owl
camel-ai/owl
π¦ OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
π¦ OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
Categories
Tags
Similar tools
ECC
affaan-m/ECC
affaan-m/ECC
hermes-agent
NousResearch/hermes-agent
nousresearch/hermes-agent
AutoGPT
Significant-Gravitas/AutoGPT
AutoGPT
ollama
ollama/ollama
Local inference runtime and CLI for open-weight large language models
transformers
huggingface/transformers
huggingface/transformers
JavaGuide
Snailclimb/JavaGuide
Java guide for backend interviews & AI application development covering system design, LLMs, Agents, and RAG.
Install
pip install owlREADME
π¦ OWL: Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation
[![Documentation][docs-image]][docs-url] [![Discord][discord-image]][discord-url] [![X][x-image]][x-url] [![Reddit][reddit-image]][reddit-url] [![Wechat][wechat-image]][wechat-url] [![Wechat][owl-image]][owl-url] [![Hugging Face][huggingface-image]][huggingface-url] [![Star][star-image]][star-url] [![Package License][package-license-image]][package-license-url]
δΈζι θ―» | Community | Installation | Examples | Paper | Citation | Contributing | CAMEL-AI |
π OWL achieves 69.09 average score on GAIA benchmark and ranks π οΈ #1 among open-source frameworks! π
π¦ OWL is a cutting-edge framework for multi-agent collaboration that pushes the boundaries of task automation, built on top of the CAMEL-AI Framework.
Our vision is to revolutionize how AI agents collaborate to solve real-world tasks. By leveraging dynamic agent interactions, OWL enables more natural, efficient, and robust task automation across diverse domains.
If you find this repo useful, please consider citing our work (citation).
π Table of Contents
- π Table of Contents
- π₯ News
- π¬ Demo Video
- β¨οΈ Core Features
- π οΈ Installation
- Prerequisites
- Install Python
- Installation Options
- Option 1: Using uv (Recommended)
- Option 2: Using venv and pip
- Option 3: Using conda
- Option 4: Using Docker
- Using Pre-built Image (Recommended)
- Building Image Locally
- Using Convenience Scripts
- Setup Environment Variables
- Setting Environment Variables Directly
- Alternative: Using a
.envFile - MCP Desktop Commander Setup
- Prerequisites
- π Quick Start
- Basic Usage
- Running with Different Models
- Model Requirements
- Supported Models
- Example Tasks
- Model Requirements
- π§° Toolkits and Capabilities
- Model Context Protocol (MCP)
- Install Node.js
- Windows
- Linux
- Mac
- Install Playwright MCP Service
- Available Toolkits
- Available Toolkits
- Multimodal Toolkits (Require multimodal model capabilities)
- Text-Based Toolkits
- [Customizing Your Configuration](#customizing-your-conf
- Model Context Protocol (MCP)