What Are the Best Frameworks for Developing AI Agents?

There is no single best framework for developing AI agents. Instead, the choice depends on the specific needs of the project and how much you are willing to accept in terms of lock-in. Every framework handles the same core tasks: running the loop, calling tools, maintaining memory, planning, and providing a traceable record of the interactions. The main difference lies in how much control they give you and how difficult it is to migrate away from them when you outgrow them.

Gartner predicts that by 2027, 40% of enterprise AI deployments will involve multi-agent systems. This long tail of potential frameworks makes the decision even more crucial. The decision should be based on a careful evaluation of the trade-offs each framework offers.

LangGraph is a top choice for those who want full control over their agent's architecture. It allows you to model the agent as an explicit graph of nodes and edges, with typed state, and provides durable execution through checkpointing after every super-step. However, the development server requires Elastic-2.0, which limits its use as a managed service. While the dev server is a catch, the control it offers is valuable for those who need it.

CrewAI is designed to facilitate the creation of a team of AI agents, each with its own roles, goals, and backstories. It has gained significant popularity, with 60,700 stars on GitHub. AutoGen, on the other hand, focuses on enabling a conversation between AI agents. Microsoft has recently moved on from AutoGen, issuing version 1.0 as the successor to both frameworks.

AutoGen is now only receiving bug fixes and security patches. If you are starting fresh, building on AutoGen may not be the best idea, as it is built on a framework that its creator has already retired.

Semantic Kernel is an established framework with well-supported SDKs for C#, Python, and Java. It is particularly good at wrapping existing enterprise APIs as plugins. However, Microsoft has made it clear that Agent Framework is the path for new work, making Semantic Kernel a safer choice for organizations with legacy systems but not the default option.

The OpenAI Agents SDK provides a thin set of primitives, including a Runner that handles the loop and tracing by default. It is suitable for small self-hosted agents on OpenAI models but should not be used as the foundation for an entire enterprise platform. The API is currently version 0.24.0 and has not yet stabilized.

Google ADK offers a code-first, model-agnostic approach through LiteLLM. It supports the Agent-to-Agent protocol natively, includes a real evaluation harness, and can be deployed on Cloud Run or any Python environment. Unlike Google Cloud, it is not tied to the Google ecosystem. With 2.14.1 version and an Apache 2.0 license, it stands out as the most comprehensive framework among the options, often overlooked in discussions.

Lastly, LlamaIndex provides a data-first approach to building AI agents. It excels at answering questions over internal documents using retrieval-grounded agents and offers a FaithfulnessEvaluator to assess the model's reliability. However, if the primary goal is to act rather than retrieve information, LlamaIndex might not be the best fit.