datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
agentic-coding-trajectories-grok46
Agentic Coding Trajectories (Grok 4.6)
Rights & intended use: public research corpus, not training data.
Hosted frontier-model outputs are research-only inputs under project policy
(synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json. License:
Synthetic Factory Research-Only License v1.0 (license: other, see LICENSE) (non-commercial).
Release status: the raw… See the full description on the dataset page: https://huggingface.co/datasets/rmems/agentic-coding-trajectories-grok46.agentic-coding-tracesThis dataset is aggregated from:
"AletheiaResearch/GLM-5.2-Agent",
"armand0e/claude-fable-5-claude-code",
"armand0e/minimax-m3-claude-code-traces",
"AletheiaResearch/Kimi-K3-Codex"
and has been converted into a single unified format:
{"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "<think>...</think>..."}, ...]}
The original raw files are kept in the .raw_sources folder for reference if you'd like to take a closer look.
Main file: USE_THIS_train.jsonl
ty
Agentic-Chain-of-Thought-Coding-SFT-Dataset
🤖 Agentic Coding CoT Dataset
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant Data Structure… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset.agentic-coding-trajectories
Agentic Coding Trajectories
Rights & intended use: legacy public research corpus / portfolio
artifact. Hosted frontier-model outputs are research-only inputs under
project policy (synthetic-factory#161):
intended_use: research_only, project_training_policy: blocked. Not
training data for any model-weight update. Machine-readable record:
rights.json.
Release status: The raw, uncurated coding-episode payload is published under
data/raw/. It is available for inspection and… See the full description on the dataset page: https://huggingface.co/datasets/rmems/agentic-coding-trajectories.guidellm-agentic-coding-trajectories
GuideLLM agentic coding trajectories
A sampled serving-load benchmark derived from Thoughtworks agentic-coding-trajectories, for GuideLLM and an OpenAI-compatible /v1/chat/completions endpoint. There are 630 rows representing 481 unique source sessions, across the same 8turn, 24turn, and 48turn configurations as the earlier version.
The configuration names now refer to original logical steps, not always HTTP request counts. Native tool steps expand into a tool-call request and a… See the full description on the dataset page: https://huggingface.co/datasets/zetomatoz/guidellm-agentic-coding-trajectories.agentic-coding-trajectories
agentic-coding-trajectories
A unified, tokenized corpus of 15,000 multi-turn agentic-coding sessions (618K turns, 41 turns/session avg) drawn from three publicly-released upstream datasets. Built for benchmarking LLM serving systems on realistic multi-turn coding-agent workloads.
Why this exists
Most LLM serving benchmarks use single-shot prompts. Real coding agents work in long multi-turn loops where each turn appends to a growing prompt. This corpus captures that shape… See the full description on the dataset page: https://huggingface.co/datasets/thoughtworks/agentic-coding-trajectories.fable5-agentic-coding-sft
FABLE.5 Agentic Coding SFT (curated)
~159,972 supervised fine-tuning examples for agentic coding — multi-turn conversations where the
assistant drives a tool-call loop (shell, file edits, tests) and commits to complete solutions. Used to train
VibeThinker-Fable-Nano-Agentic-3B.
Provenance & license
Curated/distilled from the Complete-FABLE.5-traces-2M trace set:
Original source: Glint-Research/Complete-FABLE.5-traces-2M (currently gated).
Pulled from:… See the full description on the dataset page: https://huggingface.co/datasets/Nexlab/fable5-agentic-coding-sft.arcagi2-agentic-coding-publicationThis dataset accompanies our blog post Agentic coding improves ARC AGI 2 performance across models.
This contains the complete outputs from the relevant experiment runs; including the full prompts, llm responses and tool calls.
Description of the dataset
We provide data from experiment run using three models, in the folder interleaved_thinking_vs_plain_cot:
gpt_5_2_xhigh
gpt_oss_120b_high
minimax_m2_1
For each model, unless otherwise noted, we provide data for
baseline: for each… See the full description on the dataset page: https://huggingface.co/datasets/arcagi2/arcagi2-agentic-coding-publication.agentic_coding_dataset
Agentic Coding Dataset
This dataset is a compilation of various coding and instruction-following datasets, designed to train agentic coding models.
Sources
This dataset aggregates samples from the following sources:
CodeAlpaca-20k
Instruction-following coding tasks.
Evol-CodeAlpaca-v1
Complex evolved coding instructions (WizardCoder style).
Code Review Instruct
Python code review, critique, and revision examples.
APPS (Automated Programming Progress Standard)… See the full description on the dataset page: https://huggingface.co/datasets/ethanker/agentic_coding_dataset.local-agentic-coding-bench-8gb-vram-2026-05
agentic coding benchmark: local LLMs on 8GB VRAM
can local LLMs do agentic coding (multi-turn tool calling, file creation, debugging) on consumer hardware? this dataset captures real test results.
hardware
GPU: NVIDIA RTX 4060 Ti 8GB
CPU: Intel i7-14700F
RAM: 32 GB DDR5
OS: Windows 11 + WSL2 (Ubuntu)
inference: llama-server (turboquant fork of llama.cpp)
what was tested
two agent frameworks:
Hermes Agent (NousResearch): structured tool calling with… See the full description on the dataset page: https://huggingface.co/datasets/witcheer/local-agentic-coding-bench-8gb-vram-2026-05.Agentic-Chain-of-Thought-Coding-SFT-Dataset
🤖 Agentic Coding CoT Dataset
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant Data… See the full description on the dataset page: https://huggingface.co/datasets/ArkhAngelLifeJiggy/Agentic-Chain-of-Thought-Coding-SFT-Dataset.Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1
🤖 Agentic Coding CoT Dataset v1.1
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 & MiniMax M2.1 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant… See the full description on the dataset page: https://huggingface.co/datasets/AlicanKiraz0/Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1.Agentic-Coding-Tessa
Agentic Coding Dataset for Tessa
A comprehensive dataset for training coding agents with tool-use, reasoning, and software engineering capabilities.
Dataset Composition
This dataset combines multiple high-quality sources:
hermes_reasoning (20.0%): Tool-use and reasoning dataset - interstellarninja/hermes_reasoning_tool_use
search_arena (15.0%): Search and retrieval tasks - lmarena-ai/search-arena-24k
arena_human_pref (15.0%): Human preference data for alignment -… See the full description on the dataset page: https://huggingface.co/datasets/smirki/Agentic-Coding-Tessa.tool-reasoning-sft-CODING-nvidia-Nemotron-Agentic-v1
Nemotron-Agentic-v1 — Cleaned & Rectified
335k multi-turn agentic tool-use trajectories from NVIDIA's Nemotron-Agentic-v1, converted into a strict reasoning + tool-call format with validated FSM transitions.
Origin
Derived from nvidia/Nemotron-Agentic-v1.
Nemotron-Agentic-v1 is a synthetic dataset of multi-turn conversations where language models decompose user goals, decide when to call tools, and reason over tool outputs. Trajectories are generated by simulating user… See the full description on the dataset page: https://huggingface.co/datasets/AmanPriyanshu/tool-reasoning-sft-CODING-nvidia-Nemotron-Agentic-v1.agentic-cot-coding-sft
🤖 Agentic Coding CoT Dataset v1.1
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 & MiniMax M2.1 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️… See the full description on the dataset page: https://huggingface.co/datasets/ArkhAngelLifeJiggy/agentic-cot-coding-sft.agentic-coding-traces
Agentic Coding Mooncake Traces
Synthetic agentic coding benchmark datasets in Mooncake trace (JSONL) format,
generated with AIPerf 0.9.0 for LLM inference benchmarking.
Designed for use with InferenceX via the
agentic-replay scenario-type and aiperf_adapter.py.
Files
File
Sessions
Turns
max_prompt_tokens
Seed
64k/dataset.jsonl
1,000
18,595
65,536
42
128k/dataset.jsonl
1,000
16,957
131,072
42
Format
Each line is a Mooncake trace… See the full description on the dataset page: https://huggingface.co/datasets/thangquang09/agentic-coding-traces.Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1
🤖 Agentic Coding CoT Dataset v1.1
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 & MiniMax M2.1 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant… See the full description on the dataset page: https://huggingface.co/datasets/mepartha/Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1.agentic-cot-coding-sft
🤖 Agentic Coding CoT Dataset v1.1
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 & MiniMax M2.1 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️… See the full description on the dataset page: https://huggingface.co/datasets/ansulev/agentic-cot-coding-sft.agentic_codingmirror-Agentic-Chain-of-Thought-Coding-SFT-Dataset
🤖 Agentic Coding CoT Dataset
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️ Assistant Data… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-Agentic-Chain-of-Thought-Coding-SFT-Dataset.mirror-Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1
🤖 Agentic Coding CoT Dataset v1.1
A high-quality supervised fine-tuning (SFT) dataset for training agentic coding assistants with Chain-of-Thought reasoning capabilities.
📋 Dataset Description
This dataset was created by processing and distilling ~20GB of GitHub crawl data using Minimax-M2 & MiniMax M2.1 to generate structured, reasoning-rich coding examples. Each sample demonstrates systematic problem-solving with explicit tool usage patterns.
🏗️… See the full description on the dataset page: https://huggingface.co/datasets/alucent/mirror-Agentic-Chain-of-Thought-Coding-SFT-Dataset-v1.1.AgenticCoding_v1