automatiq 0.2.0

Record browser sessions and reverse-engineer them into automation scripts.

Your activity, into automation.

AutomatiQ

[!Note] Alpha ⟶ Things will break and change. Read VISION.md to understand why Automatiq exists and where it's headed.

AutomatiQ watches you browse, then an AI agent reverse-engineers your session into a standalone Python automation/extraction script; no manual inspection needed.

How it works

1. Record (Browser Capture) ⟶ Chrome is launched with CDP instrumentation. Every network request, response body, cookie, and user interaction (clicks, typing, navigation) is recorded with timestamps. Press Ctrl+C when you're done.
2. Compile (Vision Analysis) ⟶ The recording is split into per-action video clips. A vision LLM watches each clip and produces structured annotations (what was clicked, what changed, whether the action succeeded). Network requests are decoded, deduplicated, and structured into a workspace dump.
3. Agent (Sandbox Execution) ⟶ An LLM investigator reads the workspace dump, experiments in an isolated Python/IPython environment, and iteratively produces a working script. It can test hypotheses against the live site with guardrails against loops and repetition.

Getting Started

Requirements: Python 3.11+

pip install automatiq

Set your API key (AutomatiQ uses Gemini 3 Flash by default, but any litellm-supported provider works):

# On Linux/macOS
export GEMINI_API_KEY=your-key-here

# On Windows (PowerShell)
$env:GEMINI_API_KEY="your-key-here"

Run the magic command:

automatiq run https://example.com

That's it. Browse the site, press Ctrl+C, and the agent takes over.

Usage Modes

AutomatiQ offers two main ways to operate depending on your workflow:

1. All-in-one execution

The run command records a session and immediately launches the agent to write the script.

automatiq run https://example.com

2. Step-by-step execution

If you want to record multiple sessions, or run the agent later, you can split the process:

automatiq record https://example.com   # Opens the browser and records your session
automatiq agent                        # Builds an automation script from the last recording

Models & Custom Endpoints

AutomatiQ relies on LiteLLM under the hood, meaning you can easily swap the default Gemini models for OpenAI, Anthropic, GitHub Copilot, or Local LLMs (like Ollama, LM Studio, or vLLM).

To change the default models on the fly, use the --model (for the Agent) and --recorder-model (for Vision compilation) flags.

Using Local Models (Ollama, LM Studio, vLLM)

If you are running a local inference server with an OpenAI-compatible endpoint, use the --base-url flag. You must prefix your model name with openai/ so LiteLLM knows to route it through the OpenAI protocol.

Example using Ollama (running locally on port 11434):

automatiq run https://example.com \
  --model openai/llama3.3 \
  --recorder-model openai/llava \
  --base-url http://localhost:11434/v1

Note: For a permanent configuration so you don't have to pass flags every time, see the Configuration section below.

Reference

Keyboard Shortcuts

Phase	Key	Action
Recording	Ctrl+C	Stop recording and save session
Compilation	Esc	Skip AI analysis for remaining segments
Compilation	y / n	Confirm or deny the skip prompt
Agent	q	Quit the agent session
Agent	Esc	Cancel current LLM call or code execution

Note: Ctrl+C force-quits the application at any phase.

CLI Options

Flag	Description
--model MODEL	LiteLLM model string for the agent
--recorder-model MODEL	Vision model for video-clip analysis
--base-url URL	Custom OpenAI-compatible API endpoint
--max-steps N	Maximum agent loop iterations (default: 60)
--sandbox-timeout SEC	Seconds per IPython cell (default: 60)
--output-dir PATH	Root directory for all output (default: ./output)
--no-banner	Skip the startup animation
--verbose	Show detailed diagnostic output
-V, --version	Show version
-h, --help	Show help message

Configuration

On first run, AutomatiQ creates ~/.automatiq/config.toml with commented defaults. Edit this file to permanently override models, custom endpoints, timeouts, and recording settings.

[models]
agent    = "gemini/gemini-3-flash-preview"
recorder = "gemini/gemini-3.1-flash-lite-preview"
# base_url = "http://localhost:11434/v1"   # Uncomment for Ollama / LM Studio / vLLM

[agent]
max_steps       = 60
sandbox_timeout = 60

[recording]
fps                   = 3
segment_pad           = 2
merge_gap_threshold   = 1.5
max_frames_per_prompt = 8

Priority order: CLI flag > ~/.automatiq/config.toml > built-in defaults.

Development

AutomatiQ is managed using uv.

# Clone and setup environment
git clone https://github.com/StoneSteel27/AutomatiQ.git
cd AutomatiQ
uv sync

# Run the project from source
uv run automatiq run https://example.com

Dev Setup

Development dependencies (pytest, ruff, pre-commit, etc.) are installed automatically via uv sync. To set up the git hooks:

uv run pre-commit install

Run tests:

uv run pytest

This ensures ruff, build, twine, pytest, and pre-commit hooks (lint + format on every commit) are properly configured in your isolated environment.

License

MIT