Kepler | Mission Control for Agent-driven Development
# Any Agent. Any Model. From Idea to Merged
Kepler is the agent-agnostic development environment for running AI agents across the entire development workflow, from idea or issue to merged PR.
Available on: Windows, Mac or Linux
Bring Any Agent, Any Model.
Choose the agent and model for each task, and run several at once in isolated worktrees so their changes stay separate. Swap in something better whenever it ships. Kepler stays agnostic, so your stack stays yours.
Choose an action. Skip the prompting.
Start from a built-in Action instead of a blank prompt. One click hands the agent the issue, the diff, and the repo state, already attached. Edit the prompt, model, and skills behind any Action, or save your own.
From Idea to merged without losing the thread.
Start from an idea, an issue, or a PR. Explore a solution, turn it into tracked work, implement it, review it, address feedback, and then get it merged without rebuilding context across tools and agent sessions.
Watch Every Agent Work.
Agent Graph draws your agent work as it happens. Every Task, every session, every turn, every tool call, and every subagent appears live as the work runs. Click any node for its status, its exact input, and what it did last. It’s a practical way to run agents in parallel while seeing what each one is doing.
Turn repeatable work into reusable Actions.
Start with built-in Actions for implementing issues, reviewing PRs, and addressing feedback. Change the prompts, models, and skills behind them or build and save your own.
Swap Agents, Not Workflows.
Any agent. Any tracker. Any host, self-hosted included. Kepler works with the tools you already run, and you can change the agent per Action whenever something better ships. What you’ve configured carries over. New agent, same setup.
All The Way To Merged.
An agent finishing isn’t the same as work that’s ready to ship. Parallel worktrees put each agent’s changes on its own branch. Per-branch diffs beside the Task that created them. Stage, review, commit, open the pull request. A raw agent stops at ‘done.’ Kepler takes you the rest of the way to merged code.
Kepler fits your environment & Setup, Out of the Box
Running parallel AI agents shouldn’t force you to rewrite your toolchain or compromise your local setup. Kepler seamlessly bridges your local machine, remote boxes, and existing issue trackers, giving you full control over agent workflows without changing how you code.
Automation
Commands and worktree setup.
Keep your repo’s commands one click away. Run them automatically every time Kepler creates a worktree, so your setup script fires before the agent starts.
Views
See it how you think about it.
An Agent View with live status of agent work, a kanban showing every agent’s state at a glance, or a dense list for easy scanning. Same work, arranged the way you want to look at it.
Tasks
Start from nothing.
No repo, no worktree, or no issue? Chase an idea first, then turn it into a tracked issue once it’s worth tracking.
Search
Find Any Issue Or Pull Request.
Search and filter across every tracker, repo, and host you’ve connected, down to the single issue or PR you’re thinking of.
Integrate with Your Existing Stack
Kepler works with the agents you are already running, your Git provider, and your issue tracker. Connect your existing setup and Kepler becomes the surface around it.
GitKraken Plans & Pricing
Community
- Basic GitKraken Desktop, CLI & GitLens for OSS development
- Free
Pro
- Robust productivity & private repo access for solo devs
- $10
- $4 per seat / month, billed annually
Advanced
- More integrations, powerful features & enhanced AI to supercharge devs + teams
- $14 per seat / month, billed annually
Business
- Security, compliance & control for companies and engineering leaders
- $18 per seat / month, billed annually
Kepler FAQs
What is Kepler?
Kepler is GitKraken’s agentic development environment (ADE), which means it helps you kickoff coding agent sessions across multiple repos. It gives developers full clarity and control to scale their use of parallel agents, across planning work, writing code, and reviewing what ships. You start from your existing backlog or PR queue, run agents across multiple repos, and see every session from one surface.