Promptops

• 

  promptops flow v1

• 

  thumbnail

Inspiration

While using Bolt, I imagined a future where a computer could work like a human—taking a high-level prompt and autonomously producing results without constant supervision. That vision sparked PromptOps: a local-first agent that mimics human behavior using LLM reasoning and screen interaction.

What it does

PromptOps takes natural language prompts, plans the necessary steps, and simulates human-like actions—typing, scrolling, reading screen content—to execute the task on a desktop autonomously.

How we built it

We used Python for core logic, integrating pyautogui/pynput for UI simulation and Gemini for LLM reasoning. The system includes a planner, a skill execution engine, and a vision layer that parses screen content to guide decisions.

Challenges we ran into

• Reliable UI control without clicking
• Parsing dynamic screen content contextually
• Balancing flexibility with deterministic execution
• Designing prompt interpretation without rigid skill trees

Accomplishments that we're proud of

• A modular LLM-agent pipeline with screen-grounded actions
• Local-first design with no external APIs required
• Real-time execution based on visible UI context
• Planner that adapts actions based on outcomes

What we learned

• LLMs can simulate goal-directed human behavior when grounded in visual input
• Skill-based design is brittle early on; prompt-based planning is more flexible
• Abstracting actions into reusable modules improves maintainability and growth potential

What’s next for PromptOps

• Add support for dynamic skill generation using LLMs
• Integrate full vision-based UI navigation
• Build memory and long-term goal management
• Extend to goal-based software creation from prompts

Built With

• chatgpt
• gemini
• mongodb
• pillow
• pyautogui
• python
• qdrant
• vector