Omnigent

The open-source meta-harness for all your AI agents.

Omnigent is an open-source meta-harness that gives you a common orchestration layer over Claude Code, Codex, Cursor, OpenCode, Hermes, Pi, and the agents you write yourself: swap or combine harnesses without rewriting, enforce policies and sandboxing, and collaborate in real time from any device — terminal, browser, phone, or the native desktop app.

omnigent.ai · ⬇️ Download the macOS desktop app

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Why Omnigent?

Omnigent lets you:

• 📱 Work with agents from any device, including your phone. Sessions follow you: start in your terminal, continue in the browser, pick it up on your phone. Messages, sub-agents, terminals, and files stay in sync.

• 🤖 Supervise multiple agents. Mix Claude Code, Codex, Cursor, OpenCode, Hermes, Pi, and custom agents (defined in YAML) together in the same session. Ask one agent to review another's work, or split a task across agents that are each good at different things.

• 🔌 Use any model. A first-party API key, a Claude/ChatGPT subscription, or any compatible gateway. All first-class.

• 🤝 Collaborate. Share a session so teammates can chat with your agent and watch it work live, co-drive it on your machine, or fork the conversation to continue on their own.

• ☁️ Run agents in cloud sandboxes. No laptop required: run sessions in disposable Modal, Daytona, Islo, E2B, CoreWeave, Kubernetes, OpenShell, Boxlite, or Databricks sandboxes, launched from the CLI or provisioned by the server per session (managed hosts).

• 🛡️ Govern your agents. Create policies to pause for your approval before risky actions, cap spend, or limit which tools an agent reaches. They apply to the whole server, one agent, or a single chat.

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Quick start

1. Install

One command installs Omnigent and everything it needs:

curl -fsSL https://raw.githubusercontent.com/omnigent-ai/omnigent/main/scripts/install_oss.sh | sh

Need an optional integration? Pass one or more extras to the installer:

curl -fsSL https://raw.githubusercontent.com/omnigent-ai/omnigent/main/scripts/install_oss.sh | sh -s -- --extra databricks
curl -fsSL https://raw.githubusercontent.com/omnigent-ai/omnigent/main/scripts/install_oss.sh | sh -s -- --extra modal,e2b

Available user-facing extras include:

• Model providers: databricks, bedrock, vertex
• Sandbox providers: modal, daytona, boxlite, cwsandbox, e2b, openshell, kubernetes
• SDK harnesses: antigravity, copilot, cursor, agents-sdk
• Storage and memory: s3, hindsight

Omnigent needs Python 3.12+. Install the omnigent package:

uv tool install omnigent        # or: pip install "omnigent"

Manual installs use the same extras syntax, for example:

uv tool install "omnigent[databricks,modal]"

Or with Homebrew:

brew install omnigent-ai/tap/omnigent

Or install straight from the repo:

uv tool install -q --python 3.12 git+https://github.com/omnigent-ai/omnigent.git

• uv (required). https://docs.astral.sh/uv/getting-started/installation/ The installer offers to set this up for you.
• git (required).
• Node.js 22 LTS or newer with npm (for the coding-harness CLIs installed by omnigent run) and pnpm (for the web UI). You can get both from a single Node install; pnpm is available via corepack enable or npm install -g pnpm.
• Kiro CLI (optional), for omnigent kiro: install with curl -fsSL https://cli.kiro.dev/install | bash, then sign in with Kiro. Kiro tool approvals stay answerable in the embedded Terminal; supported one-time approvals also appear as Chat cards. See docs/kiro-native-elicitation.md.
• tmux, required by the native omnigent <harness> terminal wrappers (claude, codex, cursor, hermes, kiro, pi) (brew install tmux / apt install tmux; the installer offers to install it for you).
• bubblewrap (bwrap), Linux only. The native omnigent <harness> terminal wrappers and the pi harness wrap each agent terminal in a bwrap OS-sandbox; on Linux that isolation is mandatory, so a missing bwrap binary makes those terminals fail to start (apt install bubblewrap; the installer offers to install it for you). macOS uses the built-in seatbelt sandbox and needs nothing extra.
• Databricks (optional). To use a Databricks workspace as your model provider, install Omnigent with the databricks extra: uv tool install "omnigent[databricks]" — or pass it to the bootstrap installer with ... | sh -s -- --extra databricks. Signing in to the workspace also uses the Databricks CLI.

Omnigent runs natively on Windows in a degraded mode. The install_oss.sh bootstrap is POSIX-only, so install with uv directly:

uv tool install --python 3.12 omnigent
# or from the repo:
uv tool install --python 3.12 git+https://github.com/omnigent-ai/omnigent.git

What works on Windows: omnigent server, the web UI, and the SDK-based harnesses (omnigent run <agent.yaml> with the claude-sdk / cursor / codex harnesses). Agents run under a Windows Job Object for process-tree containment.

What is not available on Windows (use Linux/macOS, or WSL, for these):

• the native omnigent claude / omnigent codex / omnigent cursor tmux/PTY terminal wrappers (run an SDK harness or the web UI instead);
• bwrap/seatbelt filesystem & network sandboxing and the L7 egress proxy — the Job Object backend contains the process tree and enforces resource limits but does not isolate the filesystem or network.

When a newer release is on PyPI, Omnigent shows a one-line notice (once per release) pointing here. To update:

omni upgrade            # detects how you installed, drains & stops the local
                        # server, then runs the matching upgrade command
omni upgrade --check    # just report whether a newer release is available

omni upgrade waits for in-flight agent sessions to finish before stopping the local server (pass --force to stop them immediately); the next omni command brings the server back up on the new version. Source checkouts update with git pull instead. Silence the notice with OMNIGENT_NO_UPDATE_CHECK=1.

The check queries your configured package index — honoring UV_INDEX_URL / PIP_INDEX_URL and your uv.toml / pip.conf (default PyPI), so private mirrors work out of the box; override with OMNIGENT_INDEX_URL if needed.

Preview the CLI/profile cleanup that would run by default:

omnigent uninstall

Remove the CLI and installer-managed PATH entries while keeping your local history, credentials, and projects:

omnigent uninstall --yes

To also remove Omnigent state under ~/.omnigent, pass --purge; Omnigent backs it up outside the target before deletion. Your ~/omnigent workspace is kept unless you explicitly add --purge-workspace.

omnigent uninstall --purge --yes

If the installed wheel is broken or omnigent is not on PATH, run the standalone script instead:

curl -fsSL https://raw.githubusercontent.com/omnigent-ai/omnigent/main/scripts/uninstall_oss.sh | sh

Add --yes to the standalone script to perform the previewed CLI cleanup.

2. Start your first agent

omnigent picks a model with you and starts a session in your terminal. It also launches a local web UI at http://localhost:6767 that shows the same session in the browser, or on a phone on your network (step 4). The desktop app wraps that same UI in a native window and adds OS notifications (with a configurable sound) and a dock badge — download it for macOS.

[!NOTE] The install puts two names for the same CLI on your PATH: omnigent and the shorter omni. They're interchangeable.

[!TIP] On first run, Omnigent picks up model credentials already in your environment (an ANTHROPIC_API_KEY / OPENAI_API_KEY, or a claude / codex CLI you're logged into) and offers one as the default.

omnigent

Or launch a specific agent runtime:

omnigent claude                      # Claude Code, in a session your team can join
omnigent codex                       # Codex
omnigent cursor                      # Cursor
omnigent opencode                    # OpenCode
omnigent hermes                      # Hermes Agent (Nous Research)
omnigent pi                          # Pi

Using OpenClaw? See the OpenClaw integration guide to import its coding agents or drive a live OpenClaw Gateway session over ACP.

🐙 Polly and 🟠🔵 Debby

Two example agents ship with the repo, and they make good first sessions:

omnigent run examples/polly/
omnigent run examples/debby/
omnigent run examples/deep-research/

# ...or on a different harness (sub-agents keep their own):
omnigent run examples/polly/ --harness <harness>
omnigent run examples/debby/ --harness <harness>

🐙 Polly is a multi-agent coding orchestrator who writes no code herself. She's the tech lead: she plans, delegates the work to coding sub-agents (Claude Code, Codex, or Pi) in parallel git worktrees, then routes each diff to a reviewer from a different vendor than the one that wrote it. You merge.

🟠🔵 Debby is a brainstorming partner with two heads, one Claude and one GPT. Every question you ask goes to both heads, and she lays the two answers out side by side. Type /debate and the heads critique each other for a few rounds before converging. (She needs both a Claude and an OpenAI credential; see step 3.)

🔎 Deep Research is a single agent that answers a question with a cited, cross-checked report. It plans sub-queries, searches the live web and reads full pages through an MCP search server, and verifies each claim across independent sources. It's also the simplest example to copy from: one agent plus one tools/mcp/*.yaml server, no sub-agents.

Prefer the browser? One command starts the local server and registers this machine as a host:

omnigent start   # starts the local server and registers this machine as a host

Open the server URL it prints, hit New Chat, pick your machine, and go. Check status with omnigent server status; stop everything with omnigent stop.

3. Choose & switch models

omnigent setup

Add a credential, set a default, or remove one, grouped by agent. Omnigent works with four kinds of credentials:

	Kind	What it is
🔑	API key	A first-party vendor key for Anthropic, OpenAI, and similar providers
🎟️	Subscription	A Claude Pro/Max or ChatGPT plan, via the official claude / codex CLIs
🌐	Gateway	Any OpenAI- or Anthropic-compatible base_url and key (OpenRouter, LiteLLM, Ollama, vLLM, Azure)
🧱	Databricks	A Databricks workspace profile (requires the databricks extra)

Defaults are per agent, so a Claude default and a Codex default coexist. You can also switch models in the middle of a session with the /model command.

When you add a Gateway credential, omnigent setup asks for a base URL and a key. The base URL depends on which agent you point it at:

Provider	For	Base URL	Key
OpenRouter	Claude Code	https://openrouter.ai/api	your OpenRouter key (sk-or-…)
OpenRouter	Codex / OpenAI agents	https://openrouter.ai/api/v1	your OpenRouter key (sk-or-…)
Ollama (local)	Codex / OpenAI agents	http://localhost:11434/v1	any value (Ollama ignores it)

For Claude Code, point at OpenRouter's Anthropic-compatible endpoint (…/api, not …/api/v1). For Codex and the OpenAI-agents harness, use the OpenAI-compatible …/api/v1.

4. Deploy a server (and use it from your phone📱)

Run Omnigent on a server with a stable URL (deploy/README.md is the full guide) and your sessions become reachable from anywhere, including your phone. The web UI is built for mobile, so you get the same chat, sub-agents, terminals, and files, in sync with your laptop.

One docker compose up runs the server on any host you have (a VPS, a home server); Render and Railway deploy with one click; Fly.io, Hugging Face Spaces, Modal, Cloudflare (serverless, scale-to-zero), and Databricks Apps (backed by Lakebase Postgres and Unity Catalog Volumes) are covered too — and a Cloudflare quick tunnel (public) or Tailscale (private) reaches a server running on your own laptop without a deploy. The server can also provision a cloud sandbox per session (managed hosts), so no laptop has to stay online. The full menu of targets, the database options, and the sandbox setup live in deploy/README.md.

Once the server is up, sign in and register your laptop as a host:

omnigent login https://your-host    # sign in once; run / attach / host reuse the token
omnigent host  https://your-host    # new sessions can now run on this machine

[!TIP] On your own network you don't need a deploy. Open your machine's LAN address on your phone (e.g. http://192.168.x.x:6767).

5. Collaborate with your team

Omnigent supports multi-user accounts, controlled by one environment variable:

OMNIGENT_AUTH_ENABLED=1 omnigent server --background

The Docker deploy in step 4 turns it on for you (OMNIGENT_AUTH_ENABLED defaults to 1 there).

Invite your teammates

Open the web UI (http://localhost:6767 locally, or your host's URL) and sign in as admin; first run prints the password and saves it locally. Then open Admin → Members → Invite to create a single-use invite link, no email server needed. Send it over; your teammate opens it, sets a password, and they're in. Signup is invite-only.

[!NOTE] Teammates need to be able to reach the server. A local server is only reachable on your network; for anyone off it, deploy an always-on host (see step 4).

Code together

• Share a live session. Hit Share in the web UI and send the link; teammates watch your agent work and chat with it in real time.

• Co-drive. A teammate co-attaches to your running session; their messages execute on your machine. Great for pairing or handing the keyboard to a domain expert mid-investigation.

  omnigent attach <session_id>
  

• Fork. Clone a conversation onto your own machine and continue independently from the fork point.

  omnigent run --fork <session_id>
  

[!TIP] Want your team to sign in with the logins they already have (Google, GitHub, Okta, Microsoft)? Set OMNIGENT_OIDC_ISSUER plus a client ID and secret on your deployed server and restart. The full walkthrough, domain allowlists, and the proxy-only header auth mode are covered in deploy/README.md#auth.

6. Govern your agents with policies

Policies decide what an agent may do: run shell commands, edit files, spend tokens. They check every action and either allow it, block it, or pause to ask you first.

• In the web UI: open a session's info panel to browse the available policies and toggle them on or off.
• In chat: ask. "Add a policy that asks me before running shell commands." The agent sets it up for you.

Want defaults that apply to everyone, or to a specific agent? Define them in your server config or an agent's YAML:

policies:
  approve_shell:
    type: function
    handler: omnigent.policies.builtins.safety.ask_on_os_tools   # ask before shell / file writes
  cap_calls:
    type: function
    handler: omnigent.policies.builtins.safety.max_tool_calls_per_session
    factory_params:
      limit: 50                    # cap how many tools one session can call
  budget:
    type: function
    handler: omnigent.policies.builtins.cost.cost_budget
    factory_params:
      max_cost_usd: 5.00           # hard spend cap...
      ask_thresholds_usd: [3.00]   # ...with a soft warning on the way

Policies stack across three levels, server-wide (admin), per-agent (developer), and per-session (you), with the stricter session rules checked first. Spend caps and access limits ship as builtins.

See the policy guide for the full catalog and trust model.

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Write your own agent

An agent is a short YAML file: your prompt, your tools — local Python functions, MCP servers, and sub-agents a supervisor can delegate to. You don't have to write it by hand: agents can build agents, so describe the agent you want in any Omnigent chat and it authors the file for you.

name: my_agent
prompt: You are a helpful data analyst.

executor:
  harness: claude-sdk          # or: claude-native, codex, codex-native, cursor,
                               # cursor-native, hermes, hermes-native, opencode,
                               # pi, pi-native, openai-agents

tools:
  # A local Python function (schema auto-generated from the signature)
  word_count:
    type: function
    callable: mypackage.mymodule.word_count

  # Tools from an MCP server (a local command, or a remote URL)
  docs:
    type: mcp
    url: https://example.com/mcp

  # A sub-agent the supervisor can delegate to
  researcher:
    type: agent
    prompt: Search for relevant information and summarize it.
    tools:
      word_count: inherit

Run it with:

omnigent run path/to/my_agent.yaml

The same file can declare sub-agents and reviewers. For a fuller example, see Polly at examples/polly/, and the Agent YAML spec for the full schema.

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Telemetry

Omnigent collects anonymized usage data (telemetry) by default. This data contains no sensitive or personally identifiable information. If you're using Omnigent through a managed service or distribution, please consult your managed service agreement to determine any data collection that may impact your use of the service. To opt out, follow our instructions in Usage Telemetry.

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Contributing

Contributions are welcome. See CONTRIBUTING.md for how to set up your environment, run the checks, and open a pull request.

Adding or changing support for a harness (Claude, Codex, Cursor, OpenCode, Hermes, Pi, ...)? Run the harness test bench to check its capability matrix against observed behavior.

Contributors

Thanks to all of our amazing contributors!