Datasets:
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
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.
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:
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
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 — 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 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
modelfor 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
@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}}
}