Featurevisor v3 – open-source feature flags management with Git

Featurevisor released version 3.0 of its open-source feature flag management tool, introducing targets, catalog, sets, and promotions to improve organization and scalability. The update adds first-class support for AI agents and allows incremental SDK upgrades with backward-compatible datafiles.

Featurevisor v3.0 is here Fahad Heylaal https://twitter.com/fahad19 on Featurevisor v3.0 is here, focused on making projects easier to organize and scale , datafiles easier to shape , and SDKs easier to use consistently across multiple different languages , with first class support for AI agents . This is a breaking release, but the generated datafile schema remains version 2 . That means you can upgrade your Featurevisor project first, publish v3-generated datafiles, and then upgrade application SDKs one by one at your own pace allowing you to coordinate the release with your application teams very conveniently . What's new? whats-new The biggest changes in v3 are: Targets /docs/targets/ replace scopes and automatic tag-based datafile /docs/building-datafiles/ generation. Catalog /docs/catalog/ is a new server for browsing your definitions. Sets /docs/sets/ let one repository contain multiple independent project trees. Promotions /docs/promotions/ copy definitions between sets, with optional promotion flows. Feature /docs/features/ tags tags are now optional. Projects /docs/projects/ have no default environments /docs/environments/ unless you define them. Namespaced /docs/namespaces/ keys use . as the default separator.- JavaScript SDK /docs/sdks/javascript/ now uses createFeaturevisor as the main API. - SDK modules /docs/sdks/javascript/ modules replace hooks. - SDK diagnostics /docs/sdks/javascript/ diagnostics replace the old logger API. setDatafile merges by default, enabling loading datafiles incrementally /docs/use-cases/on-demand-datafiles/ . Go /docs/sdks/go/ , Swift /docs/sdks/swift/ , Java /docs/sdks/java/ , Ruby /docs/sdks/ruby/ , Python /docs/sdks/python/ , and PHP /docs/sdks/php/ SDKs have been ported to v3 behavior. Catalog in AI Agents catalog-in-ai-agents With skills /docs/skills/ , you can start Featurevisor's new catalog /docs/catalog/ server within your AI agent's browser directly for ease of browsing your definitions, while still prompting for making any changes. When I started Featurevisor, I built it with the idea of developers writing the definitions by hand declaratively. Now I feel we live in a different world where AI agents are more capable of generating the definitions for us better. Being strictly declarative has been very advantageous for Featurevisor leveraging agentic workflows. Combine prompting with browseable catalog in your favourite AI agent, and you have a powerful combination for managing everything. Targets for datafiles targets-for-datafiles Scopes were previously introduced to make smaller datafiles from partially known contexts. In v3, that responsibility belongs to Targets /docs/targets/ . Targets live as files in the targets directory: description: Web datafile pick all features tagged with 'web'tag: web apply partially known context upfront to reduce conditions/segments/rules in generated datafilecontext: platform: web You can also select features directly: description: Checkout featuresincludeFeatures: - checkout - shared.navigationexcludeFeatures: - checkout.internal Every datafile is now produced from a target. The smallest possible target is simply: description: All features Unless anything else is specified, the target will include all features in the project. The generated datafiles follow the convention of datafiles/<environment /featurevisor-<target .json . Sets and promotions sets-and-promotions With Sets /docs/sets/ , which is optional to adopt, a project can be split into independent trees: sets/├── dev/├── staging/└── production/ Each set owns its own attributes, segments, features, targets, and tests. You can then use Promotions /docs/promotions/ to move definitions forward: bash $ npx featurevisor promote --from=dev --to=staging --apply This works well for teams that want release lanes, ownership boundaries, or distinct surfaces while keeping everything Git-based. The same sets-based approach also be used for completely different areas of your business, such as different products, teams, or geographies, that need to be managed independently. SDKs sdks The JavaScript SDK has a smaller and clearer public API: js import { createFeaturevisor, type Featurevisor, type FeaturevisorOptions, type FeaturevisorModule,} from '@featurevisor/sdk' Modules replace hooks: js const trackingModule: FeaturevisorModule = { name: 'tracking', after evaluation { // send exposure or diagnostic data to your own system return evaluation },}const f = createFeaturevisor { datafile, modules: trackingModule ,} Diagnostics are now the standard way to observe SDK behavior: js const f = createFeaturevisor { logLevel: 'info', onDiagnostic diagnostic { // send to your observability system },} Nothing changes for the evaluation methods. The Go /docs/sdks/go/ , Swift /docs/sdks/swift/ , Java /docs/sdks/java/ , Ruby /docs/sdks/ruby/ , Python /docs/sdks/python/ , and PHP /docs/sdks/php/ SDKs have also been aligned with the same v3 behavior where practical for each language. Datafile schema datafile-schema Featurevisor v3 still generates schema version 2 datafiles: { "schemaVersion": "2"} The old v1 datafile generation path and --schema-version selection are gone from active usage. If you still have a consumer that requires v1 datafiles, keep using an older Featurevisor v2 CLI for that consumer while you migrate it. Migration guide migration-guide The full migration guide is available here: The recommended path is: - Upgrade your Featurevisor project and generate v3 datafiles. - Publish the datafiles. - Upgrade application SDKs one at a time. Because the datafile schema remains version 2 , this can be done incrementally for applications already using v2-compatible SDKs. Where we stand today where-we-stand-today Featurevisor has come a long way since its inception /blog/introducing-featurevisor/ in 2023, going stable with v1 /blog/v1-release/ in the same year later. SDK /docs/sdks/ coverage has improved significantly since v2 /blog/v2-release/ release last year, along with skills /docs/skills/ for AI agents too for more automation. Now even more powerful with the new catalog /docs/catalog/ . v3 is all about making things more performant and scalable, especially for larger projects. I mean, really large projects with thousands of features, segments, rules, and what not. With the new improvements, I do not foresee scaling concerns being a blocker for adopting Featurevisor anymore. Based on the numbers from npm, the usage of Featurevisor has been growing steadily. And word of mouth will play a key role in the future. I am hoping the v3 release will help it grow even faster. Going forward going-forward I have been quietly expanding the visor family with Messagevisor https://messagevisor.com for i18n and l10n needs and Eventvisor https://eventvisor.org for analytics schema governance , and I will be spending some time there to make them stable and production ready. There will be synergy between the tools, like how Messagevisor already has a Featurevisor module https://messagevisor.com/docs/modules/featurevisor/ . More to come on that front. Goal is still to keep them as separate projects, while making it easier to use them together. Once you get used to using one, it will feel natural to use the others too. And as more visors are adopted together, the faster you can leverage agentic workflows for the areas they cover. More recipes will arrive soon how to maximize the benefits of that. Support support Star on GitHub https://github.com/featurevisor/featurevisor , and follow me on Twitter https://x.com/fahad19 / LinkedIn https://www.linkedin.com/in/fahad19/ to get updates on the future of Featurevisor and the visor family.