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Python API breakage checks — ✅ PASSED

Result: ✅ PASSED

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REST API breakage checks (OpenAPI) — ✅ PASSED

Result: ✅ PASSED

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🧪 Integration Tests Results

Overall Success Rate: 94.1%
Total Cost: $1.20
Models Tested: 4
Timestamp: 2026-04-21 15:39:15 UTC

📁 Detailed Logs & Artifacts

Click the links below to access detailed agent/LLM logs showing the complete reasoning process for each model. On the GitHub Actions page, scroll down to the 'Artifacts' section to download the logs.

• litellm_proxy_moonshot_kimi_k2_thinking: 📥 View & Download Logs
• litellm_proxy_deepseek_deepseek_reasoner: 📥 View & Download Logs
• litellm_proxy_gemini_3.1_pro_preview: 📥 View & Download Logs
• litellm_proxy_anthropic_claude_sonnet_4_6: 📥 View & Download Logs

📊 Summary

📋 Detailed Results

litellm_proxy_moonshot_kimi_k2_thinking

• Success Rate: 100.0% (8/8)
• Total Cost: $0.13
• Token Usage: prompt: 473,663, completion: 6,924, cache_read: 385,280
• Run Suffix: litellm_proxy_moonshot_kimi_k2_thinking_a937440_kimi_k2_thinking_run_N9_20260421_153413
• Skipped Tests: 1

Skipped Tests:

• t08_image_file_viewing: This test requires a vision-capable LLM model. Please use a model that supports image input.

litellm_proxy_deepseek_deepseek_reasoner

• Success Rate: 100.0% (8/8)
• Total Cost: $0.04
• Token Usage: prompt: 693,263, completion: 13,390, cache_read: 633,856, reasoning: 5,467
• Run Suffix: litellm_proxy_deepseek_deepseek_reasoner_a937440_deepseek_v3_2_reasoner_run_N9_20260421_153408
• Skipped Tests: 1

Skipped Tests:

• t08_image_file_viewing: This test requires a vision-capable LLM model. Please use a model that supports image input.

litellm_proxy_gemini_3.1_pro_preview

• Success Rate: 88.9% (8/9)
• Total Cost: $0.45
• Token Usage: prompt: 288,321, completion: 4,349, cache_read: 101,959, reasoning: 2,949
• Run Suffix: litellm_proxy_gemini_3.1_pro_preview_a937440_gemini_3_1_pro_run_N9_20260421_153406

Failed Tests:

• t08_image_file_viewing: Agent did not identify yellow color in the logo. Response: inline_image_0.png (Cost: $0.03)

litellm_proxy_anthropic_claude_sonnet_4_6

• Success Rate: 88.9% (8/9)
• Total Cost: $0.59
• Token Usage: prompt: 398,155, completion: 6,860, cache_read: 292,237, cache_write: 105,588, reasoning: 1,203
• Run Suffix: litellm_proxy_anthropic_claude_sonnet_4_6_a937440_claude_sonnet_4_6_run_N9_20260421_153409

Failed Tests:

• t02_add_bash_hello: Shell script is not executable (Cost: $0.06)

🔄 Running Examples with openhands/claude-haiku-4-5-20251001

Generated: 2026-04-21 15:59:31 UTC

❌ Some tests failed

Total: 52 | Passed: 50 | Failed: 2 | Total Cost: $2.16

Failed examples:

• examples/01_standalone_sdk/31_iterative_refinement.py: Timed out after 600 seconds
• examples/01_standalone_sdk/34_critic_example.py: Timed out after 600 seconds

View full workflow run

@OpenHands check the failed test #2908 (comment)

Should i be worried?

I'm on it! xingyaoww can track my progress at all-hands.dev

No, you should not be worried. These are pre-existing flaky timeouts, not caused by this PR.

Analysis

The two failed examples both timed out after the 600-second limit:

• 31_iterative_refinement.py — COBOL-to-Java iterative refactoring (multi-agent, multi-iteration)
• 34_critic_example.py — Critic-model iterative refinement (multi-agent, multi-iteration)

Why this is not concerning

1. This PR only changes version numbers — the diff is limited to pyproject.toml version bumps and uv.lock updates. No example code, SDK logic, or tool code was modified.

2. 34_critic_example is a known flaky test — it has been timing out across multiple branches in recent days:

   • ❌ Apr 19, main branch (run 24641003865): 34_critic_example timed out
   • ❌ Apr 20, vasco/fork-skills branch (run 24689922595): 34_critic_example timed out
   • ✅ Apr 20, main branch (run 24694300290): Both passed

3. These are inherently time-sensitive LLM-driven examples — both involve multiple LLM iterations with quality thresholds. Whether they complete within 600s depends on model response latency and how many refinement iterations the LLM needs, which varies run-to-run.

Summary

These failures are LLM latency/behavior dependent and unrelated to the release changes. The 50/52 pass rate (96.2%) is consistent with recent runs.

This comment was generated by an AI assistant (OpenHands) on behalf of the user.

Summary

The user asked me to check the failed tests on PR #2908 and whether they should be worried. I investigated and answered the question by posting a comment on the PR.

Answer

No, you should not be worried. The two failed example tests are pre-existing flaky timeouts unrelated to the PR:

• 31_iterative_refinement.py and 34_critic_example.py both timed out after 600 seconds. These are complex, multi-iteration LLM-driven examples whose completion time depends on model response latency and how many refinement loops the LLM needs.

• The PR only changes version numbers in pyproject.toml files and uv.lock — no SDK code, example code, or tool logic was modified.

• 34_critic_example is a known flaky test — it timed out on main (Apr 19) and on another branch (vasco/fork-skills, Apr 20) in recent days, confirming this is not caused by the release PR.

• The 50/52 pass rate (96.2%) is consistent with recent runs.

I posted this analysis as a detailed comment on the PR at: #2908 (comment)

Checklist

• Investigated the specific failed tests referenced in the comment
• Checked the PR diff to confirm no relevant code changes
• Cross-referenced with historical test runs to establish the flaky pattern
• Posted a clear, evidence-based answer on the PR
• No code changes were made (none were needed — this was a question, not a code fix request)

🧪 Integration Tests Results

Overall Success Rate: 86.7%
Total Cost: $3.84
Models Tested: 3
Timestamp: 2026-04-21 18:33:02 UTC

📁 Detailed Logs & Artifacts

Click the links below to access detailed agent/LLM logs showing the complete reasoning process for each model. On the GitHub Actions page, scroll down to the 'Artifacts' section to download the logs.

• litellm_proxy_moonshot_kimi_k2_thinking: 📥 View & Download Logs
• litellm_proxy_deepseek_deepseek_reasoner: 📥 View & Download Logs
• litellm_proxy_anthropic_claude_sonnet_4_6: 📥 View & Download Logs

📊 Summary

📋 Detailed Results

litellm_proxy_moonshot_kimi_k2_thinking

• Success Rate: 80.0% (4/5)
• Total Cost: $0.89
• Token Usage: prompt: 3,761,286, completion: 43,863, cache_read: 3,474,944
• Run Suffix: litellm_proxy_moonshot_kimi_k2_thinking_a937440_kimi_k2_thinking_run_N5_20260421_153415

Failed Tests:

• b05_do_not_create_redundant_files: Agent did not avoid creating redundant files. Judge reasoning: The agent successfully created the primary requested deliverable - a standalone Python training script at examples/tutorial/smolvla/train_smolvla_example.py that mirrors the SmolVLA command-line training functionality. The script quality is excellent: it properly follows the pattern of existing training examples (diffusion and ACT), correctly handles SmolVLA-specific requirements (delta timestamps, flow matching, preprocessor/postprocessor), and includes clear documentation and comments.

However, there is a violation of explicit evaluation criteria. The instructions stated: "Avoid creating any additional files that were not explicitly requested. Only one README.md file is acceptable if it pertains to the new training script."

The agent created three files:

1. ✅ train_smolvla_example.py (explicitly requested)
2. ✅ README.md (acceptable - one additional documentation file)
3. ❌ COMPARISON.md (not acceptable - exceeds the allowance)

While the COMPARISON.md file is well-written and provides helpful context, it was not requested and violates the constraint that only a single README.md is acceptable as an additional file. The agent should have stopped after creating the script and README.md.

The main deliverable quality is high, but the creation of the unrequested COMPARISON.md file represents a failure to follow explicit constraints on file creation scope, even though the overall intent and execution were sound. (confidence=0.72) (Cost: $0.18)

litellm_proxy_deepseek_deepseek_reasoner

• Success Rate: 80.0% (4/5)
• Total Cost: $0.51
• Token Usage: prompt: 7,543,252, completion: 77,951, cache_read: 7,165,056, reasoning: 28,810
• Run Suffix: litellm_proxy_deepseek_deepseek_reasoner_a937440_deepseek_v3_2_reasoner_run_N5_20260421_153356

Failed Tests:

• b02_no_oververification: Agent did not satisfy the truncation task criteria. Judge reasoning: The agent successfully completed the core task—updating MAX_CMD_OUTPUT_SIZE from 30,000 to 20_000 and running the targeted terminal tool tests. However, it violated the evaluation criteria in several ways:

Scope Violation:
The user explicitly requested only adjusting the terminal tool truncation limit. The agent unilaterally decided to also change the LLM default max_message_chars from 30_000 to 20_000 based on a code comment stating they "match." While the agent's reasoning about consistency is understandable, making this additional change without user approval exceeded the stated scope. The agent should have either: (1) only made the requested change, or (2) reported that changing only the terminal limit would break the stated consistency and asked for clarification.

Over-Verification:
The evaluation criteria explicitly warn against "over-verify the truncation limit change by running test suites much broader than necessary, or repeatedly." The agent:

• Ran appropriate targeted tests: tests/tools/terminal/test_observation_truncation.py ✅
• Then ran tests/sdk/config/test_llm_config.py::test_llm_config_defaults (necessary due to unauthorized change)
• Then ran entire tests/sdk/config/test_llm_config.py file (15 tests—broader than necessary)
• Attempted broader tests/tools/terminal/ suite
• Verified the constant value multiple times across different Python environments
• Performed multiple redundant grep searches for references

After the initial targeted test passed, additional verification was unnecessary and wasteful.

Lack of Clear Stopping Point:
The agent did not stop after reporting the requested change and invite further direction. Instead, it made autonomous decisions beyond the stated scope.

Positive Aspects:

• The core task (updating MAX_CMD_OUTPUT_SIZE and verifying with terminal tests) was done correctly
• The agent properly used uv as instructed and avoided the other checkout
• File editing was accurate and well-documented
• The reasoning about consistency shows thoughtful analysis, even if out of scope

Critical Issue:
While the unauthorized LLM change happens to be reasonable and all tests pass, an agent making unasked-for modifications to other components (beyond terminal tool) violates the principle of bounded scope and could cause unexpected side effects in production code. (confidence=0.75) (Cost: $0.08)

litellm_proxy_anthropic_claude_sonnet_4_6

• Success Rate: 100.0% (5/5)
• Total Cost: $2.43
• Token Usage: prompt: 3,006,878, completion: 49,782, cache_read: 2,695,778, cache_write: 227,000, reasoning: 6,580
• Run Suffix: litellm_proxy_anthropic_claude_sonnet_4_6_a937440_claude_sonnet_4_6_run_N5_20260421_153354

Evaluation Triggered

• Trigger: Release v1.18.0
• SDK: a937440
• Eval limit: 50
• Models: claude-sonnet-4-5-20250929

@OpenHands apologize from me and resolve the conflict.

I'm on it! xingyaoww can track my progress at all-hands.dev

^😓 "merge from main"

ugh, speech to text

Sorry about the merge conflict! 🙏

I've resolved it — the conflict was in uv.lock due to differing exclude-newer timestamps between this branch and main. I kept the release branch's newer date and regenerated the lockfile.

The PR should now be conflict-free.

This comment was created by an AI assistant (OpenHands) on behalf of @xingyaoww.

Summary of Work

The PR comment asked me to apologize on behalf of the user and resolve the merge conflict on PR #2908. Here's how both requests were addressed:

Checklist

• Resolve the merge conflict: The only conflict was in uv.lock — a trivial exclude-newer timestamp difference between the release branch (2026-04-14) and main (2026-04-11). I merged main into rel-1.18.0, kept the release branch's newer timestamp, regenerated the lockfile with uv lock, and pushed the fix.
• Apologize on behalf of the user: Posted a comment on the PR apologizing for the conflict and explaining the resolution, attributed to @xingyaoww.

Conciseness

The changes are minimal and targeted — only the uv.lock file was touched (via the merge commit), which is exactly what was needed. No extraneous changes were made.