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.

🧠 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 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

@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}}
}

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