How this analysis was created: This summary and feature list are AI-generated from collected project material and can contain mistakes. Stars, license and language are imported from GitHub. Inclusion does not mean that we have tested or audited this project. Check the source documentation for any feature you depend on. Learn more on our About page.
GPT4Tools is an intelligent system that can automatically decide, control, and utilize different visual foundation models, allowing the user to interact with images during a conversation.
The main features of ailab-cvc/gpt4tools are: Agent Tool Learning, Tool Optimization.
Projects with overlapping indexed features include: shizhl/confucius — This project aims to teach large language models (LLMs) to use external tools, enabling them as agents to for… openbmb/toolbench — ToolBench is an open platform for training, serving, and evaluating large language models that retrieve and call… microsoft/jarvis — JARVIS is a system for large language model task orchestration, deployment management, and automation benchmarking. It… chen-gx/toolevo — This repository contains the code and benchmark (ToolQA-D) for our research paper titled "Learning Evolving Tools for… mat-agent/mat-agent — 🚀 VLM-Powered Agent for Intelligent Tool Orchestration | 🔧 Open-source Framework for Multi-modal AI. lifan-yuan/craft — Code for paper "CRAFT: Customizing LLMs by Creating and Retrieving from Specialized Toolsets".
ToolBench is an open platform for training, serving, and evaluating large language models that retrieve and call real-world APIs to complete user instructions. It provides an API-aware inference engine that selects relevant tools from a large corpus and generates sequences of tool calls to produce final answers, along with a custom API registration system that lets users add their own REST endpoints for the model to discover and invoke. The platform includes a complete instruction-tuning pipeline for training models on curated tool-use data, a multi-tool execution engine that coordinates sequ
This project aims to teach large language models (LLMs) to use external tools, enabling them as agents to for practical task planning and tool calling. To achieve this, we propose the "Iterative Tool Learning from Introspection Feedback by Easy-to-Difficult Curriculum". Here is the brief…
JARVIS is a system for large language model task orchestration, deployment management, and automation benchmarking. It utilizes a task orchestrator to decompose complex requests into actionable steps and coordinates various expert models to synthesize final responses. The project includes an AI model deployment manager to handle the local deployment of expert models across different hardware scales. It further provides an AI workflow API consisting of web endpoints used to trigger automated task workflows and retrieve results from model selection stages. The framework incorporates an automat
This repository contains the code and benchmark (ToolQA-D) for our research paper titled "Learning Evolving Tools for Large Language Models", which has been accepted at ICLR 2025.