iauto 0.1.7

iauto is a Python library for intelligent automation

Documentation

iauto

iauto is a Low-Code intelligent automation tool that integrates LLM and RPA.

Key Features

• Low-Code: Define workflows using YAML, for collaboration and version control.
• Automation: Integrated multiple automation frameworks such as Appium and Playwright.
• Extensible: User-friendly extensible interface for customizing any complex Action.
• AI native: Create workflows containing LLM using low-code, as well as more complex automation agents based on ReAct or Multi-Agent.

News

• Integrate autogen, define your automation Multi-Agent using YAML.
• Support for running most open source LLM locally is provided through llama.cpp.

Quick Start

Installation

Python version requirement: >=3.8

iauto can be installed from PyPI using pip. It is recommended to create a new virtual environment before installation to avoid conflicts.

pip install -U iauto

If you want to run LLM locally, you can enable hardware acceleration in the following ways.

To enable cuBLAS acceleration on NVIDIA GPU:

CMAKE_ARGS="-DGGML_CUBLAS=ON" pip install -U iauto

To enable Metal on Apple silicon devices:

CMAKE_ARGS="-DGGML_METAL=ON" pip install -U iauto

Examples

A Playbook is a YAML-formatted file that describes your workflow. In most cases, all you need to do is write a YAML file.

Example 1: Web automation

This example will open the local browser, then visit https://bing.com, execute JavaScript in the browser, and obtain the result.

browser.yaml :

playbook:
  description: Open browser and goto https://bing.com
  actions:
    - browser.open:
        args:
          exec: /Applications/Google Chrome.app/Contents/MacOS/Google Chrome
        result: $browser
    - browser.goto:
        args:
          browser: $browser
          url: https://bing.com
        result: $page
    - repeat:
        actions:
          - browser.eval:
              args:
                page: $page
                javascript: new Date()
              result: $now
          - log: $now
          - time.wait: 2

Run the playbook:

python -m iauto ./browser.yaml

Example 2: Chatbot

This example launches an interactive chatbot in the terminal. You can configure the OPENAI_API_KEY in the environment variable to access an application similar to ChatGPT.

chatbot.yaml :

playbook:
  description: Chat to OpenAI
  actions:
    - llm.session:
        result: $session
    - repeat:
        actions:
          - shell.prompt:
              args: "Human: "
              result: $prompt
          - llm.chat:
              args:
                session: $session
                prompt: $prompt
              result: $message
          - shell.print: "AI: {$message}"

Set your OpenAI API key:

export OPENAI_API_KEY=sk-<YOUR_API_KEY>

Run the playbook:

python -m iauto ./chatbot.yaml

Example 3: Multi-Agent

In this example, two agents work together to solve user tasks collaboratively.

multi-agent.yaml :

playbook:
  actions:
    - llm.session:
        args:
          tools:
            - shell.cmd
        result: $session
    - agents.create:
        args:
          session: $session
          name: GeneralAssistant
        result: $general_assistant
    - agents.create:
        args:
          session: $session
          name: FinacialAnalyst
          description: |
            A financial analyst specializing in analyzing financial markets
            and investment opportunities.
        result: $finacial_analyst
    - agents.executor:
        args:
          session: $session
          agents:
            - $general_assistant
            - $finacial_analyst
        result: $agent_executor
    - repeat:
        actions:
          - shell.prompt:
              args: "Human: "
              result: $prompt
          - agents.run:
              args:
                agent_executor: $agent_executor
                message: $prompt
              result: $message
          - shell.print:
              args:
                message: "AI: {$message}"
                color: green

Run the playbook:

python -m iauto ./multi-agent.yaml

More examples

Playground

iauto offers a web-based playground program for executing workflows. The LLM playground provides a workspace for LLM Chat, ReAct reasoning, and Multi-Agent tasks.

Run LLM playground:

python -m iauto --playground=llm --playbook-dir=./playbooks

LLM playground screenshot:

Contribution

We are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

Development setup

• Code Style: PEP-8
• Docstring Style: Google Style

# Create python venv
python -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt
pip install -r requirements-dev.txt

# Apply autopep8, isort and flake8 as pre commit hooks
pre-commit install

Build

./build.sh

License

MIT

icon license: https://openmoji.org/library/emoji-1F9BE