|
2026 |
AutomationBench |
Shepard, Salimans |
arXiv |
|
2026 |
AutomationBench leaderboard |
Zapier |
|
|
2026 |
MCP-Atlas: A Large-Scale Benchmark for Tool-Use Competency with Real MCP Servers |
Bandi et al. |
arXiv |
|
2026 |
The Tool Decathlon: Benchmarking Language Agents for Diverse, Realistic, and Long-Horizon Task Execution |
Li et al. |
arXiv |
|
2025 |
$τ^2$-Bench: Evaluating Conversational Agents in a Dual-Control Environment |
Barres et al. |
arXiv |
|
2025 |
ACEBench: Who Wins the Match Point in Tool Usage? |
Chen et al. |
arXiv |
|
2025 |
APIGen-MT: Agentic Pipeline for Multi-Turn Data Generation via Simulated Agent-Human Interplay |
Prabhakar et al. |
arXiv |
|
2025 |
Berkeley Function-Calling Leaderboard |
Gorilla |
|
|
2025 |
IntellAgent: A Multi-Agent Framework for Evaluating Conversational AI Systems |
Levi, Kadar |
arXiv |
|
2025 |
MCP-Bench: Benchmarking Tool-Using LLM Agents with Complex Real-World Tasks via MCP Servers |
Wang et al. |
arXiv |
|
2025 |
MCP-Universe: Benchmarking Large Language Models with Real-World Model Context Protocol Servers |
Luo et al. |
arXiv |
|
2025 |
MCPMark: A Benchmark for Stress-Testing Realistic and Comprehensive MCP Use |
Wu et al. |
arXiv |
|
2025 |
StableToolBench: Towards Stable Large-Scale Benchmarking on Tool Learning of Large Language Models |
Guo et al. |
arXiv |
|
2025 |
The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models |
Patil et al. |
Proceedings of the 42nd International Conference on Machine Learning |
|
2025 |
ToolSandbox: A Stateful, Conversational, Interactive Evaluation Benchmark for LLM Tool Use Capabilities |
Lu et al. |
arXiv |
|
2024 |
$τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains |
Yao et al. |
arXiv |
|
2024 |
AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents |
Trivedi et al. |
arXiv |
|
2023 |
API-Bank: A Comprehensive Benchmark for Tool-Augmented LLMs |
Li et al. |
arXiv |
|
2023 |
Gorilla: Large Language Model Connected with Massive APIs |
Patil et al. |
arXiv |
|
2023 |
ToolLLM: Facilitating Large Language Models to Master 16000+ Real-world APIs |
Qin et al. |
arXiv |
|
2023 |
ToolQA: A Dataset for LLM Question Answering with External Tools |
Zhuang et al. |
arXiv |