---
language:
  - en
license: apache-2.0
task_categories:
  - text-generation
  - conversational
  - text2text-generation
tags:
  - function-calling
  - tool-use
  - agents
  - agentic
  - multi-turn
  - reasoning
  - fine-tuning
  - sft
  - synthetic
size_categories:
  - 1K<n<10K
pretty_name: AgentForge-MultiTurn-ToolCall-5k
configs:
  - config_name: default
    data_files:
      - split: train
        path: "train.parquet"
    default: true
---

# AgentForge-MultiTurn-ToolCall-5k

A **commercial-grade**, **synthetic**, **multi-turn agentic tool-calling** dataset
for supervised fine-tuning (SFT) of LLMs on agent trajectories. 5,000 conversations,
18,481 tool calls, **30.5 % include genuine error-recovery branches** — the
capability most under-represented in existing open datasets.

## Why this dataset exists

Most open tool-calling corpora (xLAM, Gorilla, ToolBench, Hermes-Function-Calling)
are dominated by single-turn, success-only traces. Real agents fail. They get
HTTP 500s, schema mismatches, sold-out inventory, conflicting calendar invites,
expired coupons. A model fine-tuned only on happy-path traces will hallucinate
recoveries instead of executing them.

AgentForge closes that gap:

| Property | AgentForge | Typical open alternatives |
|---|---|---|
| Multi-turn trajectories | 100 % | 20–40 % |
| Error-recovery traces | **30.5 %** | < 5 % |
| Tool schemas included in every example | Yes (OpenAI function-calling format) | Sometimes |
| Reasoning before every tool call | Yes | Inconsistent |
| Domain diversity | 8 domains | 1–3 domains |
| License | Apache-2.0 | Mixed, often restrictive |

## Dataset structure

```python
from datasets import load_dataset
ds = load_dataset("YOUR_USERNAME/agentforge-multiturn-toolcall", token="hf_...")
```

Each of the 5,000 records has the following fields:

| field | type | description |
|---|---|---|
| `id` | string | Unique example id, e.g. `af_00001`. |
| `domain` | string | One of `finance`, `travel`, `ecommerce`, `devops`, `crm`, `calendar`, `email`, `database`. |
| `language` | string | Always `en` in this release. |
| `difficulty` | string | `easy`, `medium`, or `hard`. `hard` ⇔ the trace includes a recovery branch. |
| `includes_recovery` | bool | Whether the trajectory includes a tool failure that the assistant recovers from. |
| `num_turns` | int | Total messages in the conversation (system + user + assistant + tool). |
| `num_tool_calls` | int | Total tool invocations in the conversation. |
| `tools` | list[dict] | OpenAI-compatible function schemas available to the assistant. (Stored as a JSON string in parquet; use `json.loads` to deserialize.) |
| `conversations` | list[dict] | ShareGPT-style messages: `role` ∈ {`system`, `user`, `assistant`, `tool`}; assistant messages may carry a `tool_calls` array. |

### Sample conversation (abridged)

```json
{
  "id": "af_00001",
  "domain": "devops",
  "difficulty": "hard",
  "includes_recovery": true,
  "num_turns": 14,
  "num_tool_calls": 4,
  "tools": [ /* OpenAI function schemas: list_deployments, get_deployment_logs, rollback_deployment, trigger_ci_pipeline, scale_service */ ],
  "conversations": [
    {"role": "system",    "content": "You are an SRE assistant..."},
    {"role": "user",      "content": "Our auth-service in production is throwing 500s..."},
    {"role": "assistant", "content": "Listing recent deployments...",
                          "tool_calls": [{"name": "list_deployments",
                                          "arguments": {"service": "auth-service", "environment": "production"}}]},
    {"role": "tool",      "name": "list_deployments",
                          "content": "{\"deployments\": [{\"deployment_id\": \"DEP-...\", \"status\": \"failed\"}]}"},
    {"role": "assistant", "content": "Pulling error logs.",
                          "tool_calls": [{"name": "get_deployment_logs", "arguments": {...}}]},
    {"role": "tool",      "name": "get_deployment_logs",
                          "content": "{\"logs\": [\"[ERROR] Cannot connect to redis...\", \"[ERROR] OOMKilled\"]}"},
    {"role": "assistant", "content": "Attempting rollback to v2.3.1.",
                          "tool_calls": [{"name": "rollback_deployment", "arguments": {...}}]},
    {"role": "tool",      "name": "rollback_deployment",
                          "content": "{\"status\": \"failed\", \"reason\": \"artifact missing\"}"},
    {"role": "assistant", "content": "Rollback failed — re-triggering CI to rebuild.",
                          "tool_calls": [{"name": "trigger_ci_pipeline", "arguments": {...}}]},
    {"role": "tool",      "name": "trigger_ci_pipeline",
                          "content": "{\"status\": \"triggered\", \"pipeline_id\": \"CI-...\"}"},
    {"role": "assistant", "content": "Summary: failure due to OOM + Redis; rollback failed; CI rebuild running..."}
  ]
}
```

## Coverage

| Domain | Conversations | Tools |
|---|---:|---:|
| finance | 625 | 5 |
| travel | 625 | 5 |
| ecommerce | 625 | 6 |
| devops | 625 | 5 |
| crm | 625 | 5 |
| calendar | 625 | 5 |
| email | 625 | 5 |
| database | 625 | 5 |
| **Total** | **5,000** | **41 unique** |

### Aggregate statistics

- Total tool calls: **18,481**
- Average turns per conversation: **10.4**
- Average tool calls per conversation: **3.7**
- With error-recovery branch: **1,523 (30.5 %)**
- Without (happy path): **3,477 (69.5 %)**
- Difficulty: easy 1,731 · medium 1,746 · hard 1,523

## Intended use

1. **Supervised fine-tuning (SFT)** of small-to-mid LLMs (1B–14B parameters) to learn:
   - when to call a tool vs. answer from parametric knowledge,
   - how to format OpenAI-style tool calls,
   - how to interpret tool responses,
   - **how to recover from tool failures** (the headline differentiator),
   - how to chain multiple tools across turns to reach a goal.
2. **Evaluation** of agentic capability — slice the dataset by `difficulty`, `domain`, or `includes_recovery`.
3. **Curriculum learning** — start with `easy` (no recovery, single domain), progressively mix in `medium` and `hard`.

### Out of scope

- This dataset is **synthetic**. Tool responses are simulated, not real API calls.
- It does **not** contain PII, real customer data, or copyrighted material.
- It is **not** a preference dataset. For DPO/IPO, use it to generate preference pairs via rejection sampling.

## Provenance & generation

- **Generation method**: deterministic Python generator with fixed random seed (`20260629`).
- **Tool schemas**: hand-authored OpenAI function-calling JSON schemas, original work.
- **Conversation content**: synthetic; no scraping of any external website, document, or API.
- **Languages**: English only in this release. Multilingual extensions are planned.

## License

Apache License 2.0

## Citation

```bibtex
@misc{agentforge_multiturn_toolcall_5k,
  title  = {AgentForge-MultiTurn-ToolCall-5k: A Synthetic Multi-Turn Agentic Tool-Calling Dataset with Error Recovery},
  author = {AgentForge},
  year   = {2026},
  note   = {Apache-2.0 licensed multi-turn agentic tool-calling dataset.}
}
```

## Release notes

- **v1.0.0** (2026-06-29): initial release. 5,000 conversations, 8 domains, 30.5 % recovery rate.

## Contact

**contact.tahirrasool@gmail.com**