Troubleshooting

Atlas error: Provider response headers timed out after <ms>ms

Updated 7 min read

Atlas fails the assistant message with ProviderHeaderTimeoutError and the message "Provider response headers timed out after <ms>ms" because Atlas enforces a header timeout on provider streams and the provider did not send response headers within it. HeaderTimeoutError is constructed with the elapsed ms, then mapped to a retryable APIError carrying the timeout in metadata. Retry first, since the mapped APIError is marked isRetryable true, then check network egress, because a proxy that buffers responses will delay headers past the limit.

What does Provider response headers timed out after <ms>ms mean in Atlas

Provider response headers timed out after <ms>ms means Atlas enforced its header timeout on a provider stream and got 0 response headers back before the limit elapsed. HeaderTimeoutError is constructed with the elapsed ms, so the value printed in the message is the real time Atlas waited before giving up.

ProviderHeaderTimeoutError is about latency to first byte, not about the size of the request or the validity of the credential. Atlas opens a stream to the provider and waits for response headers. When that wait exceeds the header timeout, Atlas constructs HeaderTimeoutError with the elapsed ms and maps it to a retryable APIError that carries the timeout in metadata. Nothing was rejected. The provider, or something on the network path between you and the provider, simply took too long to say anything at all. The elapsed ms printed in the message is your first piece of evidence.

How do I fix ProviderHeaderTimeoutError in Atlas

Step 1 of the documented fix is to retry, because the mapped APIError is marked isRetryable true. A header timeout in Atlas is frequently a one-off slow start, and a second attempt often gets response headers back well inside the limit without any change on your side.

Retry before you investigate. Atlas marks the mapped APIError isRetryable true precisely because header latency varies, and a request that timed out waiting for headers can succeed immediately on the next attempt. If retries keep hitting ProviderHeaderTimeoutError, the slowness is not random and you have a network path or an endpoint problem. At that point the useful checks are network egress, because a proxy that buffers responses will delay headers past the limit, and direct measurement of first-byte time against the provider baseURL.

Can a buffering proxy cause an Atlas header timeout

Yes. Step 2 of the documented fix is to check network egress, because a proxy that buffers responses will delay headers past the limit and cause ProviderHeaderTimeoutError in Atlas. A buffering proxy holds the provider's response until it has collected enough of it, so Atlas sees 0 headers while the timeout counts down.

Buffering is the most common non-obvious cause of a header timeout. A streaming-aware proxy passes response headers straight through the moment the provider sends them. A buffering proxy does not: it accumulates the response body first, which means the headers Atlas is waiting for do not arrive until the provider has largely finished generating. Against a streaming model, that delay easily exceeds the header timeout, and Atlas raises HeaderTimeoutError with the elapsed ms. Check whether the egress path between Atlas and the provider passes through a proxy that buffers, and ask for streaming passthrough if it does.

How to measure first-byte time against the provider baseURL

Step 3 of the documented fix is to test the provider baseURL directly and see how long first-byte takes. Measuring outside Atlas separates a slow endpoint from a problem inside Atlas, and it gives you a number to compare against the elapsed ms that HeaderTimeoutError already printed in the error message.

The direct test is the diagnostic that tells you where the latency lives. Call the provider baseURL from the same machine and the same network path Atlas uses, and time how long it takes for the first byte to come back. A first-byte time in the same range as the elapsed ms in the ProviderHeaderTimeoutError message confirms the slowness is on the network path or the provider endpoint, not in Atlas. A fast direct response while Atlas keeps timing out points back at how the request is routed when Atlas makes it, which usually means a proxy is in the path for one and not the other.

When to switch providers because of slow response headers

Step 4 of the documented fix is to switch providers or models if 1 endpoint is consistently slow to send headers. An endpoint that repeatedly triggers ProviderHeaderTimeoutError, while others respond normally from the same machine, is telling you the endpoint is the problem rather than your network or your Atlas configuration.

Consistency is the deciding factor. An occasional header timeout is what the retryable classification exists for, and Atlas handles it. A specific provider endpoint that times out again and again, while a different provider on the same network path returns headers promptly, is not something a retry will solve. Atlas lets you switch the active model and provider on the fly with favorites and recents, so moving the session off the slow endpoint is quick. Keeping a second provider configured in advance is what makes that switch available when you need it.

How to verify the header timeout fix worked

Verify the ProviderHeaderTimeoutError fix by sending 1 request and confirming that response headers arrive without a timeout. A completed message means the provider stream sent headers inside the limit, so Atlas never constructed HeaderTimeoutError with an elapsed ms value for that request.

Look for consistency rather than a single success, because the mapped APIError is retryable and an isolated pass could simply be a fast attempt on a still-slow path. Send several requests. If none of them raise ProviderHeaderTimeoutError, the header latency is genuinely back inside the limit. If timeouts recur intermittently, compare the elapsed ms values printed in the messages against the first-byte time you measured directly against the provider baseURL. Rising elapsed ms across attempts points at a buffering proxy or a degrading endpoint rather than a resolved problem.

How to fix it

  1. 01Retry the request. The mapped APIError is marked isRetryable true.
  2. 02Check network egress: a proxy that buffers responses will delay headers past the limit.
  3. 03Test the provider baseURL directly to see how long first-byte takes.
  4. 04Switch providers or models if one endpoint is consistently slow to send headers.

Frequently asked questions

how to fix Provider response headers timed out in atlas
Retry first, since the mapped APIError is marked isRetryable true. Then check network egress for a proxy that buffers responses, test the provider baseURL directly to see how long first-byte takes, and switch providers if one endpoint is consistently slow.
what is ProviderHeaderTimeoutError in atlas
ProviderHeaderTimeoutError means Atlas enforced its header timeout on a provider stream and no response headers arrived in time. HeaderTimeoutError is constructed with the elapsed ms, then mapped to a retryable APIError carrying the timeout in metadata.
does atlas retry a header timeout
Yes. The APIError that Atlas maps a header timeout to is marked isRetryable true, so retrying is the first documented fix. Header latency varies, and a second attempt often gets response headers back inside the limit.
can a proxy cause Provider response headers timed out in atlas
Yes. A proxy that buffers responses holds the provider's reply before passing it on, which delays headers past the limit and triggers ProviderHeaderTimeoutError. Ask for streaming passthrough on the Atlas egress path.
how do I test if my provider endpoint is slow for atlas
Test the provider baseURL directly from the same machine and network path Atlas uses, and time first-byte. Compare that against the elapsed ms printed in the ProviderHeaderTimeoutError message.
what does the ms number in the atlas header timeout message mean
The ms value in Provider response headers timed out after <ms>ms is the elapsed time Atlas actually waited for response headers. HeaderTimeoutError is constructed with that elapsed ms and carries the timeout in metadata.
should I switch providers because of atlas header timeouts
Switch providers or models if one endpoint is consistently slow to send headers while others respond normally from the same machine. An occasional timeout is what the retryable classification handles, a persistent one is an endpoint problem.

Try Atlas in your terminal

The terminal-native AI coding agent. Free core, single binary.

Install Atlas

Related guides

Atlas for Fastify in 2026

Atlas is a terminal-native AI coding agent for Fastify in 2026. It reads plugin encapsulation and JSON schemas, then runs node --test behind a permission prompt.

Atlas with Mistral Nemo: The 128K Small Model Slot in 2026

Running Atlas on Mistral Nemo in 2026: 128,000 tokens of context in a 12B model at $0.15 / 1M input tokens, with the Tekken tokenizer that compresses code.

Atlas with GPT-4o in 2026: A 128K Legacy Model with Dated Snapshots

GPT-4o runs in Atlas at $2.50 per Mtok input and $10 per Mtok output on a 128K context with a 16,384 output cap. Best for quick lookups and reproducible baselines.

Atlas for Angular in 2026

Adopt Atlas, the terminal-native AI coding agent, for your Angular projects in 2026. Enhance development with intelligent code search, secure local embeddings, and granular control over AI actions.

Atlas with Qwen3.6 Max Preview: The Top 3.6 Tier, Reviewed in 2026

Qwen3.6 Max Preview is Alibaba's top Qwen3.6 tier at $1.30 per Mtok input and $7.80 per Mtok output, with 256K tokens (262,144) of context. What it is worth inside Atlas.

Atlas with Qwen3 8B: The Cheap Thinking Model for small_model in 2026

Qwen3 8B in Atlas for 2026: hybrid thinking at $0.18 per Mtok input and $0.70 per Mtok output, 128K tokens (131,072), and a natural small_model slot fit.

Atlas with GPT-5 Pro: The 272,000 Token Output Ceiling in 2026

GPT-5 Pro in Atlas: the only OpenAI model with a 272,000 token max output, priced at $15 per Mtok input, $120 per Mtok output on a 400K tokens window.

Atlas for PyTorch: Terminal-Native AI Coding for nn.Module, Devices, and Autograd in 2026

Atlas is a terminal-native AI coding agent for PyTorch in 2026, where device placement, autograd, and DataLoader worker counts cause most bugs and most slowness.

Browse this resource hub