ChrisDing1105/unified-agent-trajectories

Unified Benchmark Agent Trajectories

Dataset release: v2.0.0 (2026-09-15)
Record format: unified-agent-sft-v1

A growing collection of benchmark agent execution trajectories converted into one transparent, multimodal, tool-aware representation. These are complete recorded benchmark runs—not ordinary chat transcripts—including benchmark tasks, model reasoning and answers, tool calls, tool observations, runtime status, and benchmark scores when available. The directory layout is benchmark-first so additional benchmarks can be added without changing the record format.

The first benchmark collection is derived from WildClawBench. This is an independent trajectory conversion and is not an official benchmark release.

The current release contains 1,870 trajectories from 35 recorded runs (31 full benchmark runs) across 7 model/configuration groups, with 1,718 positional image references.

The release includes external, generally available model families only. Internal SFT, RL, OPD, and experiment-specific checkpoints are excluded. Exact duplicate repair archives are also removed by source-trace SHA-256 rather than published as additional tries.

Collection summary

Model directory	Runs	Full	Other run kinds	Trajectories	Image refs
claude-opus-4.8-thinking	1	1	—	60	204
deepseek-v4-flash-0731	1	1	—	60	189
glm-5.3-flash	13	13	—	780	664
kimi-k2.7-code-highspeed	1	1	—	60	28
qwen3.6-35b-a3b	14	10	cap_retry: 1, repair: 1, validation: 2	610	334
qwen3.8-27b	3	3	—	180	121
qwen3.8-max	2	2	—	120	178

Full denotes a complete benchmark run as captured by the source harness. The smaller validation, repair, and cap_retry runs are intentionally retained as distinct tries; they must not be treated as additional full benchmark repetitions. The canonical model identifier for directory-level analysis is the model directory above. _meta.model_name is a display name, while _meta.source_model_id preserves the raw provider/runtime model identifier and may therefore use a different naming style.

Layout

<benchmark>/<model_name>/<harness_name>/try_<number>/
├── data.jsonl
├── manifest.json
└── images/
    └── <sha256>.<ext>

The original run name, source model identifier, run kind, task ID, score, runtime status, agent harness, captured harness version when available, and provenance are retained under _meta. Harness directory names stay stable (openclaw, codex, claudecode); versions are metadata rather than path components. Try numbers are stable within each model-and-harness directory and ordered by recorded date, then source collection and original run name.

Unified record format

Each line of data.jsonl is one complete recorded trajectory:

{
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "Inspect this input: <image>"},
    {
      "role": "assistant",
      "reasoning_content": "...",
      "content": "",
      "tool_calls": [{
        "id": "call-1",
        "type": "function",
        "function": {"name": "read", "arguments": {"path": "input.png"}}
      }]
    },
    {"role": "tool", "tool_call_id": "call-1", "content": "<image>..."},
    {"role": "assistant", "reasoning_content": "...", "content": "...", "tool_calls": []}
  ],
  "images": [
    "images/<sha256>.png",
    "images/<sha256>.png"
  ],
  "tools": [{"type": "function", "function": {
    "name": "read", "description": "Read a file", "parameters": {...}, "strict": false
  }}],
  "_meta": {...}
}

The representation keeps OpenAI-style assistant tool calls while using positional image references:

• Every assistant message has reasoning_content, content, and tool_calls.
• Tool actions remain in assistant.tool_calls; observations use the tool role and the matching tool_call_id.
• The Nth <image> tag across all messages maps to images[N].
• Images from both user messages and tool results are retained.
• Every try owns its images/ directory. Files are content-addressed and deduplicated by SHA-256 within that try only; no image path points outside its trajectory run.
• Tool declarations use the version-aligned registry for their harness. Claude Code's captured toolNames restrict the registry to tools actually offered to that session; function.strict is always present as a boolean so the complete collection can be loaded into one Arrow table.
• Non-image binary document blocks are represented by a typed marker such as <document:application/pdf> and counted in integrity metadata; they are never mislabeled as images or embedded as base64 text.
• Text-only benchmark trajectories correctly use "images": []. Within each try, image-bearing trajectories are listed first so the Hub Dataset Viewer preview exposes the multimodal records immediately; ordering within each group follows the source order.
• Captured model thinking is retained verbatim in reasoning_content. Missing hidden reasoning is never fabricated; _meta.integrity.reasoning_content_available and assistant_reasoning_message_count make its availability explicit.

Data policy

This is a benchmark execution trajectory archive, not a curated success-only set or a generic conversation corpus. Low scores, timeouts, tool errors, interrupted runs, and conversion-problem cases are retained and labelled in _meta.status, _meta.error_class, _meta.run_kind, and _meta.integrity. Consumers can choose their own filtering policy without losing the original behavioral distribution. _meta.integrity also reports exact tool-call/result counts, dangling calls, orphan results, the terminal message role, and whether the trajectory may be incomplete.

Status values preserve the vocabulary of the source harness:

• recorded: a session completed with a captured trajectory and no explicit error/abort stop.
• partial: a native session contains an explicit error or aborted assistant turn.
• timeout: an official harness reached its benchmark wall-time after producing a real trajectory.
• ok, error, and timeout: terminal statuses captured directly from the roller harness.

These labels describe capture/runtime state, not benchmark success. Use _meta.score and _meta.score_details for task outcome, and _meta.integrity for structural completeness.

Only message-bearing session events are projected into messages; timestamps and execution metadata remain in _meta or the per-run manifest. Embedded credentials, credential-bearing URLs, provider endpoints expressed as IP addresses, private host paths, private/CGNAT addresses, private-key blocks, email addresses, and phone numbers are replaced with explicit redaction markers before publication.

Provenance notes

• When an exact captured system prompt and tool schema are available, their provenance is captured_actf.
• Official OpenClaw and some older native session transcripts did not store the composed system prompt. These records use an empty system message and set _meta.system_prompt_available=false rather than inventing one.
• Tool schemas are selected from a version-aligned registry for each harness. Any called tool absent from that registry receives an explicit permissive fallback, and the _meta.tools_provenance value is suffixed with _plus_inferred.
• Scores are metadata only. They are never used to decide whether a trajectory is included.

Loading

Tabular trajectory loading

from datasets import load_dataset

ds = load_dataset("ChrisDing1105/unified-agent-trajectories", "wildclawbench")

This loads the JSONL trajectory tables. The images field remains a positional list of relative path strings; load_dataset does not fetch the separately stored image files.

Complete multimodal loading

Use snapshot_download when image bytes are required. A selective model/try download keeps the transfer small; remove allow_patterns to download the full release.

import json
from pathlib import Path
from huggingface_hub import snapshot_download

snapshot = Path(snapshot_download(
    repo_id="ChrisDing1105/unified-agent-trajectories",
    repo_type="dataset",
    allow_patterns=[
        "README.md",
        "wildclawbench/claude-opus-4.8-thinking/openclaw/try_001/**",
    ],
))
data_file = snapshot / "wildclawbench/claude-opus-4.8-thinking/openclaw/try_001/data.jsonl"

with data_file.open() as handle:
    record = json.loads(next(handle))
image_files = [data_file.parent / relative_path for relative_path in record["images"]]

Resolve every entry in images relative to the directory containing its data.jsonl file.

Versioning and reproducibility

This release uses format unified-agent-sft-v1. Each record includes its source trace SHA-256, source collection, original run name, task identity, raw source model ID, and conversion provenance. Per-try manifest.json files and wildclawbench/index.jsonl provide inventory and run-level statistics. Published Hub revisions are immutable version anchors; cite the exact Hub commit when an experiment requires byte-for-byte reproducibility.

License

No new blanket license is granted over upstream benchmark prompts, model-generated content, or referenced assets. Their respective upstream terms continue to apply; this is represented as license: other in the dataset card. Users must review the WildClawBench terms and the terms of the relevant model providers before redistribution or commercial use.

Citation

@dataset{chrisding1105_2026_unified_benchmark_agent_trajectories,
  author    = {ChrisDing1105},
  title     = {Unified Benchmark Agent Trajectories},
  year      = {2026},
  publisher = {Hugging Face},
  url       = {https://huggingface.co/datasets/ChrisDing1105/unified-agent-trajectories}
}

Responsible use

Agent trajectories may contain flawed plans, unsafe attempts, tool failures, incomplete responses, or benchmark-specific artifacts. Inspect metadata and apply task-appropriate filters before training or evaluation. Users are responsible for reviewing applicable upstream benchmark, model, and generated-content terms.