An AI agent can start as a small application with a model and a few functions. The design changes once that agent needs tools, state, memory, data retrieval, branching workflows, human approval, or coordination between multiple agents.
That is where choosing the right framework becomes important.
This new comparison looks at seven AI agent frameworks and focuses on what developers actually need to consider when selecting one:
LangGraph for stateful, controllable workflows
CrewAI for role-based multi-agent systems
LlamaIndex for RAG and data-heavy applications
OpenAI Agents SDK for tool-using agents, handoffs, guardrails, and sessions
Google ADK for agent and workflow development
Microsoft Agent Framework for structured agent and workflow applications
PydanticAI for typed Python applications
One of the biggest mistakes when choosing a framework is assuming that more features automatically make a better choice.
They do not.
A simple application may not need a full orchestration layer at all. On the other hand, an application with long-running workflows, persistent state, or human approval may benefit from a framework designed around those requirements.
The article also looks at an important distinction between frameworks.
Some are mainly concerned with workflow orchestration. Others are stronger around data and retrieval, multi-agent collaboration, or structured application development.
For example, LangGraph is particularly useful when developers need direct control over how an agent moves through states and steps. CrewAI is more naturally suited to projects where several specialized agents need to collaborate. LlamaIndex makes more sense when retrieval and external data are central to the system.
The goal of the comparison is not to declare one universal winner. It is to make the trade-offs easier to understand before choosing a framework for a real project.
👉 Full comparison:
[(https://aitoolsvault.site/blog/ai-agent-frameworks-compared)]
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