You can now ask Kody to pay special attention to something specific during a review, without changing any configuration.
In a PR, use @kody review <directive>. In the CLI, use kodus review —focus.
Examples:
@kody review focus on security@kody review focus on performancekodus review —focus “edge cases”
This is especially useful for larger PRs or sensitive changes, where you want the review to go deeper on a specific area, such as security, performance, edge cases, a business rule, or a team standard.
Focus works as a priority, not a filter. Kody pays closer attention to the context you provide, but still reports other concrete issues it finds in the diff.
Learn more in the Steer a Review (Focus) documentation.
We added an option to generate a single PR comment containing all prompts and suggestions, so an external agent or AI tool can apply the fixes in one pass.
In the add repositories modal, we added select all and clear selection buttons. For teams connecting dozens of repositories, this removes the need to select each one manually and significantly reduces setup time.
Teams using Bring Your Own Key now have a detailed view of LLM usage by model on the costs screen. Instead of seeing only the total amount, you can identify exactly which providers and models are driving most of the usage and make clearer decisions about what to keep, replace, or limit.
We reorganized the code review agents to run on a unified infrastructure. Previously, parts of the flow were spread across different places, which could lead to duplicated suggestions and inconsistent behavior. With this consolidation, the pipeline is more predictable, easier to maintain, and less likely to repeat suggestions in the same PR.
We improved the deduplication algorithm so distinct bugs are not grouped as if they were the same issue. The content guard now better considers the actual content of each suggestion, reducing false-positive deduplication and making sure separate issues continue to be reported individually.
We fixed unstable behavior in prose finding generation and restored strict mode for Gemini. We also updated the Anthropic SDK, keeping compatibility with newer models and improving response stability.
We updated the permissions documentation, including the OrganizationSettings scope and the restriction that only the organization Owner can configure BYOK. This makes it clearer who can do what inside the workspace.
Review comments with broken formatting: fixed cases where Kody comments appeared with unexpected formatting in pull requests.
Retry errors with BYOK providers: added preventive adjustments to avoid retry failures when a BYOK provider returns an empty response body.
Sandbox tool errors: the agent now handles errors better when running tools in the sandbox environment, without breaking the review.
Normalized user IDs: standardized how author, reviewer, and assignee IDs are handled, preventing PRs from breaking because of type differences, such as string vs. number.
@kody conversation crashing on JSON wrapped in prose: fixed a crash that happened when Kody responded to mentions and the output came back as text containing embedded JSON.
IDE sync config now respects approval settings: IDE rule synchronization now respects the approval configuration.
Rules with multiple directories: adjusted Kody Rules validation to correctly handle rules that apply to multiple path groups.
Automatic code review config creation: when a repository did not have a code review configuration, creating a Kody Rule could fail silently. The configuration is now created automatically.
Diff re-review in the agent: improved how the agent detects diffs from new commits during re-reviews.
MCP Manager OAuth disconnect: fixed an issue with disconnecting OAuth credentials in MCP Manager that could cause inconsistent behavior.
MCP Manager production build: the MCP Manager production build now correctly passes the GitHub token.
Orphaned users in the license list: users removed from the GitHub organization now appear correctly in the license list.
Skipped review email: the notification email sent when a review is skipped now includes the author’s name/email and no longer breaks when the user does not have a license.
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We updated Kody’s review pipeline to improve analysis accuracy and reduce cases where important issues could slip through unnoticed.
The engine now identifies problematic patterns more effectively, better understands changes that span multiple files, and can review code regions with more flexibility when the context is not fully obvious.
We also optimized processing and reduced the average cost per review by around 30%.
Filters applied in Cockpit are now saved in the URL, including time range, repositories, severity, and status.
This means you can copy the link and share it with your team on Slack, for example. Anyone who opens the URL will see Cockpit with the same filters applied, without having to manually recreate the view.
This makes it easier to share specific analyses, such as what changed over the past week or an increase in security suggestions in a repository.
We unified the Kody Rules flow into a single lifecycle, regardless of where the rule was created: the library, manual creation, or inheritance from the organization to the repository.
With this change, activating, editing, disabling, and approving rules now follow the same path inside the product. We also migrated older rules to the new format and centralized the control of rules pending approval.
This makes the system more consistent, reduces unexpected states, and ensures that an active rule is actually applied in Kody reviews.
We removed the dependency on Composio as an external MCP broker and moved to our own implementation for GitHub Issues, now integrated with Kodus Issues.
This reduces an external point of failure, improves call reliability, and gives us more control over authentication, rate limits, and retries.
As part of the migration, we also removed low-usage integrations that depended on Composio, such as ClickUp and CloudId.
When you enable or disable rule inheritance from the organization to a repository, the UI now shows the new state immediately, without waiting for the API response.
If the call fails, for example because of a connection issue, the state is automatically reverted and we show an error toast. Before, users would click, wait, and not get clear feedback about what had happened.
We also improved the rules cache update. Now we only invalidate the rules affected by that inheritance change, avoiding unnecessary reloads for rules that did not change.
We added an explicit origin to all Kody Rules: LIBRARY, MANUAL, INHERITED, and SUGGESTED.
This fixes an issue where some rule creation scenarios could fail silently because the backend expected an origin value that the frontend was not sending.
Now the contract between frontend and backend is clearer, and the rule origin is validated before reaching the creation flow.
Rovo is an Atlassian AI agent that generates code and edits files automatically. When it creates commits, it includes specific metadata, such as message prefixes and custom headers.
Previously, our review engine identified those commits as human-written code and could suggest changes on them.
Now we detect Rovo commits and skip those changes both in the code review logic and in the MCP business-rule validation preflight.
We fixed the OAuth flow for connecting MCPs, such as GitHub and Jira.
Previously, users who had already completed onboarding could be blocked when trying to add a new MCP. Now the callback works at any time, as long as the state parameter is valid.
Pending rules, which need approval before entering the review pipeline, now have a dedicated section with improved UX.
The section shows a status badge, who suggested the rule, when it was suggested, and inline actions to approve or reject it.
Previously, these rules appeared mixed together with active rules, which made it easy to miss that something needed attention.
GitHub Integration GitHub App post-onboarding: we fixed an issue where some GitHub integration pages returned a 404 after the user completed onboarding. This affected routes such as /github/integration and /github/organization-name when the GitHub App installation state was not updated correctly. We also adjusted the redirect after installation and added route validations to make the integration flow more reliable across different states.
Severity slicer colors: the severity control for low, medium, high, and critical used inconsistent colors. In some places, red meant critical; in others, it meant high. We standardized it to a semantic scale: gray for low, yellow for medium, orange for high, and red for critical. This affects Cockpit filters, suggestion badges, and notification settings.
No changelog entries for this filter yet.
Review comments, commit statuses, force-push detection, and webhook coverage now work consistently for Forgejo repositories. If you self-host with Forgejo, Kody behaves just like on any other major platform.
Eliminated duplicate API calls when syncing Kody Rules from changed files. PRs with many files now close noticeably faster.
Platform settings that don’t apply to your current Git provider, such as GitHub-specific options when you’re on GitLab, are now hidden automatically. Less clutter, fewer misconfigurations.
Reconnecting a Git provider after disconnecting: previously, disconnecting a provider left stale integration configs behind, causing reconnects to fail. Now stale rows are properly cleaned up.
Kody conversation replies with empty responses: resolved an issue where Kody occasionally replied with blank content in PR comment threads when using Claude Sonnet.
NaN deduplication indices: added three-layer protection against NaN values appearing in suggestion deduplication indices, preventing rare but confusing duplicate suggestions.
Forgejo prompt field always empty: the formatPromptForLLM function was reading a non-existent field on Forgejo PRs, causing the prompt section to be blank. Now it reads the correct field.
Self-hosted AST graph reliability: the kodus-graph CLI was installed but not available to the runtime user in self-hosted worker images. Fixed PATH and install order so graph indexing works out of the box.
Orphaned agent trace records: prevented a race condition in chunked review mode that could leave orphaned IN_PROGRESS trace records in the database.
Platform settings not scoped to Git tool: settings incompatible with your current Git provider were still visible. Now they’re properly filtered based on the connected tool.
No changelog entries for this filter yet.
Track the health of your review pipeline in real time. Throughput, queue, and processing time metrics are now available in Cockpit.
The review tab has been redesigned with a cleaner interface and more direct actions. You can now analyze and act on suggestions without leaving the PR screen.
We migrated the backend from Yarn 1 to pnpm. This makes builds faster, improves lockfile consistency, and reduces cache issues across environments.
The token usage screen has been redesigned to make consumption by organization and period easier to understand. It is now simpler to track usage and see how tokens are being consumed over time.
Self-hosted analytics: we fixed the analytics integration for self-hosted deployments. Usage metrics now appear correctly in the dashboard.
Issue cache: the issue count in the sidebar could sometimes become outdated. The cache is now invalidated correctly after changes.
GitHub rate limit: we fixed a rate limit issue in cloud E2E tests. We now use round-robin across multiple bot accounts to better distribute requests.
Notification routing: we fixed the behavior of targeted notifications when combined with audience settings. Notifications now reach the right people without duplicates.
Centralized config sync: we fixed issues in the centralized sync of Kody Rules configurations across repositories. Sync now correctly handles multiple scopes, stale configurations, and merge-triggered updates.
No changelog entries for this filter yet.
We added support for Moonshot as a provider, compatible with the Anthropic API.
You can now use the Kimi Coding Plan model in BYOK setups. To configure it, set the API_MOONSHOT_API_KEY key and select the model in the organization preferences.
See the Moonshot (Kimi) documentation for more setup details.
Organizations can now set monthly token consumption limits per model.
Usage is shown in real time in the interface, with a progress bar. When usage reaches 80% of the limit, the team receives an email alert. If the limit is reached, additional reviews can be blocked automatically.
See the Monthly Spend Limit documentation for more setup details.
The review engine now handles empty or malformed LLM responses better.
This reduces loops in edge cases and helps ensure valid suggestions continue to be delivered even when the provider returns an unexpected response.
On-premise installations now automatically report health metrics, such as heartbeat, and usage data.
The goal is to make diagnosis and support easier without exposing sensitive customer data.
We improved the loading and synchronization of custom rules.
We also fixed the count of orphaned rules and made partial updates safer against race conditions.
- Support for GitLab 13.x in comment webhooks
- Diff fallback for versions earlier than 15.7
- Fixes for discussion threads and replies scoped by discussionId
- Self-hosted development environment documentation
GitHub: suspended users no longer appear in the member list for license assignment.
Update Connection: integration reauthentication now correctly clears the previous configuration.
Integration reset: Kody Rules linked to the repo/directory are now removed automatically.
Release Freeze: eligibility notifications only appear when the freeze is active.
No changelog entries for this filter yet.