The Berkeley Function Calling Leaderboard (BFCL): From Tool Use to Agentic Evaluation of Large Language Models

Abstract

Function calling, also called tool use, refers to an LLM’s ability to invoke external functions, APIs, or user-defined tools in response to user queries—an essential capability for agentic LLM applications. Despite its prominence, there did not exist a standard benchmark to evaluate function calling abilities, due to two reasons – the challenging nature of evaluating when a function call is valid, and the challenge of acquiring diverse, real-world functions. We present the Berkeley Function Calling Leaderboard (BFCL), a comprehensive benchmark designed to evaluate function calling capabilities in a wide range of real-world settings. The BFCL benchmark evaluates serial and parallel function calls, across various programming languages using a novel Abstract Syntax Tree (AST) evaluation method that can easily scale to thousands of functions. We construct the benchmark using a combination of expert curated, and user-contributed functions and associated prompts. Finally, BFCL benchmark evaluates the ability of models to abstain and reason in stateful multi-step agentic setting. Evaluating a wide range of models, we observe that while state-of-the-art LLMs excel at singleturn calls, memory, dynamic decision-making, and long-horizon reasoning remain open challenges. Since its preview, BFCL has become the defacto standard for evaluating function-calls, and can be accessed at gorilla.cs.berkeley.edu/leaderboard.html.