omnigent
Omnigent is an open-source meta-harness for composing, controlling and collaborating on AI coding agents.
Omnigent solves a real problem: teams accumulating three or four coding agents with no shared policy or audit surface. The sandbox plus stateful allow/ask/deny policies are the reason to adopt, not the framework-swapping pitch. Progressive budgets and per-model usage tracking address the surprise-LLM-bill problem that hits anyone running multiple harnesses. It is alpha software that assumes you can read a YAML file and run Kubernetes, so treat it as infrastructure you operate. If you only run one agent, Claude Code or Codex alone is simpler.
Verified 7d ago · liveness 75/100 · cite: rightaichoice.com/tools/omnigent
- Platform teams running multiple coding agents that need one policy and audit layer
- AI engineers who want spend caps and model routing enforced outside prompts
- DevOps teams self-hosting agent infrastructure on Kubernetes with sandboxed runners
- Teams that need shared, reviewable agent session history across reviewers and devices
- Non-technical users who need a point-and-click agent product
- Teams wanting a fully managed service with no infrastructure to operate
- Workflows that require deterministic, fully predictable agent behavior
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Skip Omnigent if your team runs a single coding agent and already has CI sandboxing and cost guardrails, since the setup and Kubernetes overhead buys you nothing you do not already have.
Running multiple harnesses in a session multiplies token spend per run, which is why Omnigent ships progressive budgets and per-model usage tracking rather than a flat allowance
Published details describe Omnigent as free and open source under Apache 2.0, with no paid tier listed. That makes it cheapest-in-class against managed agent platforms, at the cost of operating the infrastructure yourself — budget Kubernetes, sandbox backend and engineering time instead of a subscription.
In short
omnigent — Omnigent is an open-source meta-harness for composing, controlling and collaborating on AI coding agents. Best for Platform teams running multiple coding agents that need one policy and audit layer, AI engineers who want spend caps and model routing enforced outside prompts, DevOps teams self-hosting agent infrastructure on Kubernetes with sandboxed runners. Free to use.
What's new in omnigent
Checked 7 days agoAcross the latest 5 updates: 1 changelog entry and 4 news mentions.
Omnigent v0.16.0: redesigned desktop onboarding, unified workspace browser, sandbox edits
Adds redesigned desktop onboarding, a unified workspace browser with file-change badges, copy-on-write sandbox edits, shallow and blobless git clones, and admin defaults for sharing and Slack setup — plus breaking changes to preference limits and MCP configuration permissions.
Moving agent orchestration from code to configuration
Lets teams define agent orchestration in YAML while Omnigent handles sessions, messaging, isolation and policy enforcement underneath.
Stop Surprise LLM Bills With Production-Ready Cost Controls
Details LLM cost controls including smart routing, progressive budgets, custom pricing for self-hosted models and per-model usage tracking.
Built-in structured web research and extraction for agents
Enable nimble_research and nimble_extract by name in an agent spec to run cited web research and pull structured data from live pages without glue code.
Allow, ask, deny: contextual policies in Omnigent
Introduces contextual policies with allow, ask and deny modes for agent actions, enforced at the meta-harness layer rather than through prompts.
What people actually say about omnigent — is it worth it?
We scanned public community sources for omnigent on Sep 15, 2026 and could not establish that the discussion we found is about this tool rather than something else sharing its name. Only 2 of the posts we fetched could be positively tied to omnigent. Rather than publish a sentiment score built on the wrong subject, we publish nothing here and re-run the scan.
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: October 2026
How we score →Key Features
- Unified meta-harness across Claude Code, Codex, Pi, Devin and custom agents
- Swap or combine agent harnesses with one-line configuration changes
- Stateful contextual policies: allow, ask, deny
- Progressive budgets and spend caps per model and team
- Model routing across Claude, OpenAI and Gemini models
- Risk-based escalation to human approval
- Secure OS sandbox restricting filesystem and network access
- Credential brokering that hides secrets from the agent
- Copy-on-write sandbox edits that leave underlying files unchanged
- Run YOLO mode safely inside the sandbox
- Built-in multi-agent workflows: Polly and Debby
- Custom agents defined in YAML
- Real-time collaborative sessions via URL with full history
- Queue, edit, reorder and delete follow-ups mid-run
- Organize sessions into projects with optional default pre-fills
About omnigent
Omnigent is an open-source meta-harness for AI agents: a common layer that sits above Claude Code, Codex, Pi, Devin and agents you write yourself. You swap or combine harnesses with one-line configuration changes and keep the same sandboxed session model underneath. Engineering teams running several coding agents in production use it to get one policy, sandbox and audit surface instead of rebuilding their integration every time a new agent ships. The architecture splits in two. A runner wraps any agent in a secure OS sandbox with filesystem and network restrictions, hiding credentials from the agent and brokering access instead. A server adds shared history and policy enforcement, then exposes every session over the terminal, web, native desktop app (macOS) and mobile apps (iOS, Android), plus a REST API. Built-in workflows cover two common patterns: Polly, a coding orchestrator, and Debby, a model debate. Custom agents are defined in YAML. Control is what separates it from a plain agent wrapper. Contextual policies are stateful and data-centric: they track what an agent has already done and then allow, ask or deny the next action, which is how spend caps, model routing and risk-based escalation get enforced at the meta-harness layer rather than through prompt instructions. Recent releases added progressive budgets, custom pricing for self-hosted models, per-model usage tracking, multi-provider sandboxing with a Blaxel backend, self-restarting Kubernetes runners, Devin as a built-in harness, and copy-on-write sandbox edits. Sessions can be grouped into projects with optional pre-fills, and follow-up messages can be queued, edited, reordered or deleted while the agent is still running. Collaboration is built in: live session URLs carry full history so teammates review, comment and steer the same run from any device. The catch is maturity. Omnigent is alpha, built in the open by the Databricks AI team, Neon and contributors under Apache 2.0, and it assumes comfort with YAML and infrastructure.
Behind the Verdict
The pitch is composition, but the value is governance. Omnigent's one-line harness switching — moving from Claude Code to Codex to Pi without touching your orchestration layer — is pleasant, yet swapping models is something a competent engineer can do with an adapter script. What you cannot easily write yourself is the policy engine: stateful, data-centric rules that watch what an agent has already done and then allow, ask or deny the next action. That is how you enforce spend caps, route cheap models to cheap tasks, escalate risky operations to a human, and keep an audit trail — all outside the prompt, where the agent cannot talk its way around it. The runner is the second real asset. It wraps any agent in an OS-level sandbox with filesystem and network restrictions, hides credentials from the agent and brokers access instead, and lets Linux agents make disposable edits with copy_on_write: true so changes sit in a throwaway layer while the real files stay untouched. Admins can opt into shallow or blobless clones to keep large repos manageable. YOLO mode exists, but it runs inside that sandbox rather than naked on your machine. Collaboration is genuinely uncommon at this layer of the stack. A live session URL carries full history, so a reviewer on mobile can comment on and steer the same run an engineer started in the terminal. Projects group sessions with default pre-fills. Follow-ups queue above the composer and can be steered, edited, reordered or deleted mid-turn. The built-ins are a starting point, not a product. Polly orchestrates coding; Debby runs a model debate; you write your own in YAML. Recent additions also let you point Omnigent at any Agent Client Protocol agent, and enable nimble_research and nimble_extract by name for cited web research and structured extraction without glue code. Where it does not fit: solo developers with one harness and CI sandboxing already configured, teams that want a point-and-click product, anyone who needs deterministic agent behavior, and organizations unwilling to run alpha software. Running it at scale assumes Kubernetes familiarity and operational attention — 0.16.0 shipped breaking changes to saved preferences and to who can edit MCP configuration, which is the shape of alpha life.
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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 install Omnigent over the CLI, wrap Claude Code and Codex in sandboxed runners, and write a contextual policy that caps monthly spend and routes trivial refactors to a cheaper model.
Outcome: One policy layer governs every harness, and per-model usage tracking on the Usage page shows where the budget went.
A teammate starts a session in the terminal; you open the live session URL on your phone, read the full history and comment on a step mid-run.
Outcome: You steer the same run without taking over the machine or asking for a screen share.
You point Omnigent at an Agent Client Protocol agent and set copy_on_write: true for its sandbox write paths while it experiments on your repo.
Outcome: The new agent runs with session tools and policies already wired in, and whatever it edits stays in a disposable layer.
Use Cases
- Compose Claude Code and Codex in one session to handle different kinds of coding tasks
- Enforce per-team spend caps and model routing policies without prompt engineering
- Debug multi-step agent workflows collaboratively with teammates in real time on a shared session URL
- Sandbox an untrusted agent so it cannot touch your filesystem or network during development
- Swap from Cursor to Pi as the primary agent without rewriting your orchestration layer
- Define custom YAML agents for specialized tasks such as automated code review or model debate
- Track per-model token spend across harnesses on the Usage page
- Give Linux agents a disposable copy-on-write workspace so edits never touch the real files
Models Under the Hood
as of 2026-09-22
Limitations
- Omnigent is alpha software built in the open, so stability and documentation may still be evolving.
- The 0.16.0 release shipped breaking changes: saved preferences and inference snapshots are now capped at 65,535 bytes, oversized preferences reset to defaults on upgrade, and only session owners can manage MCP servers or replace agent bundles — collaborators who previously edited MCP configuration now get read-only access.
- It installs via CLI installers (curl, uv, pip, Homebrew) and expects you to manage sessions across harnesses, browsers and devices, which adds setup and operational overhead.
- Running it at scale assumes Kubernetes and sandbox infrastructure familiarity.
as of 2026-09-30
Verification history
We have re-verified omnigent 8 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
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 8 verification passes.
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
Engineering teams comfortable self-hosting and reading YAML, running multiple coding agents that need one sandboxed policy layer
What this tier adds
Starting point: Apache 2.0 open source, no license fee, unifies Claude Code, Codex, Pi, Devin and custom agents under sandboxing and contextual allow/ask/deny policies
Where the pricing makes sense
The company stage and team size where omnigent's pricing actually pencils out — and where peers do it cheaper.
Published details describe Omnigent as free and open source under Apache 2.0, with no paid tier listed. That makes it cheapest-in-class against managed agent platforms, at the cost of operating the infrastructure yourself — budget Kubernetes, sandbox backend and engineering time instead of a subscription.
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.
A single developer following the install script and running one sandboxed agent: under an hour. A team wiring policies, projects, Slack setup and shared session defaults: roughly a day, more if you are standing up Kubernetes and a sandbox backend from scratch. Expect additional time on upgrade when a release ships breaking changes.
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 a bare Claude Code or Codex CLI setup: wrap the existing harness in an Omnigent runner and keep your current prompts, adding policies on top
- →From Cursor as primary agent: register Pi or Codex as a second harness and switch with a one-line config change instead of rewriting orchestration
- →From a custom in-house agent wrapper: point Omnigent at it via the Agent Client Protocol so session tools, sub-agents, skills and policies come along
- ↗To a single-harness workflow: run Claude Code or Codex directly, since sandboxing and policy enforcement move back to your CI and cloud provider
- ↗To a fully managed agent platform: export session history and rebuild policies as platform-native rules, accepting vendor lock-in
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “omnigent”, and we withheld 6: 6 could not be judged, because “omnigent” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about omnigent.
Official links
Tools that pair well with omnigent
Common stack mates teams adopt alongside omnigent, with the specific reason each pairing earns its keep.
Imbue
Imbue is an open AI lab publishing modular, open-source coding-agent tools you run and inspect yourself.
AutoGen
Microsoft's open-source framework for building conversational and event-driven AI agents in Python and .NET.
Mastra
Open-source TypeScript framework for building durable AI agents and workflows, with a hosted platform for observability and cloud deployment.
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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