datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
Nexus-Agents-ToolCalling
Nexus Agents — Tool-Calling Conversations
Synthetic, schema-verified tool-calling conversations for training the Nexus Projects
agents. This is the exact data behind
Nemotron-3-Nano-30B-A3B — Nexus Agents (GGUF),
including the verification transcripts that scored it (27/27 on the behavioral
interview eval, vs 13/27 for the base model).
Links: the fine-tuned model →
Nemotron-3-Nano-30B-A3B — Nexus Agents (GGUF) ·
the generator + seed data + eval harness →
Nexus Training Studio ·… See the full description on the dataset page: https://huggingface.co/datasets/NexusProjectsAI/Nexus-Agents-ToolCalling.glaive_toolcall_enBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_en.
agentic-tool-call-dataset-12k
Agentic Tool Calling Dataset 12K
A curated 12K-sample tool-calling SFT dataset in a TRL-ready chat format. Each sample contains multi-turn agent trajectories with explicit reasoning, structured tool_calls, and tool responses.
Dataset Summary
Property
Value
Total Samples
12,000
Short split
10,000 (agent_short_10k.jsonl)
Long split
2,000 (agent_long_2k.jsonl)
Language
English
Format
OpenAI-style messages with tool_calls
License
Apache 2.0… See the full description on the dataset page: https://huggingface.co/datasets/pyromind/agentic-tool-call-dataset-12k.tool-calling-english-100k
Tool Calling English (100K)
100,000 tool-calling conversations in OpenAI function calling format — the largest general English tool-use dataset for fine-tuning.
Motivation
Models trained without tool-calling examples struggle in agentic deployments. This dataset trains the full cycle: deciding when to call a tool, calling it with correct arguments, interpreting the result, and producing a grounded final response.
Dataset Description
100,000… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/tool-calling-english-100k.to-tool-call-datasets
🛠️ To-Tool-Call Datasets
A unified Qwen3-style tool-call corpus for SFT, GRPO, and agent training
To-Tool-Call Datasets is a curated mirror of public tool-call and function-calling corpora, re-serialized into one training-ready messages JSONL convention.
Quick Start ·
At a Glance ·
Format ·
Sources ·
Training Notes
[!IMPORTANT]
This repository is a format-harmonization layer, not a new claim of ownership over the… See the full description on the dataset page: https://huggingface.co/datasets/zhangdw/to-tool-call-datasets.glaive_toolcall_zhBorrowed from: https://huggingface.co/datasets/glaiveai/glaive-function-calling-v2
Translated by GPT-3.5.
You can use it in LLaMA Factory by specifying dataset: glaive_toolcall_zh.
tool-calling-mix
This is a dataset for fine-tuning a language model to use tools. I combined sources from various other tool calling datasets and added some non-tool calling examples to prevent catastrophic forgetting.
Dataset Overview
Motivation
This dataset was created to address the need for a diverse, high-quality dataset for training language models in tool usage. By combining multiple sources and including non-tool examples, it aims to produce models that can effectively use tools… See the full description on the dataset page: https://huggingface.co/datasets/younissk/tool-calling-mix.pi-toolcall-dataset
Pi edit/write Tool-Call Training Dataset (Qwen3 / Unsloth Studio)
A conversational training dataset that teaches a Qwen3 ~35B base model to
behave as a pi coding agent: plan inside `
reasoning blocks, then issue correcteditandwritetool calls (plus supportingread/bash/grep/find/ls`), handle tool errors, and recover.
The system prompt, tool schemas, and exact tool result/error strings are
transcribed verbatim from the installed pi package
(@earendil-works/pi-coding-agent… See the full description on the dataset page: https://huggingface.co/datasets/marianbusoi/pi-toolcall-dataset.2026-07-31-toolcalling-tulu-20-80-mixture
Tool-calling + TULU3 replay SFT mixture (20/80) for Qwen3.6-27B
The training mixture behind
LASR-Callum/2026-07-31-wrongly-trained-qwen36-toolcalling-tulu-lora-20-80: 1,492,442 Qwen3.6
tokens across 2,002 pre-rendered conversations, split
19.96% agentic tool-use / 80.04% TULU3 replay.
Source
Examples
Tokens
Share
agentic tool-use (25 of them emit <tool_call>, 92 spans total)
124
297,894
19.96%
TULU3 replay
1,878
1,194,548
80.04%
Total
2,002
1,492,442… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-07-31-toolcalling-tulu-20-80-mixture.sft-tool-calling-structured-output-v1
vericava/sft-tool-calling-structured-output-v1
Dataset to train (SFT) 3-20B LLMs for tool calling and structured outputs/classifications.
Includes contents in English as well as some Japanese.
tool-call-efficiency
tool-call-efficiency
Made with the whileai SDK · Collections: Efficiency, Start here: foundational post-training datasets
Teach an agent to make every tool call count.
An agent that calls a tool twice with the same arguments, looks up what
the user just told it, or keeps calling after the task is done is slow,
expensive, and harder to trust. Ask a base Qwen3-4B to work through
1,133 tool-using tasks across six agents and it does this a lot:
only 52% of its 6,681 rollouts finish… See the full description on the dataset page: https://huggingface.co/datasets/while-ai/tool-call-efficiency.toti-cakery-toolcall
Toti Cakery — Tool-Calling Fine-Tuning Dataset (Qwen3, v7)
Synthetic bilingual (Indonesian ~78% / English ~22%) SFT dataset for the Toti
Cakery WhatsApp chatbot: 13 LangChain tools (11 for customers, +2 owner-only
reports) and grounded answers from RAG FAQ context. Rows are built from the
live runtime code (SYSTEM_PROMPT, TOOL_REMINDER, tool schemas via
convert_to_openai_tool, _history_view, pertanyaan_dengan_konteks), so the
training prompt is byte-identical to what the model… See the full description on the dataset page: https://huggingface.co/datasets/LasagnaS/toti-cakery-toolcall.unified-tool-calls
unified-tool-calls
A single consolidated corpus of tool-calling conversations converted from four source datasets into one unified format.
Source datasets
source
repository
raw rows
converted
in final corpus
xlam
dusersad12/xlam-function-calling-60k
100
97
92
toolace
dusersad12/ToolACE
30
30
28
glaive
dusersad12/glaive_toolcall_en
100
97
92
hermes
dusersad12/hermes-tool-calls
18
18
16
Total entries in the merged corpus: 228.… See the full description on the dataset page: https://huggingface.co/datasets/dusersad12/unified-tool-calls.autoscientist-toolcaller-dataset
AutoScientist Tool-Calling Dataset
A curated function-calling / tool-use dataset for the Adaption AutoScientist Challenge. Its
distinguishing feature is a large slice of hard negatives and reliability-focused cases — where the
correct behavior is not a plain tool call.
Adaptive Data quality (real): on the fixed set (c4923b7f…, graded on 1,000 of 2,440 rows
under the free-tier cap) the platform reported 7.0 → 8.1, +15.7%, grade C → B — now confirmed by a
completed, uncapped run… See the full description on the dataset page: https://huggingface.co/datasets/pandeyankit84/autoscientist-toolcaller-dataset.SFT_glaive_toolcall_en
Preparing Your Dataset
Once you’ve decided that fine-tuning is the best approach—after optimizing your prompt as much as possible and identifying remaining model issues—you’ll need to prepare training data. Start by creating a diverse set of example conversations that mirror those the model will handle during production.
Each example should follow this structure below, consisting of a list of messages. Each message must include a role, content, and an optional name. Make sure some… See the full description on the dataset page: https://huggingface.co/datasets/GreenNode/SFT_glaive_toolcall_en.Linux-terminal-tool-calling
Linux Terminal Tool Calling Dataset (Linux-terminal-tool-calling)
This dataset is designed for training and fine-tuning AI agents on tool calling, reasoning, and command execution specifically for standard Linux terminal utilities and system administration tasks. It transforms raw Linux terminal command records into a structured multi-turn conversation format featuring detailed chain-of-thought/reasoning content and OpenAI/OpenClaw-style function calling.
Dataset… See the full description on the dataset page: https://huggingface.co/datasets/iselabvn/Linux-terminal-tool-calling.router-assistant-tool-calling-en-es
Router Assistant Tool Calling EN-ES
Synthetic English and Spanish conversations for supervised fine-tuning of a small,
local router assistant. The assistant answers brief social turns, obtains current
network facts through tools, handles tool failures, and asks for confirmation before
restarting the router or disabling WAN internet access.
Dataset size
Split
Conversations
Assistant completions
Train
11,066
21,242
Validation
984
1,890
Test
926
1,769… See the full description on the dataset page: https://huggingface.co/datasets/Lucasllfs/router-assistant-tool-calling-en-es.turkish-tool-calling
Türkçe Tool-Calling Veri Seti
56.247 kayıt. xLAM/APIGen 60k ve NVIDIA When2Call'dan türetilmiş,
üç davranış sınıfı içeren Türkçe function-calling veri seti.
from datasets import load_dataset
ds = load_dataset("bilalabic/turkish-tool-calling") # mesaj listesi
ds = load_dataset("bilalabic/turkish-tool-calling", "table") # düz tablo
ds = load_dataset("bilalabic/turkish-tool-calling", "sharegpt") # ShareGPT
İçerik
Kayıt
56.247… See the full description on the dataset page: https://huggingface.co/datasets/bilalabic/turkish-tool-calling.toolcall_bench
When2Call
💾 Github | 📄 Paper
Dataset Description:
When2Call is a benchmark designed to evaluate tool-calling decision-making for large language models (LLMs), including when to generate a tool call, when to ask follow-up questions, when to admit the question can't be answered with the tools provided, and what to do if the question seems to require tool use but a tool call can't be made.
We find that state-of-the-art tool-calling LMs show significant room for… See the full description on the dataset page: https://huggingface.co/datasets/Compumacy/toolcall_bench.tool-calling-browser-agent-tasks
Dataset Card
Created by: DataCreator AI
Overview
Tool Calling for Agentic Tasks with Multi-Step Workflows contains 1,062 synthetic multi-turn conversations between a user and an AI assistant. The examples primarily focus on practical agentic tasks such as train ticket booking, dynamic form filling, and payment processing. It provides diverse scenarios including successful execution, context retrieval, tool integration, and failure recovery.
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/DataCreatorAI/tool-calling-browser-agent-tasks.scugnizz-toolcalling-synthetic-v3
Scugnizz Tool Calling Synthetic
Dataset sintetico per TOOL_CALL / TOOL_RESULT.
Categorie:
{
"negative_tool_not_available": 18,
"tool_result_mail": 2530,
"positive_hash": 6,
"similar_tools": 90,
"tool_result_finance": 164457,
"positive_ip": 15,
"tool_result_weather": 134612,
"positive_dns": 60,
"positive_multitool": 432,
"tool_result_calendar": 448,
"positive_weather": 72,
"negative_no_tool_needed": 3,
"negative_missing_required_arg": 3… See the full description on the dataset page: https://huggingface.co/datasets/ProjectScugnizz/scugnizz-toolcalling-synthetic-v3.presentation_tool_calling_phase_1
Presentation Tool-Calling Dataset (Phase-1)
Supervised examples for single-step tool routing and argument filling in a slide-deck builder.
What Phase-1 trains
Choose the correct tool (function) for a user instruction.
Fill tool arguments as strict JSON.
Optionally use injected deck-state context to pick correct slide numbers/titles.
Files
train.jsonl, validation.jsonl, test.jsonl: one JSON object per line
tools.json: tool schema (tool names + arg schemas)… See the full description on the dataset page: https://huggingface.co/datasets/raketa314/presentation_tool_calling_phase_1.jupyter-interleaved-tool-call
Aurora-M manual pipeline
This repo is a small, deterministic data pipeline for the Aurora-M notebook corpus. It reads JSONL
records, converts markdown fenced code blocks into explicit tool-call markers, and then scores how
parseable those tool-call blocks are.
Core constraints (final behavior):
No Docker, no uv, and no execution of extracted code. Scoring uses ast.parse only.
Work is streaming/row-based and resilient: malformed rows/snippets should not kill the full run.
testrun… See the full description on the dataset page: https://huggingface.co/datasets/AutomatedScientist/jupyter-interleaved-tool-call.loap-reasoning-toolcalling-20k
loap-reasoning-toolcalling-20k
loap-reasoning-toolcalling-20k is a synthetic dataset designed to train language models in reasoning (Chain of Thought) and tool usage.
Language: English
Format: Chat (System, User, Model, Tool)
Dataset Structure
[
{
"id": "synthetic_agent_00001",
"conversations": [
{
"role": "system",
"content": "You are a helpful AI agent.\nYou have access to the following tools:"
},
{
"role": "tools"… See the full description on the dataset page: https://huggingface.co/datasets/igidn/loap-reasoning-toolcalling-20k.kubectl-mcp-server-tool-call-reasoning-6k
kubectl-mcp-server-tool-call-reasoning-6k
MCP tool-calling SFT 資料集，由 Agent Tools Fine-Tuning Platform 以「反向生成 + teacher solver 驗證」流程產生。
語言：繁體中文
工具（來自 MCP server）：install_helm_chart, upgrade_helm_chart, uninstall_helm_chart, helm_list, helm_status, helm_history, helm_get_values, helm_get_manifest, helm_get_notes, helm_get_hooks, helm_get_all, helm_show_chart, helm_show_values, helm_show_readme, helm_show_crds, helm_show_all, helm_search_repo, helm_search_hub, helm_repo_list… See the full description on the dataset page: https://huggingface.co/datasets/Simon-Liu/kubectl-mcp-server-tool-call-reasoning-6k.Kali-tool-calling
Kali Linux Tool Calling Dataset (Kali-tool-calling)
This dataset is designed for training and fine-tuning AI agents on tool calling, reasoning, and command execution specifically for Kali Linux tools. It transforms the original KALI_LINUX_TOOLKIT_DATASET into a structured multi-turn conversation format featuring detailed chain-of-thought/reasoning content and OpenAI/OpenClaw-style function calling.
Dataset Details
Total Records: 790
Language: English
Format:… See the full description on the dataset page: https://huggingface.co/datasets/iselabvn/Kali-tool-calling.qwen_tool-calling_finetune_dataset
🛠️ Tool-Calling Instruction Dataset
This dataset consists of instruction-completion pairs for training Large Language Models (LLMs) to convert natural language requests into structured tool/function calls.The data format is inspired by ChatML and includes explicit system, user, and assistant roles.
Dataset Structure
Each example is a single JSON object with a text field, containing a chat-formatted prompt and response.
The user gives an instruction (e.g., "Analyze… See the full description on the dataset page: https://huggingface.co/datasets/emrecandan0/qwen_tool-calling_finetune_dataset.eu-multilang-tool-calling-180k
eu-multilang-tool-calling-180k
175,716 multi-turn function-calling conversations in 6 under-served EU languages: Hungarian, Bulgarian, Greek, Croatian, Slovak, Slovenian. Apache 2.0 — commercial use permitted.
~29-30K examples per language across 7 domains: fiscal, banking, e-commerce, calendar, weather, generic government services.
Quick Load (SFT / Instruction Tuning)
from datasets import load_dataset
# Full dataset
ds =… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/eu-multilang-tool-calling-180k.tool-calling-traces-ptbr
Tool calling conversations in Portuguese
484 synthetic conversations that teach a model when to call a tool, which one to call and
with which arguments, and also when to answer directly, with no tool at all.
Each line of the file is a complete conversation: the user's question, the tool call, the
simulated return of that tool, and the final answer.
It was built because no dataset of tool calling in Portuguese with fictional tools existed.
The 30 tools and the user questions were… See the full description on the dataset page: https://huggingface.co/datasets/annajuliaasf/tool-calling-traces-ptbr.