Choosing an AI Agent Framework for a Real Project

One thing that becomes obvious when building agent-based products is that the framework decision is really an architecture decision.

A prototype may only need an LLM API and a few functions. But as soon as the product needs persistent state, tool calls, branching workflows, data retrieval, human approval, or multiple agents, the trade-offs become much more important.

I put together a comparison of seven frameworks:

LangGraph, CrewAI, LlamaIndex, OpenAI Agents SDK, Google ADK, Microsoft Agent Framework, and PydanticAI.

The interesting part is that they are not all solving exactly the same problem.

LangGraph is focused on state and workflow control. CrewAI is built around collaborative agent teams. LlamaIndex is especially relevant when the product depends on retrieval and external data. OpenAI Agents SDK provides a more direct agent-development approach with tools, handoffs, guardrails, and sessions.

Google ADK and Microsoft Agent Framework take broader workflow approaches, while PydanticAI is a strong fit for developers building typed Python applications.

For an indie product, this distinction matters because adding a framework also adds architecture and maintenance decisions.

A framework should solve a real problem in the application. If the project does not need persistent state, multi-agent coordination, or complex orchestration, a simpler architecture may be enough.

The full article compares the seven options and breaks down which types of projects each one fits best.

👉 Full article:
https://aitoolsvault.site/blog/ai-agent-frameworks-compared

Question for builders: Which AI agent framework are you using, and what made you choose it?