---
license: cc-by-4.0
pretty_name: A2A-MetaTrace
task_categories:
- tabular-classification
tags:
- traffic-analysis
- metadata-privacy
- agent
- a2a
- multi-agent
- workflow-fingerprinting
size_categories:
- 100K<n<1M
configs:
- config_name: default
  data_files:
  - split: train
    path: data/default/train.parquet
dataset_info:
- config_name: default
  features:
  - name: trace_id
    dtype: int64
  - name: task_class
    dtype: string
  - name: variant
    dtype: string
  - name: client_id
    dtype: string
  - name: n_stages
    dtype: int32
  - name: stage_idx
    dtype: int32
  - name: step_type
    dtype: string
  - name: direction
    dtype: string
  - name: src
    dtype: string
  - name: dst
    dtype: string
  - name: t
    dtype: float64
  - name: length
    dtype: int64
  - name: capability
    dtype: string
  - name: label_visible
    dtype: bool
  - name: mode
    dtype: string
  - name: transport
    dtype: string
  splits:
  - name: train
    num_examples: 101516
---

# A2A-MetaTrace

A labeled, **metadata-only** corpus of multi-agent **A2A** (Agent-to-Agent) workflow
traffic. Each row is one wire message reduced to what a passive network observer sees,
`obs(m) = (src, dst, t, length, direction)`, with workflow ground-truth labels; message
bodies are discarded. The corpus exists to study how much of a *pending agent workflow*
leaks from communication-graph metadata alone, and to evaluate metadata-protecting
transports against it.

**Links:** [GitHub](https://github.com/dangoldbj/a2a-metatrace) | [HuggingFace dataset](https://huggingface.co/datasets/dangoldbj/a2a-metatrace) | [Paper (arXiv)](https://arxiv.org/abs/2606.07150)

**Provenance (disclosed).** Workflows run over the real `a2a-sdk` protocol path
(Agent Cards, discovery, `message/send`, SSE) against real official `a2a-samples` agent
servers backed by real language-model calls. The workflow *compositions* and *labels*
are ours (see `DATASHEET.md`). This is the honest provenance claim: the protocol path and
agent behavior are real; the composition is designed.

## Config

| config | agent backend | transport | workflows | classes | variants | rows (messages) |
|---|---|---|---|---|---|---|
| `default` | agents | https | 270 | 9 | 27 | 101516 |

The corpus is captured from official `a2a-samples` agents composed into multi-agent
workflows; transport is HTTPS-direct (the metadata-protecting transport is evaluated
analytically; see `DATASHEET.md`).

## Usage

```python
from datasets import load_dataset
import pandas as pd

ds = load_dataset("a2a-metatrace", split="train")   # message-level rows
df = ds.to_pandas()

# reconstruct workflows and their labels by grouping on trace_id
by_wf = df.groupby("trace_id")
labels = by_wf["task_class"].first()
```

A workflow is the unit an adversary classifies; featurize per `trace_id` (message counts,
length stats, timing, direction n-grams) and recover `task_class`. Use the `variant`
column for a **leave-variant-out** split (generalization to unseen compositions).

## Regenerating the corpus

The published Parquet is produced by capturing real official `a2a-samples` agents. To
reproduce it end to end:

1. **Get the agents.** Clone the official samples repo and point the harness at its
   Python agents directory:
   ```bash
   git clone https://github.com/a2aproject/a2a-samples.git
   export A2A_SAMPLES_DIR=$(pwd)/a2a-samples/samples/python/agents
   ```
2. **Provide model credentials.** The sample agents call real models:
   - `export OPENAI_API_KEY=...` (the OpenAI- and LiteLLM-backed agents), and
   - for the Google-ADK agents, a Vertex project via Application Default Credentials:
     `export GOOGLE_CLOUD_PROJECT=...` and `gcloud auth application-default login`.

   Keys may instead be placed in a local `.envrc` (`export KEY=VALUE` lines); see
   `corpus/sample_agents.py` for all configuration variables.
3. **Install and run** (Python 3.13):
   ```bash
   uv sync
   uv run python -m corpus.run_corpus --runs-per-class 30   # writes results/corpus/a2a_metatrace.json
   uv run python scripts/export_hf.py                        # writes data/ + this README
   ```

See `DATASHEET.md` for the workflow classes, provenance, and disclosed model substitutions.

## What it is for

- **Workflow-fingerprinting / traffic analysis** on agent interoperation traffic.
- **Evaluating metadata-protecting transports** for agent interoperation.
- A reproducible, provenance-disclosed alternative to purely synthetic agent-traffic models.

See `DATASHEET.md` for construction, intended use, and limitations. Regenerate this card
and the Parquet files with `uv run python scripts/export_hf.py`.

## Citation

```bibtex
@misc{a2ametatrace,
  title  = {A2A-MetaTrace: a metadata-only corpus of multi-agent A2A workflow traffic},
  author = {Dangol, Bijaya},
  year   = {2026}
}
```