively explore each framework

Each framework is evaluated across 10 dimensions and scored.

The scoring methodology works as follows:

• Each question receives a score from 1-10

  • 10 means the framework fully satisfies the requirements

  • 6-9 indicates good to very good implementation

  • 0-5 means the framework barely addresses the requirement

  • For each question, there is a description and evidence link that the user can explore to build better context on why the score is given.

• The dimension score is calculated as the average of the assigned scores for that dimension's questions

• No weighting is applied across dimensions (more on this in the caveats section)

For a detailed breakdown of how each dimension is operationalized with specific questions, please refer to my article "10 Ways to Critically Evaluate and Select the Right Multi-Agent Framework".

See the detailed post on how the dimensions are operationalized with questions.

10 Ways to Critically Evaluate and Select the Right Multi-Agent Framework

Important Caveats and Limitations

There are several important considerations to keep in mind when interpreting this analysis:

• Breadth vs. Depth - The dimensions provide broad coverage to give you sufficient signal, but may not capture fine-grained details. For example, within "Developer Experience," there are nuances around documentation quality that may not be fully represented in the scoring.

• Context-Dependent Importance - Not all dimensions are equally important for every use case. The ability to serialize agents to JSON may be critical for some applications but irrelevant for others, while features like async support, state management, and observability might be universally important. This analysis does not assign weights to dimensions, so you'll need to prioritize based on your specific requirements.

• Rapidly Evolving Landscape - Frameworks are evolving at an extraordinary pace. Features that are missing today might be implemented in the near future, and some capabilities may exist but be poorly documented or not immediately obvious. Readers are encouraged to:

  • Check the latest documentation for each framework

  • Use the evidence section provided as part of each question and score.

  • Test frameworks with small prototypes before committing

  • Consider the release date of this analysis (April 2025) when making decisions

• Implementation Quality Varies - A feature might be technically present but poorly implemented or difficult to use. The scoring attempts to account for this, but hands-on experience may reveal different insights.

So .. Which is the best Framework Out there?

The honest answer is: it depends on what dimensions matter most for your specific use case.

Disclosure
My analysis and scoring are possibly biased here - I have the most experience with AutoGen and I am a contributor.
AutoGen scores high on the declarative specification dimension (ability to serialize any component - agents, teams, termination conditions etc, into JSON that can be reused) - something no other framework supports at the moment. You can remove this dimension in the interactive tool as needed.

For example, if your priority is a straightforward, intuitive developer experience, AutoGen scores quite high here, followed closely by the Google ADK. If built-in deployment options are crucial for your project, Google ADK takes the lead with its comprehensive deployment story.

Using the interactive tool provided with this article, you can select dimensions that matter most for your specific use case and watch how the rankings dynamically adjust to reflect your priorities.

Key Highlights from My Analysis

Several interesting patterns emerged during this comparative exercise:

• Convergent Agent Abstraction - AutoGen, Google ADK, and OpenAI Agents SDK have all converged on a similar parsimonious abstraction for agents, making development more intuitive and consistent across these frameworks.

• User Experience: AutoGen AgentChat provides the most parsimonious api (e.g., you get alot done with very little code). As an example, you can replicate the OpenAI operator agent in AutoGen in 50 lines of code.

• Execution Speed: PydanticAI implementation seems (based on a single run at a single point in time, and my own implementation) to be the fastest followed by OpenAI agents sdk, llamaindex, AutoGen, LangGraph, Google ADK.

• AutoGen's Actor Model - AutoGen's implementation of the actor model provides a clean separation of concerns and excellent scalability potential that stands out in the ecosystem (see the concept of runtimes, enabling distributed agents).

• Google ADK's Comprehensive Design - The Google ADK's session , memory abstractions, event hooks, and deployment capabilities are all exceptionally well designed. Despite being a later entrant to the market, Google seems to have carefully studied earlier frameworks and incorporated their lessons learned.

• Trade-offs Are Inevitable - Each framework makes different design choices that optimize for certain use cases at the expense of others. Understanding these trade-offs is crucial for making the right selection.

Is the Raw Data Available? Yes!

Yes! The complete evaluation data is publicly available and open for community input. The interactive comparison tool is backed by JSON files (one per framework) that contain all the raw scores and justifications. These files are available on GitHub.

I expect that I might have gotten some assessments wrong or missed recent framework updates. If you spot any errors or have additional insights, a pull request with corrections or updates are extremely welcome and appreciated!

Beyond Technical Dimensions: Strategic Considerations

There are several important considerations that may influence your framework choice beyond the technical dimensions outlined in my evaluation:

Ecosystem and Community Factors

• Brand and Organizational Backing - Frameworks supported by major organizations like Google or Microsoft often benefit from better long-term stability, regular updates, and dedicated support teams.

• Community Size and Activity - Larger, more active communities typically mean more examples, tutorials, third-party extensions, and faster help when you encounter problems.

• Documentation Quality - Even powerful frameworks can be frustrating to use if poorly documented. The quality, completeness, and accessibility of documentation significantly impacts adoption speed.

• Third-Party Integrations - The availability of pre-built connectors to tools and services you already use can dramatically reduce development time.

All of the frameworks here are Open source (minimizes lock in risk), but some explore very deep enterprise product integration that can eventually become problematic.

Practical Implementation Concerns

• Support Responsiveness - How quickly are issues addressed? Are maintainers active on GitHub, Discord, or other channels?

• Learning Curve - Framework complexity relative to your team's expertise is a critical consideration. Many users of frameworks, by definition, are not experts—if they were, they might build custom solutions instead.

• Production Readiness - Some frameworks excel in research settings but lack features needed for production deployment, like robust error handling or monitoring.

• Enterprise Features - Security compliance, access controls, and audit logging might be essential in certain environments.