Datasets:

JDKdev
/

agentforge-multiturn-toolcall

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

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)

{
  "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

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.
Evaluation of agentic capability — slice the dataset by difficulty, domain, or includes_recovery.
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

@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