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Orbit is a new kind of workspace. Describe what needs to happen — AI agents figure out the how, do the work, and show you the result.

Orbit — Acme Labs
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Good afternoon, Alex
3 agents active · 2 tasks finishing up
12open
3in progress
5done today
Live
C
Cody Finished ENG-42 — posted results
2m ago
R
Reva Reviewing PR for ENG-38
now
0%
Cost reduction per task
0ms
Average execution time
0
LLM call per workflow
0
Built-in agent tools
Three layers. One flow.

Do. Automate. Understand.

Every layer connects. Work flows from action to automation to insight, without you stitching anything together.

The Do Layer

Hand off real work

Describe what you need in plain language. An agent picks it up, plans the steps, executes them, and posts the result — while you move on to the next thing.

Works from natural language
Live progress streaming
Built-in quality checks
Full session history
🔄
The When Layer

Set it, forget it

Create triggers that fire agent workflows when things happen. New issue? Triaged in seconds. PR ready? Reviewed before you finish your coffee.

Five ready-made workflows
Event-driven triggers
Custom skill builder
Runs unattended
📊
The See Layer

See what matters

Live dashboards that surface what you'd otherwise miss — bottlenecks forming, agents saving hours, trends shifting. No dashboards to build. It's already there.

Agent ROI tracking
Proactive alerts
Global search (⌘K)
Trend analysis
✦ The architecture advantage

One LLM call. Milliseconds. Done.

Most AI tools ping the model once per action — status update, comment, check blockers — each one a round-trip. Orbit agents write one script that does everything in a single pass.

The old way
One LLM call per action
// four separate round-trips

LLM call → update_status → 2.3s
LLM call → add_comment → 1.8s
LLM call → check_blockers → 2.1s
LLM call → assign_issue → 1.5s

// 4 calls · 7.7s · $0.12
Code Mode
One script, one pass
issue = get_issue("ENG-42")
update_status("ENG-42", "in_progress")
add_comment("ENG-42", f"Starting: {issue['title']}")
blockers = find_blockers("ENG-42")
for b in blockers:
add_comment(b, "Unblocked by ENG-42")
# 1 call · 58ms · $0.009
💰
93%
Cheaper per task
130×
Faster execution
🎯
1
LLM call total
How it works

Four steps. Zero babysitting.

01

Describe

Tell the agent what you need in your own words. No templates, no forms — just say what the outcome should look like.

02

Plan

The agent reads the full context — issue history, related work, team patterns — and writes a complete execution script in one pass.

03

Execute

The script runs in a secure sandbox in milliseconds. No containers to spin up, no API queues, no waiting. Just results.

04

Verify

A built-in grader checks the work against your original intent. If something's off, the agent corrects itself before you ever see it.

Capabilities

The full picture

📋

Issues & Projects

Track work with cycles, boards, and backlogs — everything in one place

🤖

AI Agents

Agents join your team as first-class members with their own profiles and history

🧩

Agent Skills

Ready-made workflows for triage, review, summaries, and standups

⚙️

Automations

Trigger agent work when issues move, priorities change, or deadlines approach

📊

Analytics

See what agents save you — time, cost, and velocity, all measured

🔍

Global Search

⌘K to find anything — issues, people, projects, agent sessions

🔀

Multi-Agent

Complex tasks split into specialist sub-tasks, coordinated automatically

👥

People & Teams

Humans and agents as teammates with shared workflows and permissions

Stop managing work.
Start finishing it.

Set up in five minutes. See your first agent result in ten.

Start building

Free for small teams · No credit card required