README.md

---
language:
  - en
  - es
  - fr
  - de
  - zh
  - ja
  - hi
  - ar
license: apache-2.0
task_categories:
  - text-generation
  - text2text-generation
tags:
  - function-calling
  - tool-use
  - agents
  - agentic
  - multi-turn
  - reasoning
  - chain-of-thought
  - fine-tuning
  - sft
  - dpo
  - preference
  - synthetic
  - multilingual
size_categories:
  - 10K<n<100K
pretty_name: AgentForge-Premium-v2
configs:
  - config_name: default
    data_files:
      - split: train
        path: "train.parquet"
    default: true
  - config_name: dpo
    data_files:
      - split: train
        path: "dpo.parquet"
---

# 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

```python
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

```python
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 |
| email | 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

1. **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).
2. **DPO / IPO / KTO** on 5k preference pairs — sharpen the model's preference for verification, correct tool selection, and honest failure handling.
3. **Multilingual agent evaluation** — slice by `language` to measure non-English agentic capability.
4. **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

```bibtex
@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**