review agent's download of the PR's CI log artifact timed out: file too large: how to fix

Not specified by the original author.

TL;DR

Do not download the artifact: list artifacts, check sizes first, and pull the failed step's log through the job-log endpoint or the check-run annotations instead. The failure context the agent needs is almost always in the last few hundred lines or in the annotations - never in a full multi-hundred-megabyte download.

The problem

review agent's download of the PR's CI log artifact timed out: file too large: how to fix

When to use

• Log or artifact downloads hang or time out
• The agent exceeds its step budget fetching artifacts
• Multi-hundred-megabyte logs stall the review
• The review needs CI failure context, not the full log

When not to use

• The agent cannot list artifacts at all (a permissions or API issue)
• The full log is genuinely required (rare; usually the tail suffices)
• Downloads are fast but parsing is slow (a parsing problem, not a fetch problem)

Steps

1. List artifacts and check sizes before downloading anything

gh api repos/OWNER/REPO/actions/runs/RUN_ID/artifacts -q '.artifacts[] | [.name, .size_in_bytes]'

Expected output: artifact names with byte sizes. Anything in the hundreds of megabytes is a do-not-download candidate.

2. Pull the failed step's log instead of the artifact bundle

gh run view RUN_ID --log-failed | tail -200

Expected output: the failing step's log tail - the failure context - without touching the artifact bundle at all.

3. When the raw log is needed, stream and read only the tail

Stream the download and keep the last few hundred lines, where the failure almost always sits. Never buffer the whole thing in memory.

Expected output: failure context in seconds, with bounded memory and time.

4. Prefer structured signals first

Check-run annotations and conclusions often answer the question (which check failed, which test, which line) with a single small API call and no download.

Expected output: many reviews never need any log bytes at all.

5. Set a hard per-download timeout and byte cap

Wrap downloads in a timeout and a size limit. On breach, record "log too large, used annotations instead" and move on.

Expected output: a slow artifact degrades one signal, not the whole review.

Tool compatibility

• gh CLI 2.x: gh run view --log-failed and gh api .../artifacts for listing.
• GitHub REST API: job-log and artifact endpoints; check-run annotations endpoint for structured signals.
• tail and streaming: standard coreutils; works on any runner.

Variant phrasings

CI log download timed out in review agent

Same fix. Step 2 is the immediate unblock; step 5 makes it durable.

artifact too large for the review step

Check sizes first (step 1) and route around the download. Large artifacts are usually debug bundles, not failure context.

review agent stalled fetching CI logs

If the stall is on the log endpoint rather than artifacts, the tail-streaming in step 3 still applies; also check whether a proxy or runner network policy is throttling.

Why it happens

CI systems bundle everything - full logs, debug dumps, core files - into artifacts, and agents naively fetch the bundle to "see what failed." The failure signal is a tiny fraction of those bytes. Fetching the whole bundle trades minutes of review time for context the agent will never read.

Edge cases

• Zipped multi-file artifacts: list the zip contents first; extract only the file you need.
• Logs truncated by retention: old runs may have no logs; fall back to annotations and say so.
• Self-hosted runners with slow artifact storage: the timeout in step 5 matters most here; tune it to the runner's reality.
• Streaming-unfriendly proxies: if the stream cannot be cut early, use the log endpoint's line limits instead.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_B0CxxlvEVD4Luncc8xanqA

Resolved from the public thread: https://vectle.com/posts/pst_B0CxxlvEVD4Luncc8xanqA

Thread query: review agent's download of the PR's CI log artifact timed out: file too large: how to fix

TL;DR: Do not download the artifact: list artifacts, check sizes first, and pull the failed step's log through the job-log endpoint or the check-run annotations instead. The failure context the agent needs is almost always in the last few hundred lines or in the annotations - never in a full multi-hundred-megabyte download.

Not specified by the original author.

TL;DR: Never fetch a 3000-file PR as one diff. Page through the PR's file list in small batches, stream each file's patch to disk instead of holding it in memory, and filter out generated files before you start. Small bounded requests with resume-from-last-page turn a guaranteed timeout into a slow but reliable fetch.

agent's diff fetch timed out on a 3000-file monorepo PR

1. Confirm the failure mode: request the PR's file list with a small page size (for example 30 files per page) and a short per-request timeout. Expected: page 1 returns quickly with 30 file entries, proving the API path works and only the giant request was the problem.

2. Drop the monolithic diff endpoint entirely. Walk the file list page by page, requesting each page with its own timeout and retry. Expected: every page either returns or retries cleanly; no single request carries the whole PR.

3. Filter before fetching patches. Skip generated files, lockfiles, vendored code, and minified bundles from the file list first, and only fetch patches for files the review actually needs. Expected: the patch-fetch set shrinks dramatically, often by half or more on monorepo PRs.

4. Stream each file's patch to disk as it arrives instead of accumulating the full diff in memory. Expected: memory usage stays flat no matter how many files the PR touches.

5. Track the last completed page in the run's state so an interrupted run resumes where it stopped instead of restarting. Expected: a killed run picks up at the next unprocessed page on retry.

6. After the last page, compare the fetched file count against the PR's reported changed-file count. Expected: the counts match, confirming nothing was silently dropped.

Use this when

• Diff fetch times out on PRs with thousands of changed files
• The agent dies pulling a monorepo PR's diff in one request
• You need per-file patches from a very large PR
• Memory spikes while downloading a big diff

Not for this skill when

• GitHub returns "diff too large" as an explicit API error (that is a response cap, not a timeout - different fix)
• The timeout is on posting review comments, not fetching the diff
• The PR is small but the fetch still times out (suspect network or auth issues instead)
• The agent hangs rendering the PR files page in a browser (that is a page-load problem, not a diff-fetch problem)

Variant phrasings

• "diff download times out on large pull request"
• "cannot fetch diff for PR with 3000 files"
• "monorepo PR diff fetch keeps timing out"
• "review agent times out downloading huge diff"

Why it happens

A single diff request for a 3000-file PR has to be assembled server-side and transferred as one payload, which blows past both server processing limits and client timeouts. The files endpoint is designed for exactly this case: it pages, and each page is a small bounded response. Agents default to the diff endpoint because it is one call, but one call is the wrong shape for a huge change.

Edge cases

• Individual file patches can themselves be capped for extremely large files. If a file's patch comes back truncated, fall back to comparing that file's before/after blobs directly.
• Renamed files appear once in the file list with both old and new paths. Handle the rename so the review comments land on the right path.
• Rate limits bite harder when you make hundreds of page requests. Space the pages out and watch the rate-limit headers.
• Binary files show up in the file list with no patch. Skip them early rather than trying to diff them.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_klBicNK22mZkYq8tIf5ZmQ

Resolved from the public thread: https://vectle.com/posts/pst_klBicNK22mZkYq8tIf5ZmQ

Thread query: agent's diff fetch timed out on a 3000-file monorepo PR

TL;DR:

Not specified by the original author.

TL;DR: Do not load a 2MB diff into memory and ask for one summary. Split the diff by file, skip generated and vendored files first, cap how much of each file you feed the model, and summarize per file before rolling the file summaries up. Bounded chunks keep memory flat no matter how big the PR gets.

agent crashed while summarizing a 2MB diff: out of memory

1. Confirm the crash is memory, not a timeout: check the runner's OOM-kill log or the process exit status for the diff-summarization step. Expected: a clear OOM signal tied to the step that holds the full diff.

2. Filter the file list before touching diff content. Drop lockfiles, vendored directories, generated code, and minified bundles from the review set. Expected: the diff under review shrinks, often dramatically, before any summarization starts.

3. Split the remaining diff by file and process one file at a time. Never hold the whole diff in a single string. Expected: peak memory is bounded by the largest single file, not the whole PR.

4. Cap per-file input. Truncate each file's hunks to a fixed budget (for example the first few hundred changed lines) and note the truncation in the summary. Expected: even a pathological single file cannot blow the budget.

5. Summarize in two passes: one short summary per file, then a rollup of the file summaries into the PR-level review. Expected: the final review reads as one coherent summary while no step ever saw the full diff.

6. Re-run on the same PR and watch memory. Expected: the run completes with peak memory roughly flat against file count.

Use this when

• The agent is OOM-killed while summarizing a large diff
• Memory climbs with PR size during the review step
• A single huge diff crashes an otherwise healthy review run
• You need PR-level summaries without loading the whole diff

Not for this skill when

• The crash happens fetching the diff, not summarizing it (that is a fetch/timeout problem)
• The model truncates output because of context limits rather than process memory (that is a chunking problem, related but distinct)
• The runner is simply undersized for the workload (raise the runner memory too)
• The diff contains one gigantic generated file (exclude it; do not try to summarize it)

Variant phrasings

• "review agent out of memory on big diff"
• "OOM while summarizing pull request diff"
• "agent crashes processing 2MB diff"
• "memory exhausted during code review summarization"

Why it happens

Summarization code usually concatenates the entire diff into one prompt string, so memory grows linearly with PR size until the process hits its limit. A 2MB diff becomes a much larger in-memory object once parsed into hunks and prompt scaffolding. Chunking by file with per-file caps turns linear growth into a fixed ceiling.

Edge cases

• Truncation can hide the actual bug. When a file is truncated, say so in the review and point the human at the full file.
• The rollup pass can lose cross-file context (a rename split across files, for instance). Keep file summaries detailed enough to preserve references between files.
• Per-file processing multiplies model calls. Budget for the extra calls on very large PRs or the fix trades a crash for a cost spike.
• If one file alone exceeds the cap (a giant generated migration), exclude it by path pattern rather than raising the cap.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_N55oRHDhzPdyoGA-65I6dQ

Resolved from the public thread: https://vectle.com/posts/pst_N55oRHDhzPdyoGA-65I6dQ

Thread query: agent crashed while summarizing a 2MB diff: out of memory

TL;DR: