I’ve spent the past two weeks building with the Assistants API. Not toy examples – actual tools that real people will use. Here is what I found.
The Good: Speed to Something Real
I built an internal documentation assistant for a fintech project in about four hours. Upload the docs, write a focused system prompt, wire up a simple Go client that manages threads. Done. The retrieval isn’t perfect, but it’s good enough for “which endpoint handles X” type questions. Previously this would have required a vector store, an embedding pipeline, chunking logic, and a retrieval chain. Now it’s an API call.
The code interpreter is surprisingly useful. I hooked it up to a tool that lets internal users ask data questions in plain English. “How many transactions failed last week?” gets translated into Python, executed in OpenAI’s sandbox, and the result comes back formatted. It took me a day. Building a safe code execution sandbox from scratch would have taken a week minimum.
The Bad: Opacity Everywhere
The retrieval is a black box. I can’t control how it chunks my documents. I can’t see what it retrieved before generating an answer. I can’t tune the similarity threshold or re-rank results. For the documentation assistant, this is tolerable – the stakes are low and approximate recall is fine.
For anything involving financial data at the fintech company, it’s a non-starter. I need to know exactly what context the model saw. I need to audit the retrieval path. I need to explain to compliance why the system gave a specific answer. The Assistants API can’t do any of that.
Thread management is also trickier than it looks. Threads accumulate context over time, and stale context degrades answers. I learned this the hard way when the documentation assistant started mixing up API versions because it was carrying context from a conversation about v1 into a question about v2. Now I have a policy: new thread for every topic change. It’s crude but it works.
The Ugly: Runs Are Flaky
A “Run” is one execution of an assistant against a thread. It can succeed, fail, stall, or time out. In my first week, I had runs that just… hung. No error. No timeout. Just pending forever. I added my own timeout logic around every run, with a hard kill after 30 seconds and a retry with a fresh thread if it fails twice.
ctx, cancel := context.WithTimeout(ctx, 30*time.Second)
defer cancel()
run, err := client.CreateRun(ctx, threadID, assistantID)
if err != nil {
return fmt.Errorf("create run: %w", err)
}
// Poll until complete or timeout.
for {
status, err := client.GetRun(ctx, threadID, run.ID)
if err != nil {
return fmt.Errorf("check run status: %w", err)
}
if status.Status == "completed" {
break
}
if status.Status == "failed" || status.Status == "expired" {
return fmt.Errorf("run %s: %s", status.Status, status.LastError)
}
time.Sleep(500 * time.Millisecond)
}
This isn’t elegant. It works. The API really needs webhooks or server-sent events instead of polling, but we work with what we’ve got.
Where I’m Using It
Internal tools with low stakes. Documentation Q&A, data exploration, onboarding helpers. The Assistants API is perfect here. Fast to build, good enough quality, and the opacity doesn’t matter because the stakes are low.
Prototypes that need to prove value. If the question is “would this feature be useful?” the Assistants API gets you an answer in days instead of weeks. Then you can decide whether to build custom infrastructure for the production version.
Where I’m Not
Anything with compliance requirements. Financial data, personal information, regulated workflows. If I can’t audit the retrieval path and explain every answer, I can’t use it.
Anything that needs precise orchestration. If the workflow involves multiple models, conditional branching, or complex tool chains, the Assistants API is too constrained. You’ll fight the abstraction instead of benefiting from it.
The Verdict
The Assistants API is the right default for a lot of use cases. It’s fast, it’s cheap, and it handles the boring parts – thread management, tool execution, file retrieval – so you don’t have to. The cost is control, and for many applications that’s a trade worth making.
Just go in with your eyes open. Know what you’re giving up. Have a plan for when you need to go custom. And for the love of all that’s holy, add your own timeouts.