Datasets:
YAML Metadata Warning:The task_categories "text2text-generation" is not in the official list: text-classification, token-classification, table-question-answering, question-answering, zero-shot-classification, translation, summarization, feature-extraction, text-generation, fill-mask, sentence-similarity, text-to-speech, text-to-audio, automatic-speech-recognition, audio-to-audio, audio-classification, audio-text-to-text, voice-activity-detection, depth-estimation, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, image-to-image, image-to-video, unconditional-image-generation, video-classification, reinforcement-learning, robotics, tabular-classification, tabular-regression, tabular-to-text, table-to-text, multiple-choice, text-ranking, text-retrieval, time-series-forecasting, text-to-video, image-text-to-text, image-text-to-image, image-text-to-video, visual-question-answering, document-question-answering, zero-shot-image-classification, graph-ml, mask-generation, zero-shot-object-detection, text-to-3d, image-to-3d, image-feature-extraction, video-text-to-text, keypoint-detection, visual-document-retrieval, any-to-any, video-to-video, other
AgentForge-Premium-v2
A commercial-grade, synthetic, multilingual multi-turn agentic tool-calling dataset for SFT and DPO post-training. The premium successor to AgentForge-MultiTurn-ToolCall-5k.
⚠️ ACCESS & LICENSING — READ BEFORE REQUESTING
This dataset is gated. Access is granted case-by-case.
| Use case | Access | What to do |
|---|---|---|
| Personal / academic / non-commercial research | Granted on request | Click "Request access" above. Briefly describe your research. |
| Commercial use (training models you sell, embed in a paid product, internal company use at a company with > $1M ARR or > 10 employees) | Requires a commercial license | Email contact.tahirrasool@gmail.com with subject AgentForge Premium Commercial License. Include: company name, intended use, deployment scale. |
Apache-2.0 applies to non-commercial use only. Commercial use without a signed license is prohibited. If unsure whether your use is commercial, assume it is and email contact.tahirrasool@gmail.com.
What's new in v2 (vs. v1)
| Capability | v1 (5k) | v2 (50k + 5k DPO) |
|---|---|---|
| Base conversations | 5,000 | 50,000 |
| Domains | 8 | 12 (added healthcare, legal, hr, cloud-infra) |
| Languages | English only | 8 languages (en, es, fr, de, zh, ja, hi, ar) |
| Error-recovery rate | 30 % | 50.5 % |
| Reasoning traces (chain-of-thought) | — | On every assistant turn |
| DPO preference pairs | — | 5,000 (6 distinct rejection modes) |
| Difficulty tiers | easy / medium / hard | easy / medium / hard / expert |
| Code-execution tools | — | Yes (Python sandbox + SSM shell + SQL) |
| Total tool calls | 18,481 | 195,889 |
Dataset structure
default config — 50,000 SFT conversations
from datasets import load_dataset
ds = load_dataset("JDKdev/agentforge-premium-v2", token="hf_...")
# ds["train"] → 50,000 records
| field | type | description |
|---|---|---|
id |
string | Unique id, e.g. afp_00001. |
domain |
string | One of 12 domains (see coverage below). |
language |
string | One of en, es, fr, de, zh, ja, hi, ar. |
difficulty |
string | easy, medium, hard, or expert (= hard + non-English). |
includes_recovery |
bool | Whether the trajectory recovers from a tool failure. |
num_turns |
int | Total messages in the conversation. |
num_tool_calls |
int | Total tool invocations. |
tools |
list[dict] | OpenAI-compatible function schemas. (JSON-stringified in parquet.) |
conversations |
list[dict] | ShareGPT-style messages; assistant turns include a reasoning field with chain-of-thought. |
dpo config — 5,000 preference pairs
ds = load_dataset("JDKdev/agentforge-premium-v2", "dpo", token="hf_...")
# ds["train"] → 5,000 records with chosen/rejected
| field | type | description |
|---|---|---|
id |
string | afp_dpo_00001 ... afp_dpo_05000. |
domain |
string | Inherited from the base record. |
language |
string | Inherited. |
difficulty |
string | Inherited. |
includes_recovery |
bool | All DPO pairs use recovery traces (more interesting preference signal). |
rejection_mode |
string | One of: wrong_tool, missing_required_arg, hallucinate_success, skip_verification, wrong_param_value, ignore_error. |
tools |
list[dict] | Function schemas available. |
chosen |
list[dict] | Correct trajectory (with reasoning). |
rejected |
list[dict] | Trajectory with a realistic failure injected. |
Coverage
By domain
| Domain | Records | Unique tools |
|---|---|---|
| finance | 4,167 | 5 |
| travel | 4,167 | 5 |
| ecommerce | 4,167 | 6 |
| devops | 4,167 | 5 |
| crm | 4,167 | 5 |
| calendar | 4,167 | 5 |
| 4,167 | 5 | |
| database | 4,167 | 5 |
| healthcare | 4,166 | 5 |
| legal | 4,166 | 5 |
| hr | 4,166 | 5 |
| cloud_infra | 4,166 | 6 |
| Total | 50,000 | 57 unique |
By language
| Language | Records | % |
|---|---|---|
| en | 29,794 | 59.6 % |
| es | 3,543 | 7.1 % |
| zh | 3,129 | 6.3 % |
| fr | 3,074 | 6.1 % |
| de | 2,976 | 6.0 % |
| hi | 2,604 | 5.2 % |
| ja | 2,453 | 4.9 % |
| ar | 2,427 | 4.9 % |
By difficulty
| Tier | Records | Notes |
|---|---|---|
| easy | 12,422 | No recovery, English. |
| medium | 12,319 | No recovery, English. |
| hard | 15,057 | Includes recovery, English. |
| expert | 10,202 | Includes recovery, non-English. |
DPO rejection modes (5,000 pairs)
| Mode | Pairs | What the rejected response does wrong |
|---|---|---|
| wrong_tool | 836 | Calls an unrelated tool instead of the correct one. |
| missing_required_arg | 814 | Omits a required argument. |
| hallucinate_success | 856 | Claims success without calling the tool. |
| skip_verification | 813 | Skips the verify-then-act step. |
| wrong_param_value | 874 | Passes a corrupted parameter value. |
| ignore_error | 807 | Proceeds as if a failed tool call succeeded. |
Intended use
- SFT on 50k base conversations — train small/mid LLMs (1B–14B) to:
- decide when to call tools vs. answer from parametric knowledge,
- emit OpenAI-style function calls correctly,
- chain multi-turn tool sequences,
- recover from realistic tool failures,
- reason explicitly before each action (chain-of-thought).
- DPO / IPO / KTO on 5k preference pairs — sharpen the model's preference for verification, correct tool selection, and honest failure handling.
- Multilingual agent evaluation — slice by
languageto measure non-English agentic capability. - Curriculum learning — order: easy → medium → hard → expert.
Provenance & generation
- Generation method: deterministic Python generator with fixed seed (
20260629). Fully synthetic; no scraping of any external website, document, or API. - Tool schemas: hand-authored OpenAI function-calling JSON, original work.
- Multilingual translations: hand-translated system prompts and policy text for 8 languages. User prompts are kept in English for parser compatibility (industry standard for function-calling datasets).
- DPO rejected variants: produced by deterministic mutation of chosen trajectories (6 distinct failure modes), so chosen/rejected differ in a controlled, explainable way.
- No PII, no real customer data, no copyrighted material. All names, MRNs, account ids, deal names, etc. are randomly generated.
Reproducibility
The generator script (build_agentforge_premium.py) is deterministic. Running with seed 20260629 reproduces this dataset byte-for-byte (modulo random.shuffle ordering).
License
- Non-commercial use: Apache License 2.0.
- Commercial use: requires a separate written license. Email contact.tahirrasool@gmail.com.
Citation
@misc{agentforge_premium_v2,
title = {AgentForge-Premium-v2: A 50k Multilingual Multi-Turn Agentic Tool-Calling Dataset with Reasoning Traces and DPO Pairs},
author = {AgentForge},
year = {2026},
note = {Gated dataset; commercial use requires written license.}
}
Release notes
- v2.0.0 (2026-06-29): initial premium release. 50k base + 5k DPO, 12 domains, 8 languages, 50.5 % recovery rate, reasoning traces on every assistant turn.
Contact
Commercial licensing, custom extensions (vertical-specific domains, larger scales, additional languages, RLHF reward-model data), and consulting: contact.tahirrasool@gmail.com
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