Awesome GitHub RepositoriesTool Optimization

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

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

ICLR 2026 Agentic Reinforced Policy Optimization (ARPO)

The GAIA game is over, and Alita is the final answer.

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.

https://arxiv.org/abs/2506.01056

🔧✨Tool-Star: Empowering Multi-Tool Collaborative Web Agent via Reinforcement Learning

ReTool: Reinforcement Learning for Strategic Tool Use in LLMs ByteDance Seed

This is the official implementation of paper Nemotron-Research-Tool-N1: Exploring Tool-Using Language Models with Reinforced Reasoning, where we present Nemotron-Research-Tool-N1, a family of tool-using reasoning language models. These models are trained with an R1-style reinforcement learning…

``` STE/ ├─ toolmetadata/: tool related metadata ├─ prompts/: full prompts used ├─ savedresults/: prediction results in json ├─ {, FT, ICL}.json: results for baseline model, tool-enhanced w/ fine-tuning, tool-enhanced with ICL ├─ CLround.json: continual learning (each round) ├─ main.py: main…

ICLR 2025 Tool-Planner: Task Planning with Clusters across Multiple Tools

🚀 VLM-Powered Agent for Intelligent Tool Orchestration | 🔧 Open-source Framework for Multi-modal AI

🛠️🤖 From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven Interactions

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…

Inspired by the human ability to adaptively select tools, we introduce AutoTIR, a reinforcement learning framework that enables LLMs to autonomously decide whether and which tool to invoke during the reasoning process, rather than following static tool-use strategies. AutoTIR leverages a hybrid…

Zhi Gao, Yuntao Du, Xintong Zhang, Xiaojian Ma, Wenjuan Han, Song-Chun Zhu, Qing Li

We release here the code for most of the main experiments in paper CREATOR: Tool Creation for Disentangling Abstract and Concrete Reasoning of Large Language Models, including the code of our framework and various baselines. We also release two new datasets: Creation Challenge and Tool Transfer…

Large Language Models (LLMs) have exhibited significant potential in performing diverse tasks, including the ability to call functions or use external tools to enhance their performance. While current research on function calling by LLMs primarily focuses on single-turn interactions, this paper…

Data Release • Web Demo • Tool Eval • Paper • Citation

SPORT introduces an online self-exploration loop that enables multimodal agents to self-improve via AI-generated tasks and LLM-verified preference tuning without human annotations.