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omnigent

by omnigent-ai

Meta-harness for orchestrating, sandboxing, and swapping heterogeneous AI agents

Python
Updated Jul 3, 2026
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Summary

Orchestrates and orchestrates heterogeneous AI agents (Claude Code, Codex, Cursor, Pi, and custom models) behind a unified Orchestration harness. Lets teams swap agent harnesses without rewriting orchestrations, apply sandboxing and policy controls, and collaborate in real time from any device. Includes a meta-harness and governance hooks that make enforcing runtime policies and switching backends straightforward. Also supports Agent Registry Pattern for swapping agent backends without rewrites.

Key Benefits

As multi-agent systems become composed of distinct vendor models and bespoke agents, consistent governance and swap‑ability matter for trust and reproducibility. Omnigent makes it practical to compare agent behavior under identical orchestration, enforce sandboxing, and collect interaction logs that feed Agent-to-Agent Protocol (A2A) evaluation and agent track record metrics. That capability is essential for building reputation-aware systems and continuous agent evaluation pipelines. LLM-as-Judge Pattern provides evaluation approaches that complement these capabilities.

When to Use

Teams building multi-agent pipelines who need to compare models, enforce governance, and iterate without reworking orchestration code.

Applications

  • Compare agent outputs by swapping LLM backends without changing orchestration code
  • Enforce runtime policies and sandboxing for experimental or risky agents
  • Capture interaction logs and metrics to feed A2A evaluation and reputation signals
  • Enable remote collaboration on agent sessions from any device during testing
Works With
openaianthropichuggingfacepython
Topics
agent-frameworkagent-governanceagent-orchestrationagentsaiai-agentai-agentsclaude-codecodexcoding-agents+6 more
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Keywords
multi-agent trustmulti-agent orchestrationagent governanceagent track record