Project Title

gorilla — Training and Evaluating LLMs for Function Calls and API Integrations

Overview

Gorilla is a Python-based open-source project designed to train and evaluate Large Language Models (LLMs) for function calls and API integrations. It stands out for its comprehensive approach to testing LLMs in real-world scenarios, including multi-turn and multi-step function calling capabilities. Gorilla provides a platform for developers to assess and compare different LLMs and tools in various tasks, enhancing the interaction between apps and services with human-out-of-loop capabilities.

Key Features

Comprehensive evaluation of LLMs for function calls and API integrations
Multi-turn and multi-step function calling capabilities testing
Agent Arena for comparing different agents in various tasks
GoEx, a runtime for LLM-generated actions with post-facto validation and risk management

Use Cases

Researchers and developers using Gorilla to evaluate and compare LLMs in real-world function calling scenarios
Enterprises leveraging Gorilla to test and improve their LLMs' performance in handling complex workflows and service states
Educational institutions using Gorilla for teaching purposes, demonstrating the practical applications of LLMs in API integrations and function calls

Advantages

Provides a platform for community-driven prompt hub and novel ranking system
Offers a detailed leaderboard for tracking the performance of different LLMs and tools
Enables the assessment of LLM-generated actions with post-facto validation and risk management

Limitations / Considerations

The project's effectiveness is highly dependent on the quality and diversity of the datasets used for training and evaluation
The complexity of real-world scenarios may require continuous updates and improvements to the evaluation system
The integration of LLMs with APIs and function calls may raise security and privacy concerns that need to be addressed

Hugging Face Transformers: A library of pre-trained models for Natural Language Processing, offering a different approach to LLM applications by focusing on model deployment and inference.
OpenAI Gym: A toolkit for developing and comparing reinforcement learning algorithms, which, like Gorilla, provides a standardized environment for evaluating AI models but focuses on reinforcement learning rather than function calls.
AllenNLP: An open-source NLP research library, which, while not specifically focused on LLMs and function calls, offers a framework for building and evaluating NLP models that could be adapted for similar purposes.

Basic Information

GitHub: https://github.com/ShishirPatil/gorilla
Stars: 12,428
License: Unknown
Last Commit: 2025-09-17

📊 Project Information

Project Name: gorilla
GitHub URL: https://github.com/ShishirPatil/gorilla
Programming Language: Python
⭐ Stars: 12,428
🍴 Forks: 1,224
📅 Created: 2023-05-19
🔄 Last Updated: 2025-09-17

🏷️ Project Topics

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