autoMate

简体中文 | ENGLISH

An Open Source Development Platform for Agent+RPA.

📚Documentations|🎞️Introduction Video|🗨QQ Channel

✨ Features

• 🌈 Generate automation code by chatting.
• 🔍 Run automation code with one click from quick search.
• 📦 Comprehensive automation toolkit
• ⚙️ Integrated framework and tools for automation development.
• 🥳 Compatible with all online and local LLMs.

🖥 Enviroment

• LLM APIs formatted by OpenAI.
• Refer to the following LiteLLM configuration for detailed information:

LLMs	Completion	Streaming	Async Completion	Async Streaming	Async Embedding	Async Image Generation
openai	✅	✅	✅	✅	✅	✅
azure	✅	✅	✅	✅	✅	✅
aws - sagemaker	✅	✅	✅	✅	✅
aws - bedrock	✅	✅	✅	✅	✅
google - vertex_ai	✅	✅	✅	✅	✅	✅
google - palm	✅	✅	✅	✅
google AI Studio - gemini	✅	✅	✅	✅
mistral ai api	✅	✅	✅	✅	✅
cloudflare AI Workers	✅	✅	✅	✅
cohere	✅	✅	✅	✅	✅
anthropic	✅	✅	✅	✅
huggingface	✅	✅	✅	✅	✅
replicate	✅	✅	✅	✅
together_ai	✅	✅	✅	✅
openrouter	✅	✅	✅	✅
ai21	✅	✅	✅	✅
baseten	✅	✅	✅	✅
vllm	✅	✅	✅	✅
nlp_cloud	✅	✅	✅	✅
aleph alpha	✅	✅	✅	✅
petals	✅	✅	✅	✅
ollama	✅	✅	✅	✅	✅
deepinfra	✅	✅	✅	✅
perplexity-ai	✅	✅	✅	✅
Groq AI	✅	✅	✅	✅
Deepseek	✅	✅	✅	✅
anyscale	✅	✅	✅	✅
IBM - watsonx.ai	✅	✅	✅	✅	✅
voyage ai					✅
xinference [Xorbits Inference]					✅
FriendliAI	✅	✅	✅	✅

🔗 Related Links

• Basic Features

• Project Overview

🍬 Quick Start

Download the latest version from the release and double-click to run directly; no dependencies are required.

⌨️ Local LLM Application Development

This project is divided into two parts: front-end and back-end. The front-end project is in the app directory, and the back-end project is in the server directory. This means that to run autoMate, you need to start both the front-end and back-end simultaneously. The project will create an SQLite database autoMate.db in the home directory. To view the database contents, we recommend using the open-source database software DBeaver.

Initiate the Front-End

1. Install Node.js (version v18.x is required).
2. Navigate to the app directory using the command line.
3. Run npm install to install dependencies.
4. Run npm run dev to initiate the front-end

Initiate the Back-End：

1. Install Python 3, preferably version 3.9+.
2. Navigate to the server directory using the command line.
3. Create and activate virtual env, and run python -m venv .venv.
4. Run pip install -r requirements.txt to install the required dependencies.
5. Run flask --app main run to start the back-end.

Packaging

Back-end packaging command:

pyinstaller main.spec

Front-end packaging command:

npm run build:win

After packaging, place main.exe in the front-end root directory.

🤝 Collaborations

Please refer to Contribution Guidance.

Highly recommended reading HOW TO ASK QUESTIONS THE SMART WAY、HOW TO ASK QUESTIONS TO OPEN SOURCE COMMUNITY 和 HOW TO REPORT BUGS EFFICIENTLY、HOW TO SUBMIT A GOOD ISSUE TO OPEN SOURCE PROJECTS. Better questions are more likely to get help.