README.md

---
license: mit
language:
  - en
pretty_name: Agentic Vibecoding Traces
tags:
  - agentic
  - agent-traces
  - tool-use
  - vibecoding
  - sft
  - distillation
  - multi-agent
  - multi-teacher
  - reasoning
  - chain-of-thought
  - anonymized
  - gated
task_categories:
  - text-generation
size_categories:
  - 1K<n<10K
extra_gated_prompt: >-
  This dataset contains anonymized personal agentic coding traces. By requesting
  access you agree to use it for research and model training only, not to attempt
  re-identification of the contributor, and not to redistribute the raw data.
extra_gated_fields:
  Name: text
  Email: text
  Organization or independent: text
  Intended use: text
  I agree to the data-use terms above: checkbox
configs:
  - config_name: premium
    default: true
    data_files:
      - split: train
        path: data/premium/train.parquet
      - split: validation
        path: data/premium/validation.parquet
      - split: test
        path: data/premium/test.parquet
  - config_name: standard
    data_files:
      - split: train
        path: data/standard/train.parquet
      - split: validation
        path: data/standard/validation.parquet
      - split: test
        path: data/standard/test.parquet
  - config_name: unfiltered
    data_files:
      - split: train
        path: data/unfiltered/train.parquet
      - split: validation
        path: data/unfiltered/validation.parquet
      - split: test
        path: data/unfiltered/test.parquet
  - config_name: opencode
    data_files:
      - split: train
        path: data/opencode/train.parquet
      - split: validation
        path: data/opencode/validation.parquet
      - split: test
        path: data/opencode/test.parquet
  - config_name: claude-code
    data_files:
      - split: train
        path: data/claude-code/train.parquet
      - split: validation
        path: data/claude-code/validation.parquet
      - split: test
        path: data/claude-code/test.parquet
  - config_name: grok-cli
    data_files:
      - split: train
        path: data/grok-cli/train.parquet
      - split: validation
        path: data/grok-cli/validation.parquet
      - split: test
        path: data/grok-cli/test.parquet
  - config_name: manicode-freebuff
    data_files:
      - split: train
        path: data/manicode-freebuff/train.parquet
      - split: validation
        path: data/manicode-freebuff/validation.parquet
      - split: test
        path: data/manicode-freebuff/test.parquet
---

# 🧠 Agentic Vibecoding Traces

<div align="center">
  <img src="https://res.cloudinary.com/cmazqjs6/image/upload/racer_is_op_banner_branded_pu7zud.png" alt="RACER IS OP" width="100%">
</div>

<br>

**3.5 years of real agentic coding sessions across 4 CLI agents and 25+ teacher models** — fully anonymized, segmented per-task, with complete tool-call trajectories (bash commands + outputs, file edits) and chain-of-thought reasoning.

> **The culmination dataset:** every "vibe coding" session, extracted from local agent storage, scrubbed, and packaged for SFT.

> [!IMPORTANT]
> **Gated access.** Access requests are reviewed manually. Data is anonymized (projects → `proj-<hash>`, identities/secrets redacted). Do not attempt re-identification or redistribution.

## 📊 Dataset Overview

| Property | Value |
|----------|-------|
| **Premium Segments** | 5,266 *(default config — completed, deduped, scored ≥70)* |
| **Standard** | 5,838 (score ≥50) |
| **Unfiltered** | 5,928 (everything, for RL/negative mining) |
| **Duplicates Removed** | 187 exact task→solution pairs |
| **Source Agents** | 4 (opencode, Claude Code, Grok CLI, Manicode/Freebuff) |
| **Teacher Models** | 25+ (`model` column on every row) |
| **Tool Calls** | ~52,000 (with inputs AND outputs) |
| **Reasoning Text** | ~41M chars of CoT |

## ✨ Quality Tiers

> **Quality > Ease of Access > Quantity**

| Config | Rows | Gate |
|--------|-----:|------|
| `premium` *(default)* | 5,266 | score ≥70 + final answer present + no refusals + no tool-call loops |
| `standard` | 5,838 | score ≥50 |
| `unfiltered` | 5,928 | everything — use for RL / negative samples / analysis |

Scoring rubric (0-100): completed trajectory (+25), real tool work (+10), tool error rate (+15/+7), no repetition loops (+10), sane length (+5), refusal (-40), dead-end (-30).

## 🔌 Source Agents

| Agent | Config | Premium Rows |
|-------|--------|-------------:|
| opencode | `opencode` | ~4,500 |
| Manicode / Freebuff | `manicode-freebuff` | ~1,000 |
| Claude Code | `claude-code` | ~47 |
| Grok CLI | `grok-cli` | ~10 |

Agent configs contain premium-tier rows only.

## 🤖 Teacher Models — filter per model

Every row carries a `model` column. Pick any single teacher:

```python
from datasets import load_dataset

ds = load_dataset("saidutta69/agentic-vibecoding-traces", "premium", split="train")

# Single-model SFT — filter the model column
deepseek = ds.filter(lambda x: x["model"] == "deepseek-v4-flash-free")
oxalpha  = ds.filter(lambda x: x["model"] == "x-preview-f-free")
opus     = ds.filter(lambda x: x["model"] == "claude-opus-4-8")

# Or an agent-specific config (premium tier)
cc = load_dataset("saidutta69/agentic-vibecoding-traces", "claude-code")

# RL / negative mining
raw = load_dataset("saidutta69/agentic-vibecoding-traces", "unfiltered")
```

Top teachers by segment count:

| Model | Segments |
|-------|---------:|
| deepseek-v4-flash-free | ~3,700 |
| manicode/freebuff | ~1,050 |
| mimo-v2.5-free | ~420 |
| minimax-m2.5-free | ~120 |
| hy3-free | ~110 |
| claude-opus-4-8 | ~60 |
| x-preview-f-free (Ox Alpha) | ~47 |
| glm-5.2 | ~22 |
| ... 17 more | ... |

## 🧹 Anonymization Pipeline

Privacy-first. Every row passed through:

| Step | Detail |
|------|--------|
| **Project names** | All directory slugs (+ stem variants) → `proj-<sha1-8>` |
| **Home paths** | `/Users/<user>/...` → `~/` everywhere incl. nested JSON |
| **Identity strings** | Real name, handles, orgs → `[REDACTED]` |
| **Emails** | → `[EMAIL]` (2,130 replaced) |
| **Secrets** | API keys, tokens (`sk-`, `ghp_`, bearer…) → `[REDACTED]` (1,794+) |
| **Hostnames** | → `[HOST]` |
| **Session titles** | Dropped entirely (leak project context) |

Verified zero occurrences of any known project name, handle, or identity string in the final parquets.

## 📂 Data Fields

| Column | Type | Description |
|--------|------|-------------|
| `id` | string | Stable sample id |
| `agent` | string | Source agent (opencode / claude-code / grok-cli / manicode-freebuff) |
| `model` | string | Teacher model — **filter on this** |
| `split` | string | train / validation / test (hash-seeded deterministic) |
| `messages` | string (JSON) | OpenAI-format messages incl. `tool_calls` (args as JSON), `role:"tool"` results, `reasoning_content` CoT |
| `project_id` | string | Anonymized project hash |
| `quality_score` | int | 0-100 rubric score |
| `has_final` | bool | Trajectory concluded with a substantive answer |
| `error_loop` | bool | ≥4 consecutive identical tool calls detected |
| `err_rate` | float | Fraction of tool outputs containing errors |
| `n_tool_calls` | int | Tool calls in segment |
| `n_assistant_turns` | int | Assistant turns |
| `total_chars` | int | Approximate size |

## 🎯 Usage

```python
from datasets import load_dataset
import json

ds = load_dataset("saidutta69/agentic-vibecoding-traces", "all", split="train")

row = ds[0]
msgs = json.loads(row["messages"])
# [{"role":"user","content":"..."},
#  {"role":"assistant","content":"","reasoning_content":"...","tool_calls":[{"id","function":{"name","arguments"}}]},
#  {"role":"tool","content":"...output..."},
#  {"role":"assistant","content":"final answer"}]
```

Works with TRL / Axolotl / LLaMA-Factory tool-calling templates. Segments are self-contained task→solution trajectories ready for assistant-only loss masking.

## 🤝 Contributing Your Sessions

Want to contribute your own agentic coding traces to grow this dataset?

Run the open-source cleaner on your own machine first: [github.com/instax-dutta/vibe-trace-cleaner](https://github.com/instax-dutta/vibe-trace-cleaner) — it extracts sessions from your coding agents (opencode, Claude Code, Grok CLI, Manicode/Freebuff), anonymizes everything locally (projects hashed, identities/secrets redacted), and produces a reviewable output.

Then **email [contact@sdad.pro](mailto:contact@sdad.pro)** with your cleaned file (or questions) and I'll walk you through anything the tool doesn't cover, plus run a second independent privacy check before publishing.

Your sessions go through the same privacy pipeline: project names hashed, identities/secrets/emails redacted, titles dropped — nothing identifying ships.

## ⚠️ Notes & Limitations

- Traces are **real interactive sessions**: some user requests are casual/incomplete; tool outputs may include long file dumps (kept for fidelity).
- Teacher quality varies by model tier (free-tier models dominate counts) — filter by `model` for consistent teacher quality.
- Contains terminal outputs from macOS/Linux environments; environment-specific details are inherent to agentic data.
- MIT license applies to this packaging; underlying tool outputs are machine-generated session artifacts.

## 📜 Citation

```bibtex
@misc{agentic-vibecoding-traces,
  author = {Sai Dutta Abhishek Dash},
  title = {Agentic Vibecoding Traces: 3.5 Years of Multi-Agent Coding Sessions},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/datasets/saidutta69/agentic-vibecoding-traces}}
}
```