--- 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 $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**