Kepler AI Coding: The Best AI Coding Workflow with GitKraken Kepler in 2026

Introduction

This Kepler AI coding guide covers GitKraken Kepler, an Agentic Development Environment (ADE). GitKraken describes Kepler as a client that works with coding-agent harnesses through the Agent Client Protocol (ACP). Its examples include Claude Code and Codex CLI.1

This guide explains the protocol roles and how to plan and review agent-assisted coding work. It draws on documentation reviewed for this article, not hands-on testing. The workflow tips below are developer advice, not independently tested Kepler features.

What Is GitKraken Kepler?

The client and the coding agent have different roles. GitKraken describes Kepler as the client and Claude Code or Codex CLI as the agent harness. ACP connects them.1

For a related walkthrough on this site, see the GitKraken Kepler tutorial. This article focuses specifically on GitKraken's tool rather than unrelated products that also use the name Kepler.

From Individual Sessions to Coordinated Work

Several active coding sessions can be hard to track. Record each task's goal, owner, repository, branch and checks still needed.

For a microservice change, start by identifying the affected services and their dependencies. Do not assume that individually plausible changes will work together once merged.

Keeping Task Status Clear

A Kanban-style board can help track coding tasks. Use stages that fit your process, such as planned, in progress, awaiting review and complete. These are suggested labels, not confirmed Kepler stage names.

Record what is blocked and what evidence is needed to move a task forward. An agent finishing its response is not the same as a change passing review.

Kepler AI Coding: ACP, MCP and Agent Roles

ACP and MCP serve different roles. GitKraken describes ACP as the link between a client such as Kepler and an agent harness. Model Context Protocol (MCP) connects AI applications to external tools, data and services.12

Coding-Agent Integrations Versus Model Selection

Claude Code and Codex CLI are examples of coding-agent harnesses in the GitKraken source. They should not be listed alongside a model name as if every item were the same type of integration.1

Choose the agent integration first. Then check that agent's model options and account requirements. A model name or ChatGPT subscription does not prove a Kepler integration exists. For broader reading, see our AI coding assistants guide for beginners.

Sessions and Continuity

Session controls are not recovery guarantees. GitKraken describes ACP functions for creating, listing, resuming and closing sessions. That does not prove the application will survive every restart or lost connection.1

Keep the task goal, relevant commands and review status recorded so that another developer can understand what remains to be done.

Worktrees and Safety Boundaries

If your workflow uses Git worktrees, check the branch and working directory during review. Separate working folders help distinguish changes. They do not prove that agent commands run in a secure sandbox.

Review permissions, credentials and command scope separately. A clean branch or an explanation of the agent's reasoning is not evidence that a command is safe to run.

Planning Tasks Across Repositories

Plan Kepler AI coding work across repositories as one delivery goal. A feature may involve a frontend update, an API change and a database migration. Record each repository's requirements and checks.

A practical task description should include:

• The expected behavior and acceptance criteria.
• The repositories, services and files likely to be affected.
• Compatibility requirements between components.
• Tests, build commands and migration checks.
• Actions requiring human approval, such as destructive migrations or a production deployment.

The following is a suggested review process, not an official Kepler architecture diagram:

Define goal and acceptance criteria
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                 v
Identify repositories and dependencies
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                 v
Delegate a bounded task to a coding agent
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                 v
Review diffs and run relevant checks
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                 v
Resolve failures and obtain human approval
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                 v
Merge or deploy through the team's existing process

Coordinating a Frontend, API and Database Change

Agree on the API contract before implementing dependent frontend work. Review migration behavior and compatibility with the currently deployed application. Test the components together as well as separately.

For an existing Spark or Airflow pipeline, the same planning principle applies: describe the intended change, preserve the relevant configuration and use the project's established validation process. This is a workflow example, not a claim that Kepler natively runs or manages those systems.

Setting the Agent's Scope

Define each task's scope. State what the agent may edit, what it must leave alone and which actions need approval. For a refactor, separate cleanup from behavior changes. Check that generated tests cover the requirement, not just the current code.

Use smaller tasks where that makes the review easier. Do not assume a successful agent session proves the whole feature is ready to merge.

Remote or Long-Running Tasks

Before leaving a task unattended, check what your setup supports. Test lost connections, interrupted processes and failed builds. Keep enough context to restart the task deliberately.

Do not infer shipped Kepler behavior from the protocol alone. GitKraken's August 12, 2026 ACP article described remote-agent support as still in development; that dated statement is not a current release-status guarantee.1

Repeatable Workflows and Review Checklists

A session record can help another developer repeat an approach. Save the problem, prompts, commands, test results and decisions. Keep the instructions current; do not assume the same steps will always work.

Turning a Session Into Reusable Instructions

Record enough detail to explain:

1. What problem the session addressed.
2. Which repository and environment assumptions mattered.
3. What changed and why.
4. Which checks ran and what they showed.
5. What still requires review.

Avoid storing credentials or sensitive production data in a reusable example. Review the record before sharing it with the team.

Sharing and Maintaining the Instructions

Keep workflow instructions somewhere the team can find, review and update them. Include the relevant tool and dependency versions when they affect the steps.

This recommendation does not assume a native Kepler playbook index, automatic session conversion or shared-memory feature. A workflow record can be useful without any of those product capabilities.

Verification Before Reuse

Before reusing an old procedure, check that its commands, file paths and assumptions still apply. Run the relevant tests and inspect the resulting diffs. Previous success is useful context, not a substitute for checking the new change.

Starting and Managing Coding Tasks

Start each Kepler AI coding task with its goal, repository context and acceptance criteria. For a Jira or Linear ticket, include the requirements through a method your setup supports. For a pull-request follow-up, include the feedback to address.

Three Practical Starting Points

• A new task: describe the desired behavior and its limits.
• An issue or ticket: identify the requirement, dependencies and reproduction steps.
• A pull-request follow-up: specify which comments need action and which changes are out of scope.

These are ways to structure the work, not a verified list of native Kepler integrations.

Monitoring Progress

Track active tasks, blocked work and completed checks. When an agent says it is done, compare the changed files with the original request.

For team communication in Slack or another channel, summarize decisions and link to the relevant work. Do not assume the article establishes a particular Slack integration, voice-input feature or automatic task-routing behavior.

Reviewing Through the Existing Git Workflow

Review Kepler AI coding changes with the team's existing Git client and review tools. Inspect the diffs, stage intended files and resolve conflicts. Keep merge or deployment approval separate from permission to generate code. For related reading, see our GitHub AI code review guide.

Developers comparing approaches can also read our AI coding tools coverage. Product-specific integration details should be checked in the relevant current documentation before adoption.

Evaluating Productivity Without Assuming Results

Judge Kepler AI coding by reviewed work and total effort, not agent activity. Record a baseline, then compare similar tasks.

Background Work and Review Effort

If your setup supports background tasks, count the review effort as well as the time saved. Overnight work may still need debugging or rework.

Delivery Metrics

Choose measurements that reflect your team's delivery process. Compare equivalent work and account for differences in task size and risk. This article reports no measured DORA improvements, speed gains or security-test results for Kepler.

Open Protocols, Licensing and Deployment Choices

ACP and MCP describe communication interfaces. Support for an open protocol does not, by itself, establish that the application using it is open source, self-hostable or compliant with a particular organization's requirements.

Before choosing a deployment, review the applicable license, credential handling, data access and permission model. Assess these against your requirements rather than treating a generic safety statement as evidence.

For tool comparisons, see Cursor vs Windsurf and GitHub Copilot vs Cursor.

For other options, see free AI coding tools, GitHub Copilot vs Tabnine and Claude alternatives.

For a separate agent-harness example, read our DeepSeek Harness guide. This does not establish a Kepler integration.