Omnigent
Omnigent is an open-source AI agent framework and meta-harness: orchestrate Claude Code, Codex, Cursor, Pi, and custom agents — swap harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device.
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What is Omnigent?
Omnigent is an open-source meta-harness that provides a common orchestration layer for building, running, and collaborating on AI agents across different harnesses and models. It lets teams compose multi-agent workflows (for example combining Claude Code, Codex, Pi, Cursor, and custom agents) without rewriting agent code, by making harness and model swaps a one-line change and keeping tools, prompts, skills, and policies stable. Omnigent also provides stateful, data-centric policy controls and sandboxed runners so operators can enforce guardrails (cost budgets, access controls, restricted tool use) at the meta-harness layer rather than by prompt engineering. The platform includes a terminal-first CLI, an automatically-starting web UI and an optional native macOS app, plus built-in multi-AI orchestrators and sample agents to help users get started quickly.
Omnigent pricing
Pricing model: Freemium
Omnigent is offered as an open-source project that you can install and run yourself (no site-hosted subscription required); the website and docs present install steps and quickstart flows for the free open-source distribution. There is no consumer-paid plan listed on the site itself; costs come from the user’s chosen model provider API usage, compute, and any self-hosted infrastructure. The project is described as alpha/beta and is intended to be run locally or on your infrastructure; if used via managed platforms or Databricks integrations, separate pricing from those providers would apply.
Omnigent pros
- One-line harness/model swapping across agents
- Unified orchestration for multiple LLM harnesses
- Stateful, data-centric policy enforcement (budgets, access controls)
- Sandboxed runner to isolate agent execution
- Live session sharing with full history via URL
- Real-time collaboration with view/edit permission levels
- Terminal-first UX with streaming output for power users
- Automatically-starting web UI for browser-driven workflows
- Optional native macOS app for desktop experience
- Built-in multi-AI agents (Debby, Polly) for quick demos
- Open-source under Apache-style license
- CLI install and setup wizard to detect credentials
- Fork/clone sessions to reproduce or iterate on work
- Works with both hosted models and your own agent code
- Extensible: define agents in short YAML files
Omnigent cons
- Alpha/beta maturity risk — feature stability not guaranteed
- Requires developer environment (Python 3.12+, Node.js, tmux)
- Needs external LLM API keys to use many harnesses
- Potential infrastructure/cost from running multiple models
- Some features require macOS for native desktop app
- Learning curve for YAML agent definitions and policies
- Limited managed/hosted offering; primarily self-hosted open source
- Enterprise integrations and managed governance not yet fully packaged
Frequently asked questions about Omnigent
What is Omnigent and what problem does it solve?
Omnigent is an open-source meta-harness that sits above existing agent harnesses and LLMs to let you compose, control, and collaborate on multi-agent workflows without rewriting code; it solves friction from running multiple agent windows, switching harnesses, and enforcing guardrails by providing a single orchestration layer and stateful policies.
Which models and harnesses does Omnigent support?
Omnigent provides integrations (harnesses) for Claude Code, Codex, Cursor, Pi, and also supports your custom agents — you can swap harnesses or models with a one-line change while keeping the agent’s tools, prompts, and policies intact.
How do I install and run Omnigent?
Follow the quickstart: install the CLI (the site provides an install script), ensure Python 3.12+, Node.js 22 LTS, and tmux are available, run the install command and use the omni setup wizard to configure credentials, then launch built-in agents such as Debby to confirm the install.
Does Omnigent provide a GUI?
Yes — every session starts a web UI automatically (the server prints a localhost URL) and there is an optional native macOS app that can connect to a local or remote server for a fuller desktop experience.
How does session sharing and collaboration work?
You can share live agent sessions via URL with full session history; share grants either VIEW (read-only stream) or EDIT (co-drive and send messages) permission levels, and sessions can be cloned or forked from the UI or the CLI for reproduction and parallel work.
How are policies and guardrails implemented?
Omnigent applies stateful, data-centric policies at the meta-harness layer (not via prompts), enabling enforcement of cost budgets, access controls, and other guardrails across runs and agents, with sandboxed runners to limit what agent code can access or execute.
Is Omnigent open source and what license does it use?
Omnigent is an open-source project; the website and repository indicate it is published openly (the project is developed in the open), and installation and docs target self-hosted usage under an open-source license.
Do I need API keys or paid model access to use Omnigent?
Yes; while Omnigent itself is open source and free to run, many harnesses require external LLM API keys (for example Claude or OpenAI) and those provider APIs may incur charges based on usage.
Which interfaces are available for interacting with agents?
Omnigent offers a keyboard-first terminal interface (CLI) with streaming output, an automatically-started web UI for browser interaction, and an optional macOS desktop app; the CLI remains the default power-user experience.
Can I run Omnigent in a team or enterprise environment?
Yes — Omnigent is designed for collaboration with shared servers, session sharing, and policy controls; however, the project is primarily self-hosted/open-source today and enterprise-managed offerings or packaged governance from third parties may be required for production-grade deployments.