| --- |
| 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. |
|
|
| ## ⚠️ 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 automatically on request | Click **"Request access"** above. Briefly describe your research. | |
| | Commercial use (training models you sell, embed in a paid product, use internally at a company with > $1M ARR or > 10 employees) | **Requires a commercial license** | Email **contact.tahirrasool@gmail.com** with subject line `AgentForge Commercial License`. Include: company name, intended use, deployment scale. | |
|
|
| The Apache-2.0 license applies to **non-commercial use only**. Commercial use |
| without a signed license agreement is prohibited. If you are unsure whether your |
| use is commercial, assume it is and email **contact.tahirrasool@gmail.com**. |
|
|
| Quoted excerpts for benchmarking or academic papers are fine without a license. |
|
|
| ## 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 (non-commercial access; commercial license on request) | 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 |
|
|
| - **Non-commercial use**: Apache License 2.0. |
| - **Commercial use**: requires a separate written license. Email **contact.tahirrasool@gmail.com**. |
|
|
| ## 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 = {Gated dataset; commercial use requires written license.} |
| } |
| ``` |
|
|
| ## Release notes |
|
|
| - **v1.0.0** (2026-06-29): initial release. 5,000 conversations, 8 domains, 30.5 % recovery rate. |
|
|
| ## Contact |
|
|
| Commercial licensing, custom extensions (50k-scale, multilingual, DPO preference pairs, custom domains), and consulting: |
| **contact.tahirrasool@gmail.com** |
|
|
