Oh My Agent: A Portable Multi-Agent Harness for Cross-Model Orchestration

Overview

Oh My Agent is a portable multi-agent harness designed for building, running, and testing agent-based skills and workflows. Developed by first-fluke and written in TypeScript, it provides a consistent environment for cross-model orchestration. This tool enables users to swap backends without rewriting logic. It is particularly useful for teams looking to experiment with different AI runtimes, including Claude, Codex/OpenAI, and Cursor.

Benefits

Oh My Agent offers several key advantages for developers and researchers working with AI agents.

First, it supports cross-model orchestration. Engineers can wire agent teams, task delegation, and skill invocation into a unified system. This allows them to compose standards-aware orchestrations and seamlessly switch between different AI backends. For example, a team can move from using Claude to OpenAI without needing to refactor their core logic.

Second, the tool emphasizes reproducibility and testing. It supports reproducible agent runs, which is essential for measuring reliability and trust in multi-agent systems. Key features include debug runs to facilitate easier debugging of agent interactions and replayable traces. Users can replay complex multi-agent interactions to diagnose delegation failures and edge cases. Pluggable skill adapters make cross-model experimentation straightforward by allowing skills to be adapted across different runtimes.

Third, Oh My Agent focuses on multi-agent trust and evaluation. As agents begin to delegate work to other agents, reproducibility and a shared testing harness become critical for measuring reliability. The tool provides interaction traces to capture detailed interaction logs for analyzing agent behaviors. It also allows users to compare agent behaviors across different runtimes to identify inconsistencies. Additionally, it surfaces agent-to-agent failure modes to improve system robustness.

Use Cases

This tool is ideal for engineers and researchers who are building and comparing multi-agent workflows across different runtimes. It is also perfect for those implementing and validating agent-to-agent delegation patterns. Teams looking to prototype standards-aware agent teams with rapid iteration capabilities will find it very useful.

Specific applications include comparative analysis. Users can compare agent behaviors across Claude and OpenAI backends using the same workflow to determine performance differences. Another use case is diagnosing failures. Teams can replay complex multi-agent interactions to identify and resolve delegation failures and edge cases. Rapid prototyping is also a major application. Developers can build standards-aware agent teams with pluggable skill adapters for quick experimentation and development.

Pricing

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Vibes

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Additional Information

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