code-yeongyu/oh-my-openagent

What it solves

OmO is an AI agent designed to handle complex, end-to-end professional tasks—such as deep research, building backends and frontends, and large-scale code migrations—without requiring the user to constantly intervene or re-explain requirements.

How it works

OmO operates as a command-line tool that integrates multiple LLMs and specialized "skills." It uses several key mechanisms to improve reliability:

• Ultrawork (ulw): A mode where the agent reads the codebase, plans, proves each step, and verifies results on the real surface before finishing.
• Multi-Agent Graphs: By using keywords like mass ulw, the system transforms a single job into a parallel graph of specialized agents, routing specific tasks to the most appropriate model.
• Kibitzer Memory: A memory system based on a git repository of markdown files. A smaller, cheaper model monitors the main agent's work and provides nudges to prevent repeated mistakes.
• Model Mixing: It supports various frontier models (Claude, ChatGPT, Kimi, GLM) and automatically applies tuned system prompts for each.

Who it’s for

It is built for developers and professionals who need an agent capable of performing "real work" (coding, research, and system architecture) with minimal supervision.

Highlights

• Automated Verification: Proves and checks steps during the ultrawork process.
• Integrated Memory: Uses a git-based markdown system to remember user preferences and past work.
• Parallel Execution: Can orchestrate a graph of agents to handle massive tasks in parallel.
• Skill-Based Extension: Loads only the necessary specialized knowledge/skills required for the specific task.
• Efficient Token Use: Routes easy tasks to cheaper models while reserving expensive models for complex logic.