Changelogs

FriendlyReviewer now reads the AGENTS.md file at the root of your repository and applies the conventions it describes to every review — no extra configuration needed.

• Rules are extracted from AGENTS.md and injected into each review alongside your existing .friendlyreviewer/helper.md team rules.
• The file is only re-processed when its content actually changes (tracked by a content hash per project), so an unchanged AGENTS.md adds no overhead.
• Only declarative rules about the code are kept; setup instructions and other non-review content are ignored.
• You can inspect what was extracted before starting a review, with fr-agent project rules --remote <url> (CLI 1.2.0 or later) or the matching fr_get_rules MCP tool. Both read your local AGENTS.md by default; override the path with --agents-md <path>.
• The answer tells you where you stand: ok returns the enforced rules, none means nothing is cached yet (rules are generated on your next review), and version_mismatch means your local AGENTS.md no longer matches the cached version.

When a review failed for an internal reason, the raw error could end up posted in the comment on your merge request. That no longer happens.

• Raw AI provider responses and system traces — which could include billing messages, internal endpoints or stack traces — were posted verbatim in the review comment, visible to everyone with access to the merge request.
• A failed review now posts a single neutral notice telling you the review is unavailable and that we have been alerted. The details go to our logs and to an internal alert instead.

When a merge request is too large to review, FriendlyReviewer now tells you once instead of repeating itself on every push.

• Previously, each new push to an oversized MR re-triggered the review and posted the "review aborted — too big" notice again, cluttering the thread.
• Now the notice is posted a single time. On later pushes that are still too big, FriendlyReviewer stays quiet and removes the transient "review started" comment it had just added.
• If you trim the MR back under the limit and it later grows too large again, you'll be notified once more — the state resets on its own.

The public site navigation has been reworked to make room for more entries and to behave properly on small screens:

• Nav links are now lightweight text links, keeping the header uncluttered as the number of pages grows.
• A burger menu collapses the navigation below 900px for a clean mobile layout.
• New Changelogs page (this one), fed by one Markdown file per entry with a month-based index.

The formatted review now shows how long the review took, displayed just before the review history.

GitHub and GitLab reviews now include a ready-to-copy "vibe coding" prompt block, so you can feed findings straight back to your AI coding agent.

Added a Connect with GitLab SSO button on the setup page for a one-click connection.

New Customize page walking you through how to tailor FriendlyReviewer to your project via .friendlyreviewer/ config files — set your review rules, ignore patterns and conventions, with a bootstrap guide to generate the config.

Added a per-request timeout on AI calls to prevent reviews from getting stuck when a model call hangs.

FriendlyReviewer now reviews pull requests on Bitbucket Cloud, in addition to GitHub and GitLab.

Knowledge Base analysis now batches multiple files per AI call, and the KB/dependencies phase is skipped on update reviews — noticeably reducing review time.

The dashboard's "Settings" section is now Platforms, with a reordered navigation to make connecting GitHub and GitLab clearer.

Review severity is now shown with weather-themed badges, giving a quicker read of a review's overall risk at a glance.

Review output is now grouped and easier to scan, with emoji severity badges that surface the most important findings first.

Added a context-light hosted MCP endpoint so MCP-compatible agents can request reviews. Access is scoped per review with short-lived tokens, and authentication failures now return a proper OAuth challenge.

FriendlyReviewer is now available as a standalone command-line agent (fr-agent), so your AI coding agents can trigger and drive reviews directly from the terminal. The friendly-review workflow has been migrated to the new CLI.

When a PR or MR is updated, FriendlyReviewer now re-reviews using a single diff-based pass, so follow-up reviews focus on what actually changed rather than re-scanning the whole change.