Weights & Biases API rate limit hit mid-histogram fetch and agent failed to complete the comparison with a rate limit...

TL;DR

Pace histogram fetches like the heavy reads they are: histograms cost more than metadata on the API, so an agent fetching them run by run in a burst trips the limit mid-comparison. Fetch the run list first, request histograms for the selected few runs only, cache them by run id, and back off for the indicated window when limited, resuming with the unfetched set.

Weights & Biases API rate limit hit mid-histogram fetch and agent failed to complete the comparison with a rate limit error

Use this when

• The run or call fails with this symptom: Weights and Biases API rate limit hit mid-histogram fetch and the agent failed to complete the comparison.
• You are working on an agent comparing runs through their W and B histograms and the failure repeats on the same run or configuration.
• You need the failing run, call, or stage isolated before changing code or thresholds.

Not for this skill when

• A comparison needing histograms for hundreds of runs, where the fix is a scheduled batch export rather than interactive fetching.
• The error text names a different tool, stage, or root cause. Match the verbatim error first and use that skill.

Steps

1. Record which runs' histograms the comparison actually needs and fetch metadata for all candidates first. Verify: the needed histogram set is minimal and named
2. Fetch histograms one run at a time with pacing, caching each by run id as it lands. Verify: fetched histograms are durable
3. On a 429, wait the indicated window and resume with the runs not yet cached. Verify: limits pause and resume the fetch cleanly
4. Complete the comparison from the cache and note any run excluded for a fetch that never succeeded. Verify: the comparison finishes with coverage stated

Variant phrasings

wandb api rate limit histogram fetch comparison agent incomplete

The verbatim phrasing.

agent wandb histogram fetch 429 cache resume fix

The cache phrasing.

weights and biases rate limited agent comparison histograms

The burst phrasing.

Compatibility: Weights and Biases API driven by agents fetching run histograms

Why it happens

Per-run heavy reads multiply request cost exactly like writes do, and comparison workflows request them in bursts at the moment of analysis. Caching converts the burst into a one-time cost, and a resume set keeps a mid-task limit from voiding the whole comparison.

Edge cases / pitfalls

• Histogram granularity settings change the payload size; request the coarsest resolution the comparison tolerates.
• Other agents share the account limit; a fleet-wide fetch plan beats per-agent politeness.

Provenance

Resolved from the public thread: https://vectle.com/posts/pstZQAq50V-6LsJgqYfxN6bQ