What input formats work?

The most common input is a list of commit messages from git log output, but PR titles and descriptions or hand-written features-and-fixes lists also work fine. Provide enough detail for the model to group and summarize sensibly — more structured input produces noticeably better changelogs.

What format does it output?

Most output follows Keep a Changelog (Added, Changed, Fixed, Deprecated, Removed, Security). Some tools also support Conventional Commits format. Output is Markdown ready for GitHub releases. Specify the format you want if your project follows particular conventions.

Can it categorize changes automatically?

Yes. The model groups changes by type — features for new functionality, fixes for bugs, breaking changes for API alterations, plus performance and internal refactors. This avoids the manual categorization step and keeps grouping consistent across releases.

Will it identify breaking changes?

It catches the obvious indicators reliably — API removals, signature changes, version bumps — but it's less reliable on subtle breakages like changed default values or modified return types. Always verify breaking changes manually, since they have outsized impact on your users.

What about user-friendly summaries?

This is where the model shines. A technical commit like 'fix: handle null pointer in async handler' becomes a user-readable entry like 'Fixed crash when async operation returns no data'. End users care about the effect, not the implementation, and the translation is exactly what changelogs are for.

Should I use it for every release?

It's optional. For larger releases with many changes, the model saves real time. For small releases with one or two changes, writing it by hand is faster. For libraries that follow strict SemVer, the consistent categorization helps maintain conventions across many releases.

Is the data sent to a server?

Yes — generation requires an LLM API call. Avoid sending sensitive details like internal URLs, undisclosed security advisories, or confidential business changes that aren't yet public.

AI Changelog Generator

Server-powered

Generate clean changelogs from commit messages using AI online. Free changelog generator following Keep a Changelog format.

AI-Powered Developer Tools Generators

Instant results

Version

Release Date

Paste commit messages or describe your changes

0 commits | 0 characters

What is Keep a Changelog?

Keep a Changelog is a widely adopted standard for maintaining changelogs in software projects. It provides a consistent, human-readable format that makes it easy for users and contributors to understand what has changed between versions.

The format organizes changes into six categories: Added for new features, Changed for modifications to existing functionality, Deprecated for features that will be removed, Removed for features that have been removed, Fixed for bug fixes, and Security for vulnerability patches.

Each version entry includes a release date and groups changes by category. This structure makes it simple to scan for specific types of changes, such as security patches or breaking changes that might affect your integration.

Our AI changelog generator automatically categorizes your commit messages into these standard sections, saving you the manual effort of sorting and formatting each entry. It understands conventional commit prefixes like feat:, fix:, and chore: as well as plain English descriptions.

Why Maintain a Changelog?

User Communication

A changelog tells users what has changed, what new features are available, and what bugs have been fixed. It is the primary communication channel for release updates.

Team Alignment

Developers, QA, and product managers can quickly understand the scope of each release. It serves as a historical record of project evolution and decision-making.

Version Tracking

Following semantic versioning alongside a changelog makes it clear when breaking changes occur. Users can confidently upgrade knowing exactly what to expect.

Security Audits

Security-conscious users and organizations need to know when vulnerabilities are patched. A dedicated Security section makes this immediately visible.

Deprecation Notices

A changelog gives you a place to announce deprecations ahead of removal, giving users time to migrate to alternatives before breaking changes ship.

Open Source Best Practice

Maintaining a CHANGELOG.md is an industry standard for open source projects. It signals project maturity and professionalism to potential contributors.

How It Works

Paste Commits

Paste your git commit messages, conventional commits, or plain English descriptions of what changed. Set your version number and release date.

AI Categorization

Our AI analyzes each commit and categorizes it into the proper Keep a Changelog section: Added, Changed, Deprecated, Removed, Fixed, or Security.

Copy or Download

Preview the formatted changelog, toggle between rendered and raw Markdown views, then copy to clipboard or download as a .md file.

Changelog Categories Explained

Added

New features and capabilities that did not exist before. Maps to feat: in conventional commits.

Changed

Modifications to existing features or behavior. Includes refactors, performance improvements, and style changes.

Deprecated

Features that are still available but will be removed in a future version. Gives users advance notice to migrate.

Removed

Features, endpoints, or functionality that have been deleted. Breaking changes often fall into this category.

Fixed

Bug fixes and corrections. Maps to fix: in conventional commits.

Security

Patches for security vulnerabilities. Critical for informing users about fixes they should apply immediately.

How to Use AI Changelog Generator

Provide commit/feature list

Paste git log output, PR list, or manually written changes. More detail = better changelog.

Choose format

Keep a Changelog, Conventional Commits, or custom. Standardized formats are easier to maintain.

Generate changelog

AI groups by category (Added/Changed/Fixed), summarizes technical commits into user-readable language.

Review and adjust

Verify breaking changes are flagged correctly. Adjust language for user audience. Add release date and version number.

When to Use AI Changelog Generator

Software releases

Every release deserves documentation, and writing it during the crunch right before shipping is no fun. The model can produce a respectable changelog from your commit history, which is especially valuable on projects where you've made dozens of small commits and need someone to group them sensibly.

Library and SDK releases

When users need to upgrade a library, they need to know what changed in language they can act on. The model translates technical commits into user-readable language, flags breaking changes, and helps you stay consistent with SemVer conventions across releases.

Internal release notes

Even internal releases benefit from a brief written record. A structured summary helps product teams track what's shipping, support teams understand new behavior, and executives skim what's been delivered without reading every PR description.

Migration guides

For major versions, users need more than a list of breaking changes — they need migration steps. The model can extract breaking changes from your commits and suggest the upgrade path, giving you a draft that's a useful starting point even though it still needs manual review.

AI Changelog Generator Examples

From commit list

Input

feat: add dark mode\nfix: resolve memory leak in async handler\nrefactor: simplify auth logic\nbreaking: API endpoint /users moved to /api/v2/users

Output

## [Version] - 2024-01-15\n\n### Added\n- Dark mode support\n\n### Fixed\n- Memory leak in async handler\n\n### Changed\n- Simplified authentication logic\n\n### Breaking Changes ⚠️\n- API endpoint /users moved to /api/v2/users

Commits grouped by category, with the breaking change explicitly flagged using a warning emoji. The output follows Keep a Changelog format and is ready to drop into a GitHub release.

User-friendly summary

Input

feat: implement caching layer for API responses

Output

### Added\n- Faster API responses with intelligent caching

A technical commit message about implementing a caching layer becomes a user benefit about faster API responses. End users care about what changes for them, not the implementation details underneath.

Detected breaking change

Input

refactor: rename getUser to fetchUser in API client

Output

### Breaking Changes ⚠️\n- API client method `getUser` renamed to `fetchUser`. Update calls accordingly.

The model recognizes that renaming a public method is breaking (existing calls will fail) and adds a migration instruction. This is the kind of nuance that helps library users upgrade smoothly instead of running into surprise build errors.

Tips & Best Practices for AI Changelog Generator

1.Verify breaking changes carefully. The model can miss subtle ones — a changed default value, a modified return type, an altered exception behavior — that cost users real time when they hit them. Manual review is essential for SemVer accuracy.
2.Give the model more context than just commit subjects. Adding PR descriptions and short feature summaries produces dramatically richer changelog entries than bare commit messages alone.
3.Standardize the format across releases. Pick Keep a Changelog or Conventional Commits and stay with it — consistency makes your changelog actually useful as a long-term reference.
4.Add the release date and version yourself. The model produces the content; you supply the metadata so it lands in the right place.
5.For libraries, write migration guidance for any breaking change. The model can suggest steps, but you need to verify they actually work before users see them.
6.Trim what doesn't matter. Internal refactors, dependency bumps, and doc-only fixes generally don't need changelog entries unless they affect users in some observable way.

Frequently Asked Questions

It produces release notes from raw input — commit messages, PR descriptions, or a manually written list of features and fixes. The model groups changes by category (features, fixes, breaking changes), summarizes them in language users can act on, and outputs in standard formats like Keep a Changelog or Conventional Commits.

AI Changelog Generator

What is Keep a Changelog?

Why Maintain a Changelog?

User Communication

Team Alignment

Version Tracking

Security Audits

Deprecation Notices

Open Source Best Practice

How It Works

Paste Commits

AI Categorization

Copy or Download

Changelog Categories Explained

Added

Changed

Deprecated

Removed

Fixed

Security

How to Use AI Changelog Generator

Provide commit/feature list

Choose format

Generate changelog

Review and adjust

When to Use AI Changelog Generator

Software releases

Library and SDK releases

Internal release notes

Migration guides

AI Changelog Generator Examples

From commit list

User-friendly summary

Detected breaking change

Tips & Best Practices for AI Changelog Generator

Frequently Asked Questions

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