AI Catalog: Discover, create, and share agents and flows (opens in new tab)

The GitLab AI Catalog serves as a centralized repository designed to streamline the discovery, creation, and distribution of AI agents and automated flows across an organization. By providing a structured environment for managing foundational and custom AI assets, it fosters team collaboration and ensures consistency throughout the development lifecycle. Ultimately, the catalog enables developers to scale AI-driven automation from experimental private prototypes to production-ready, instance-wide solutions.

Discovering and Enabling AI Assets

• The catalog acts as a central hub for two distinct asset types: Agents, which handle on-demand or context-specific tasks, and Flows, which are multi-step automations that orchestrate multiple agents.
• Users can browse assets via the Explore menu, inspecting titles, descriptions, and visibility statuses before implementation.
• To utilize an asset, it must first be added to a top-level group via the "Enable in group" button and then activated within specific projects.
• The duplication feature allows teams to copy existing agents or flows to serve as templates for further customization.

Development and Configuration

• Custom agents are built by defining specialized system prompts and configuring specific tool access, such as granting read-only permissions for code and merge requests.
• Custom flows utilize a YAML-based structure to define complex behaviors, incorporating components like prompts, routers, and agent hierarchies.
• New assets are typically assigned a unique display name (e.g., ci-cd-optimizer) and initially set to private visibility to allow for safe experimentation.
• Effective creation requires thorough documentation of prerequisites, dependencies, and specific use cases to ensure the asset is maintainable by other team members.

Managing Visibility and Sharing

• Private visibility restricts access to project members with at least a Developer role or top-level group Owners, making it ideal for sensitive or team-specific workflows.
• Public visibility allows anyone on the GitLab instance to view and enable the asset in their own projects.
• Best practices for sharing include using descriptive, purpose-driven names like security-code-review rather than generic identifiers.
• Organizations are encouraged to validate and test assets privately before moving them to public status to ensure they solve real problems and handle edge cases.

Versioning and Lifecycle Management

• GitLab employs automated semantic versioning (e.g., 1.1.0) where any change to a prompt or configuration triggers an immutable version update.
• The platform uses "version pinning" to ensure stability; when an asset is enabled, projects remain on a fixed version rather than updating automatically.
• Updates are strictly opt-in, requiring users to manually review changes and click an "Update" button to adopt the latest version.
• Version history and current status can be monitored through the "About" section in the Automate menu for both agents and flows.

To maximize the benefits of the AI Catalog, organizations should establish a clear transition path from private experimentation to public sharing. By leveraging version pinning and granular tool access, teams can safely integrate powerful AI automations into their development workflows while maintaining full control over environment stability and security.