APIGen-MT-5k

Summary APIGen-MT is an automated agentic data generation pipeline designed to synthesize *verifiable, high-quality, realistic datasets* for agentic applications This dataset was released as part of APIGen-MT: Agentic PIpeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay Code: https://github.com/apigen-mt/apigen-mt.github.io The repo contains **5000** multi-turn trajectories collected by APIGen-MT This dataset is a subset of the data used to train the xLAM-2 model series Overview Agentic data consists of realistic trajectories in which an AI agent gradually comprehends the human's intent and interacts with tools and the environment step-by-step to complete the task. APIGen-MT builds on APIGen, which focuses on generating single-turn function calling data. It addresses the lack of high-quality multi-turn agent interaction data in public datasets and the high cost of manually collecting such data for domain-specific applications. Each task in our dataset is verified through three hierarchical stages: format checking, function executions and domain policy check, and semantic verification, ensuring its reliability and correctness. We conducted a human evaluation over 200 sampled trajectories, and the success rate is 99%. The overall framework for the dataset collection procedure is shown below. See more details at our project website. Dataset Details **Models Used**: GPT-4o, DeepSeek-V3 **Domains**: Retail and Airline (via τ-bench) **Size**: 5000 multi-turn dialogues **Format**: ShareGPT-like JSON, with structured conversation turns The dataset is at `apigen-mt_5k.json`. After accepting the usage terms and login in your HuggingFace account, you can simply access the dataset using The data is released in *ShareGPT* format shown below Benchmark Results Berkeley Function-Calling Leaderboard (BFCL v3) Performance comparison of different models on BFCL leaderboard. The rank is based on the overall accuracy, which is a weighted average of different evalu