AutoGen or MetaGPT for code generation tasks? Honest review from a 10-person startup

Hi everyone,

I'm spearheading the tech procurement and vendor evaluation for our small but growing startup (10 people, Series A funded). We're looking to integrate an AI agent framework specifically to automate and assist with a significant portion of our code generation tasks. This includes generating boilerplate, API integrations, data processing scripts, and even some unit tests. Our stack is primarily Python-based.

After an initial market scan, AutoGen and MetaGPT have emerged as the top contenders in our internal discussions. I've spent the last two weeks running parallel, small-scale evaluations on both, but I'm keen to hear from this community's real-world experiences before we commit to a full implementation.

My primary evaluation criteria are:
* **Total Cost of Ownership (TCO):** This goes beyond just API calls. I'm factoring in development time for setup and maintenance, computational resources needed to run the agents reliably, and the complexity of the infrastructure required.
* **Quality & Reliability of Output:** How often does the generated code run correctly on the first try? How much manual correction and debugging is typically required post-generation? Does the framework encourage a structured, reviewable process?
* **Implementation Timeline & Learning Curve:** For a small team without dedicated ML engineers, how long does it take to go from a working prototype to a stable, integrated workflow? Is the documentation sufficient for troubleshooting?
* **Exit Strategy & Flexibility:** If we outgrow the framework or find it unsuitable in 12 months, how coupled will our processes be to it? Can we easily extract our patterns and prompts?

From my preliminary testing:
* **AutoGen** feels incredibly flexible with its conversational agent design. The ability to have a "User Proxy" agent that executes code and provides feedback seems powerful for iterative refinement. However, I'm concerned about the potential for long, looping conversations that burn tokens without converging on a correct solution. Setting up a coherent multi-agent workflow for a specific task required more upfront prompt engineering than I anticipated.
* **MetaGPT's** strong emphasis on replicating a software company structure (with Product Managers, Architects, Engineers) is conceptually appealing for standardization. The idea of getting a structured output like a PRD or a list of tasks before code generation is attractive. In practice, though, I found it to be quite heavy; it generates a *lot* of intermediate artifacts, which feels like overkill for many of our smaller tasks. I'm also not entirely clear on the computational overhead of running all these "roles."

My specific questions for the community are:

1. For those who have used both in production, what was the decisive factor in choosing one over the other for code generation? Was it raw output quality, operational simplicity, or something else?
2. Regarding TCO, which framework ended up being more "expensive" in terms of ongoing developer hours for supervision and adjustment? Did one require significantly more GPU/RAM resources in your deployment?
3. Have you found effective ways to limit runaway costs (like infinite agent loops in AutoGen or excessive artifact generation in MetaGPT)? Are there specific configuration patterns you'd recommend?
4. How did the frameworks differ in handling a simple, well-defined coding task versus a more complex, ambiguous one? Did one scale better in complexity?

I'm leaning towards a more controlled, deterministic process, but I don't want to stifle potential creativity or problem-solving. Any insights, especially from smaller teams with limited DevOps bandwidth, would be immensely valuable.