---
license: mit
language:
- en
pretty_name: DistDebug-Bench Traces
size_categories:
- 1K<n<10K
tags:
- distributed-systems
- debugging
- root-cause-analysis
- agents
- trajectories
- software-engineering
configs:
- config_name: attempts
  data_files:
  - split: train
    path: data/attempts.parquet
  default: true
---

# DistDebug-Bench Traces

Agent traces behind the main results (Table 1) of *DistDebug-Bench: Can LLM
Agents Diagnose Bugs in Distributed Systems?* (NeurIPS 2026, Evaluations and
Datasets Track): 17 agent configurations, 120 bugs each, and the three
attempts per bug that the paper scores, for 6,120 attempts in total.

- Benchmark: https://huggingface.co/datasets/OrderLab/distdebug-bench
- Code, runner, and evaluator: https://github.com/OrderLab/distdebug-bench
- Website and leaderboard: https://distdebug.com

## Contents

| Path | Description |
|---|---|
| `data/attempts.parquet` | One row per attempt: configuration, bug, run, the nine accuracy scores, token use and cost, and the diagnosis. |
| `runs/<config>.tar.gz` | Every attempt's full run directory, including the trace. |
| `configs.json` | Harness, harness version, and model of each configuration. |
| `selection.json` | The run IDs of the three scored attempts per bug and configuration. |

| Configuration | Harness | Model | Archive |
|---|---|---|---:|
| `mini-swe-agent__gpt-6-astra` | Mini-SWE-Agent 2.2.8 | GPT-6 Astra | 14 MB |
| `mini-swe-agent__claude-opus-5.5` | Mini-SWE-Agent 2.2.8 | Claude Opus 5.5 | 10 MB |
| `mini-swe-agent__gpt-5.5` | Mini-SWE-Agent 2.2.8 | GPT-5.5 | 17 MB |
| `mini-swe-agent__gpt-5.4` | Mini-SWE-Agent 2.2.8 | GPT-5.4 | 103 MB |
| `mini-swe-agent__gpt-5.3-codex` | Mini-SWE-Agent 2.2.8 | GPT-5.3-Codex | 18 MB |
| `mini-swe-agent__mimo-v2.5-pro` | Mini-SWE-Agent 2.2.8 | MiMo v2.5 Pro | 32 MB |
| `mini-swe-agent__kimi-k2.6` | Mini-SWE-Agent 2.2.8 | Kimi K2.6 | 30 MB |
| `mini-swe-agent__mimo-v2.5` | Mini-SWE-Agent 2.2.8 | MiMo v2.5 | 34 MB |
| `mini-swe-agent__deepseek-v4-flash` | Mini-SWE-Agent 2.2.8 | DeepSeek V4 Flash | 26 MB |
| `mini-swe-agent__gpt-5.2-codex` | Mini-SWE-Agent 2.2.8 | GPT-5.2-Codex | 13 MB |
| `mini-swe-agent__hy3` | Mini-SWE-Agent 2.2.8 | Hunyuan3 | 20 MB |
| `mini-swe-agent__gemini-3.5-flash` | Mini-SWE-Agent 2.2.8 | Gemini 3.5 Flash | 29 MB |
| `mini-swe-agent__o4-mini` | Mini-SWE-Agent 1.14.4, 2.2.8 | o4-mini | 23 MB |
| `codex-cli__gpt-5.2-codex` | Codex CLI 0.128.0 | GPT-5.2-Codex | 7 MB |
| `codex-cli__o4-mini` | Codex CLI 0.128.0 | o4-mini | 8 MB |
| `opencode__gpt-5.2-codex` | OpenCode 1.4.6 | GPT-5.2-Codex | 12 MB |
| `opencode__o4-mini` | OpenCode 1.4.6 | o4-mini | 6 MB |

### Run directories

Each archive unpacks to `<config>/<bug>/run-NN/`. Run IDs are the original
ones. The paper scores the first three attempts per bug with a schema-valid
diagnosis, so a gap in the numbering means an earlier attempt ended without
one. Every run directory has:

- `diagnosis.json`: the final diagnosis (root-cause blocks and an explanation)
- `diagnosis_scores.json`: the ground truth, the diagnosis, and its scores
- `session_stats.json`: API calls, tokens, cost, and wall time

The trace depends on the harness:

| Harness | Trace files |
|---|---|
| Mini-SWE-Agent | `trajectory.json`: the run configuration and every model message, tool call, and tool output |
| Codex CLI | `codex_exec.log` (Codex's JSON event stream) and `codex_last_message.json` |
| OpenCode | `prompt.txt` and `opencode_cli.log` (OpenCode's JSON event stream) |

### Attempts table

| Column | Description |
|---|---|
| `config`, `harness`, `model` | Configuration (archive name), harness, and model |
| `bug`, `run`, `attempt` | Benchmark instance, run ID, and position (1–3) among the scored attempts |
| `harness_version` | Harness version recorded in the trace |
| `workspace` | `anonymized` or `legacy`. Legacy attempts ran before workspace paths were anonymized, so their workspace path contains the bug ID. |
| `has_trace` | Whether the run directory contains a trace file |
| `num_blocks` | Number of root-cause blocks in the diagnosis |
| `file_coverage` … `mention_coverage` | The nine accuracy metrics, in [0, 1] |
| `api_calls` … `duration_seconds` | `session_stats.json` fields |
| `diagnosis` | The raw `diagnosis.json` text |

```python
from datasets import load_dataset

attempts = load_dataset("OrderLab/distdebug-bench-traces", split="train").to_pandas()
per_bug = attempts.groupby(["config", "bug"])["block_coverage"].mean()
print(per_bug.groupby("config").mean().sort_values(ascending=False))
```

Notes:

- Nine attempts submitted a diagnosis with no blocks. They count as zero on
  every metric, as in the paper.
- `mini-swe-agent__o4-mini` `ray-60991/run-01` has no trace file.
- Codex CLI records one API call and no cost per run.
- Claude Opus 5.5's thinking text is hidden, so `output_reasoning_tokens` is
  0. Thinking tokens are billed as output tokens, so `total_tokens` and `cost`
  are correct.

## Reproducing the paper

The GitHub repository rescores every diagnosis with the public evaluator,
aggregates the scores, and rebuilds Table 1 and Figures 2 and 3. It then
checks the results against the paper's:

```bash
git clone https://github.com/OrderLab/distdebug-bench && cd distdebug-bench
pip install -e ".[paper]"
hf download OrderLab/distdebug-bench-traces --repo-type dataset --local-dir traces
python paper/reproduce.py traces
```

## Redactions

The traces are otherwise unmodified, except for these replacements:

- Local paths, the machine's host name and user name, a private API endpoint,
  an internal project name, and one git author line are replaced with the
  placeholders `/home/user`, `host`, `user`, `<azure-endpoint>`, `harness`, and
  `user <user@example.com>`.
- Encrypted reasoning items returned by the OpenAI Responses API
  (`encrypted_content`) are set to `null`. They are opaque and cannot be
  decrypted.

Rescoring the redacted diagnoses reproduces every stored score.

## License

The diagnoses, scores, and metadata are under the MIT license. Tool outputs
in the traces quote source code, git history, and issue text from the 11
upstream systems, which remain under their own licenses. See the
[benchmark card](https://huggingface.co/datasets/OrderLab/distdebug-bench) for
the list.

## Citation

```bibtex
@inproceedings{jiang2026distdebugbench,
  title     = {{DistDebug-Bench}: Can {LLM} Agents Diagnose Bugs in Distributed Systems?},
  author    = {Jiang, Yuxuan and Liu, Chenglin and Rose, John and Miller, Mason and Thavamani, Ruthesh and Wu, Juntao and Huang, Yinwei and Xu, Jiahao and Huang, Peng},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}
```