Meta-Harness by Databricks and Neon

Notifications You must be signed in to change notification settings

Fork 99

Star 803

BranchesTags

Open more actions menu

Folders and files

NameName

Last commit message

Last commit date

Latest commit

History

25 Commits

25 Commits

.github

.github

ap-web

ap-web

deploy

deploy

docs

docs

examples

examples

omnigent

omnigent

scripts

scripts

sdks

sdks

tests

tests

.gitignore

.gitignore

.pre-commit-config.yaml

.pre-commit-config.yaml

.python-version

.python-version

CONTRIBUTING.md

CONTRIBUTING.md

LICENSE

LICENSE

NOTICE

NOTICE

README.md

README.md

SECURITY.md

SECURITY.md

openapi.json

openapi.json

pyproject.toml

pyproject.toml

pyrefly.toml

pyrefly.toml

railway.toml

railway.toml

render.yaml

render.yaml

setup.py

setup.py

uv.lock

uv.lock

uv.toml

uv.toml

Repository files navigation

A meta-harness for all your AI agents

Omnigent provides a common layer over Claude Code, Codex, Pi, and the agents you write yourself: swap or combine harnesses without rewriting, keep them in check with policies and sandboxing, and collaborate in real time on the same live session, from any device.

omnigent.ai · ⬇️ Download the macOS desktop app

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. Use Claude Code, Codex, 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 or Daytona sandboxes, launched from the CLI or provisioned by the server per session (managed hosts). More providers coming soon.

🛡️ 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.

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

Prefer to install manually?

Omnigent needs Python 3.12+. Install the omnigent package:

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

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

Toolchain and prerequisites (if the installer reports a missing tool)

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 Claude, Codex, and Pi coding harnesses. omnigent run installs the harness CLI you pick. https://docs.npmjs.com/downloading-and-installing-node-js-and-npm

tmux, required by the native omnigent claude / omnigent codex wrappers (brew install tmux / apt install tmux; the installer offers to install it for you).

Databricks (optional). To use a Databricks workspace as your model provider, install Omnigent with the databricks extra: uv tool install "omnigent[databricks]". Signing in to the workspace also uses the Databricks CLI.

1. 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 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, or your own agent:

omnigent claude # Claude Code, in a session your team can join omnigent codex # Codex omnigent run path/to/agent.yaml # your own agent (see "Write your own agent")

🐙 Polly and 🟠🔵 Debby

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

omnigent run examples/polly/ omnigent run examples/debby/

Run an orchestrator on a different harness (sub-agents keep their own):

omnigent run examples/polly/ --harness pi omnigent run examples/debby/ --harness openai-agents

🐙 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.)

Prefer the browser? Start a server and register your machine as a host:

omnigent server start # start the local server and web UI in the background omnigent host # (separate terminal) register this machine as a host

In the web UI, hit New Chat, pick your machine, and go. Check status with omnigent server status; stop everything with omnigent stop.

1. 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.

Gateway base URLs (OpenRouter, Ollama)

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.

1. 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 deploys with one click; Fly.io, Railway, Hugging Face Spaces, and Modal are covered too. 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).

1. Collaborate with your team

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

OMNIGENT_AUTH_ENABLED=1 omnigent server start

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

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

omnigent run --fork

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.

1. 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.

Write your own agent

An agent is a short YAML file: your prompt, your tools, and optional helper 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: codex, codex-native, claude-native, openai-agents, pi

tools:

A local Python function (schema auto-generated from the signature)

word_count: type: function callable: mypackage.mymodule.word_count

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

[truncated for AI cost control]