omnigent
Open-source meta-harness to compose, control, and collaborate on AI agents.
A genuinely useful layer for teams juggling multiple agent harnesses. The sandbox, policies, and real-time collaboration are practical, and the alpha is honest about rough edges. Non-technical teams should wait; technical early adopters will find real leverage.
Verified 8d ago · liveness 75/100 · cite: rightaichoice.com/tools/omnigent
- Teams building multi-agent systems
- Developers avoiding agent framework lock-in
- AI engineers needing policy-based guardrails
- Collaborative agent debugging and review
- Non-technical users without coding or YAML experience
- Teams needing a fully managed, no-infrastructure solution
- Use cases requiring strict, deterministic agent behavior
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Skip Omnigent if you are not comfortable with command-line setup and YAML, or if you need a fully managed service with guaranteed stability and support.
You need to self-host the server and manage your own infrastructure, which involves time and potential hosting costs.
Omnigent is completely free and open-source (Apache 2.0), which is a strong fit for indie developers, startups, and enterprises that want to avoid per-seat licensing. Compared to commercial orchestration tools like LangSmith or AgentOps, you save on subscription costs but must invest in self-hosting and maintenance.
In short
omnigent — Open-source meta-harness to compose, control, and collaborate on AI agents. Best for Teams building multi-agent systems, Developers avoiding agent framework lock-in, AI engineers needing policy-based guardrails. Free to use.
What's new in omnigent
Checked 8 days agoAcross the latest 4 updates: 4 feature updates.
Omnigent 0.8.0 released
Adds live model discovery, editor-style web workspace, more agent integrations, and loop guardrails.
Omnigent 0.7.0 released
Introduces first-class Automations and a dedicated /tasks page for scheduled recurring tasks.
Queue follow-up commands while an agent is running
Enables typing follow-ups mid-run that send in order when the agent idles, with edit and reorder control.
Plug any ACP agent into Omnigent
Supports any ACP-compliant agent with session tools, sub-agents, skills, and policies pre-wired.
What people actually say about omnigent — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
39 mentions across 3 sources (Hacker News, YouTube, GitHub) · researched Aug 15, 2026.
- +Unifies Claude Code, Codex, Cursor, Pi, and custom agents in one layer.
- +Enforces stateful policies like spend caps and model routing at meta level.
- +Sandboxes agents with filesystem/network restrictions for security.
- +Real-time collaboration across devices via web, desktop, mobile, and API.
- +Open-source Apache 2.0 with no vendor lock-in.
- −Alpha software — agents break mid-session, rough around edges.
- −Steep learning curve; requires YAML/CLI comfort, not beginner-friendly.
- −Not a managed service; self-hosting adds operational overhead.
- −Huge open issues backlog (963) signals bugs and missing polish.
- −Linux desktop app is recent — Windows/macOS support still maturing.
- • No managed service; you pay for your own infrastructure (VPS, cloud, Docker).
- • Time investment to set up YAML configs and learn the system.
- • Potential model API costs when using Claude, Codex, etc., as with any agent harness.
Viability Score
How well maintained and how widely used is omnigent? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: August 2026
How we score →Key Features
- Unified meta-harness for multiple agent frameworks
- Swap agent harnesses with one-line config changes
- Stateful policies: spend caps, model routing, risk escalation
- Secure OS sandbox with filesystem/network restrictions
- Credential brokering and hiding from agents
- YOLO mode with safety controls
- Built-in multi-agent workflows: Polly (coding) and Debby (debate)
- Custom agent definitions in YAML
- Real-time collaborative sessions via URL with full history
- Cross-device access: terminal, web, desktop, mobile, REST API
- Queueable follow-ups with edit and reorder
- Slack integration with inline approvals
- Native iOS and Android apps
- Desktop apps for Windows, Linux, macOS
- Automations for scheduled recurring tasks
About omnigent
Omnigent is an open-source meta-harness that unifies multiple AI agent frameworks—Claude Code, Codex, Cursor, Pi, and custom agents—under a common sandboxed layer. It lets developers swap or combine harnesses without rewriting code, enforce stateful policies (spend caps, model routing, risk escalation) at the meta level rather than via prompts, and collaborate on live agent sessions in real time from any device. Built for teams building multi-agent systems, Omnigent provides a uniform runner that wraps any agent in a secure OS sandbox (filesystem/network restrictions, credential brokering). A server adds shared history and policy enforcement, exposing sessions via terminal, web, native apps, mobile, and REST API. The tool ships with built-in multi-agent workflows like Polly (coding orchestrator) and Debby (model debate), and allows custom agents defined in YAML. Recent updates include desktop apps for Windows/Linux, Slack integration with inline approvals, queueable follow-ups, and a native iOS app. What sets Omnigent apart is its focus on composition and control without vendor lock-in. You can mix and match leading agent tools and LLMs with one-line configuration changes. It is currently in alpha, developed in the open by the Databricks AI team and Neon, and licensed under Apache 2.0. While powerful for technical teams, it requires comfort with YAML and command-line setup, and is not a managed service.
Behind the Verdict
Omnigent shines as a control plane for teams that already work with multiple agent tools. Its core value is not in any single agent but in the meta-layer: you get a consistent sandbox, policy engine, and collaboration surface across Claude Code, Codex, Cursor, Pi, and your own YAML-defined agents. The Contextual Policies are particularly strong—stateful spend caps and model routing enforced at the harness level, not through fragile prompt engineering. The secure OS sandbox with credential brokering is a practical safety net for running untrusted or experimental agents. Real-time collaborative sessions with full history are a differentiator, letting you review and steer live agent runs from web, desktop, or mobile. The recent additions of Automations (scheduled recurring tasks), a /tasks page, ACP-agent support, and live model discovery indicate active development. However, Omnigent is alpha software, and that shows in rough edges and evolving documentation. It demands CLI and YAML comfort, so it's not for non-technical users. If you're a solo developer or small team experimenting with agents, the setup overhead may not pay off; for teams building multi-agent systems, the composition and control benefits are substantial. It is Apache 2.0 licensed, so you can self-host and extend it. Overall, a promising open-source option for technically adept teams seeking vendor-neutral agent orchestration.
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Real-world workflow fit
Concrete scenarios for the personas omnigent actually fits — and what changes day-one when you adopt it.
You want to standardize agent usage across your team, prevent runaway API costs, and review agent work before merging.
Outcome: You set up Omnigent, define policies with spend caps and model routing, and share sessions with your team for collaborative review. You catch issues early and keep costs predictable.
You need to combine Claude Code for coding and a custom YAML agent for code review, while keeping them sandboxed.
Outcome: You define both agents in Omnigent, use the sandbox to restrict network access, and run them in a shared session. You can swap in Codex later with one line, without rewriting your orchestration.
You want to try different agent harnesses and models without committing to one.
Outcome: You install Omnigent, use one-line config to switch between Cursor and Pi, and test your workflows with live model discovery. You gain flexibility without rework.
Use Cases
- Compose Claude Code and Codex in one session to handle diverse coding tasks
- Enforce per-team spend caps and model routing policies without prompt engineering
- Debug multi-step agent workflows collaboratively with team members in real time
- Sandbox untrusted agents to prevent filesystem and network access during development
- Swap from Cursor to Pi as the primary agent without rewriting your orchestration layer
- Define custom YAML agents for specialized tasks like automated code review or debate
Models Under the Hood
as of 2026-08-17
Limitations
- Omnigent is alpha software and built in the open, which means stability and documentation may be limited.
- It is a meta-harness that requires installation via CLI or package managers and is geared toward developers, so there is some setup overhead.
- The web UI and certain features are still evolving, as indicated by recent releases focusing on workspace improvements and guardrails.
as of 2026-08-15
Verification history
We have re-verified omnigent 5 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published omnigent tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/mo
Ideal for
Individual developers and small teams wanting a free, open-source way to compose and control multiple AI agents without per-seat fees.
What this tier adds
Starting tier: all core features included at no cost, including policies, sandbox, collaboration, and built-in workflows.
Where the pricing makes sense
The company stage and team size where omnigent's pricing actually pencils out — and where peers do it cheaper.
Omnigent is completely free and open-source (Apache 2.0), which is a strong fit for indie developers, startups, and enterprises that want to avoid per-seat licensing. Compared to commercial orchestration tools like LangSmith or AgentOps, you save on subscription costs but must invest in self-hosting and maintenance.
Setup time & first value
How long it actually takes to get something useful out of omnigent — broken out by persona, not the marketing-page minute.
For a developer comfortable with CLI and YAML, you can get Omnigent installed and running within 30-60 minutes. Writing custom policies and YAML agents may take a few hours. Non-technical users should expect a longer learning curve.
Switching to or from omnigent
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From manual agent setup: Install Omnigent and configure your existing agents (Claude Code, Codex) via YAML; you can then manage them centrally.
- ↗To a managed orchestration service: Export your YAML agent configurations and policies, then import them into a commercial platform if you outgrow self-hosting.
Integrations
Resources & Guides
Tutorials & Learning
Official links
Tools that pair well with omnigent
Common stack mates teams adopt alongside omnigent, with the specific reason each pairing earns its keep.
Featured Head-to-Head Comparisons
Omnigent vs Presto Voice
Presto Voice is purpose-built for QSR drive-thru automation with proven ROI, while Omnigent is a developer tool for orchestrating multiple AI agents. If you run a chain of drive-thrus and want to boost order value, choose Presto. If you build multi-agent systems and need a free, open-source control plane, choose Omnigent.
Omnigent vs Truleo
Truleo and omnigent serve completely different domains: Truleo is a specialized law enforcement intelligence platform that connects siloed data (RMS, CAD, jail calls) to generate case leads, while omnigent is an open-source meta-harness for developers to combine and control AI agents like Claude Code and Codex. Choose Truleo if you're a police department needing to reduce report writing time from 40 to 7 minutes and automate jail call analysis; choose omnigent if you're a technical team building multi-agent systems and want policy-based guardrails without vendor lock-in.
Omnigent vs Locus Robotics
Locus Robotics and Omnigent serve entirely different domains: Locus automates physical warehouse tasks with AMRs, while Omnigent enables multi-agent AI orchestration. Choose Locus for high-volume fulfillment centers needing 2-3x productivity gains via robots; choose Omnigent if you're a developer building and controlling multiple AI agents with policy-based guardrails. They are not direct competitors.
Appgyver vs Omnigent
AppGyver is the clear choice if you are already in the SAP ecosystem and need to build compliant extensions, automate workflows, or create AI-powered business apps with governance. Omnigent wins for technical teams that want to orchestrate multiple AI agent frameworks (Claude Code, Codex, etc.) in a secure, policy-controlled environment with real-time collaboration. They solve different problems—choose by your stack.
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