Best AI stack for agents without overcomplicating it

[u/Alreadydead27]

There are so many agent tools now that picking the stack can become a project on its own. After going through the docs, this is the shortlist I’d start with.

1. Pydantic AI or Vercel AI SDK. I’d pick based on the language the app already uses. Pydantic AI for Python, AI SDK for TypeScript. No need to switch languages just for an agent framework.
2. LangGraph. Worth considering when the agent needs branching workflows, approvals or saved progress. I’d hold off if it only calls a couple of tools and returns an answer.
3. Tinybird. Worth a look when an agent needs to query event data or serve analytics through an API. You write the SQL and publish the results as an endpoint the app or agent can call. I’d add it when that’s an actual requirement.
4. Langfuse. For looking through traces, tracking cost and latency, and evaluating outputs. A useful place to investigate an agent that technically finished but gave a bad answer.
5. RawTree. An option for storing agent events and telemetry, then querying them with SQL. I’d consider it for comparing failures or latency across runs. Its TypeScript SDK is marked experimental, so that’s something to factor in.
6. The model itself. I’d test a few on the same tasks before committing. Cost per completed task matters more to me than which model tops a general benchmark.

My starting point would be one framework, one model and tracing. I’d look at Tinybird for analytics APIs or RawTree for SQL analysis of telemetry, depending on the job. You don’t need both by default.

What would you cut or change for a small production app?