OmniMCP

[!IMPORTANT] Labs, not the OpenAdapt product. This repository is a perceive-plan-act computer-use experiment. It is not how OpenAdapt records, compiles, or replays a workflow. The product is openadapt-flow: a compiled program that reports VERIFIED only if an independent check agrees. pip install openadapt.

OmniMCP provides rich UI context and interaction capabilities to AI models through Model Context Protocol (MCP) and microsoft/OmniParser. It focuses on enabling deep understanding of user interfaces through visual analysis, structured planning, and precise interaction execution.

Core Features

• Visual Perception: Understands UI elements using OmniParser.
• LLM Planning: Plans next actions based on goal, history, and visual state.
• Agent Executor: Orchestrates the perceive-plan-act loop (omnimcp/agent_executor.py).
• Action Execution: Controls mouse/keyboard via pynput (omnimcp/input.py).
• CLI Interface: Simple entry point (cli.py) for running tasks.
• Auto-Deployment: Optional OmniParser server deployment to AWS EC2 with auto-shutdown.
• Debugging: Generates timestamped visual logs per step.

Overview

cli.py uses AgentExecutor to run a perceive-plan-act loop. It captures the screen (VisualState), plans using an LLM (core.plan_action_for_ui), and executes actions (InputController).

Demos

• Real Action (Calculator): python cli.py opens Calculator and computes 5*9.

• Synthetic UI (Login): python demo_synthetic.py uses generated images (no real I/O). (Note: Pending refactor to use AgentExecutor).

Prerequisites

• Python >=3.10, <3.13
• uv installed (pip install uv)
• Linux Runtime Requirement: Requires an active graphical session (X11/Wayland) for pynput. May need system libraries (libx11-dev, etc.) - see pynput docs.

(macOS display scaling dependencies are handled automatically during installation).

For AWS Deployment Features

Requires AWS credentials in .env (see .env.example). Warning: Creates AWS resources (EC2, Lambda, etc.) incurring costs. Use python -m omnimcp.omniparser.server stop to clean up.

AWS_ACCESS_KEY_ID=YOUR_ACCESS_KEY
AWS_SECRET_ACCESS_KEY=YOUR_SECRET_KEY
ANTHROPIC_API_KEY=YOUR_ANTHROPIC_KEY
# OMNIPARSER_URL=http://... # Optional: Skip auto-deploy

Installation

git clone [https://github.com/OpenAdaptAI/OmniMCP.git](https://github.com/OpenAdaptAI/OmniMCP.git)
cd OmniMCP
./install.sh # Creates .venv, installs deps incl. test extras
cp .env.example .env
# Edit .env with your keys
# Activate: source .venv/bin/activate (Linux/macOS) or relevant Windows command

Quick Start

Ensure environment is activated and .env is configured.

# Run default goal (Calculator task)
python cli.py

# Run custom goal
python cli.py --goal "Your goal here"

# See options
python cli.py --help

Debug outputs are saved in runs/<timestamp>/.

Note on MCP Server: An experimental MCP server (OmniMCP class in omnimcp/mcp_server.py) exists but is separate from the primary cli.py/AgentExecutor workflow.

Architecture