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Eligibility and permissionsTo use Vega, your role requires the Vega actions, and your project needs an AI provider. You can use:
  • The managed AI provider, which requires your organization to be eligible and to accept the AI features terms.
  • A custom AI provider, which uses your own Anthropic or OpenAI key and is available on Enterprise plans.
To learn more about permissions, read Roles and RBAC.
Investigating issues, fixing bugs, and managing feature flags across LaunchDarkly is faster with Vega, the LaunchDarkly AI-powered agent that works across the platform. Unlike general-purpose AI assistants, Vega is integrated into the LaunchDarkly feature management and observability platform and has access to your flags, environments, telemetry, and code to deliver context-aware responses. By default, Vega runs on the managed AI provider, which uses Anthropic’s Claude models. You can also connect a custom AI provider and run Vega with your own Anthropic or OpenAI key. To learn more, read Vega AI providers. When you ask Vega a question or invoke it from a specific view, it accesses relevant data to reason about what’s happening, identify causes, and take action.

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Vega capabilities

Vega has two primary capability areas:
  • Vega for auto-remediation: An AI debugging assistant embedded in observability views. Vega investigates logs, traces, errors, and alerts, summarizing what happened and identifying causes. If you connect Vega to GitHub, it can suggest or open fixes. Vega can also run automatically when alert thresholds are breached.
  • Vega for flag cleanup: Vega can identify stale feature flags and safely remove them from your codebase. Click a button on any flag, and Vega determines whether it’s safe to remove, then creates a GitHub PR with the code changes.

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Eligibility

How you get access to Vega depends on the AI provider a project uses. To learn more about the two providers, read Vega AI providers.

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Custom AI provider

With a custom AI provider, Vega runs on your own Anthropic or OpenAI key. Custom AI providers are only available on Enterprise plans. With one, you don’t need to accept the AI features terms or sign an order form. To get started, read Add a custom AI provider.

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Managed AI provider

The managed AI provider requires the AI features terms. How your organization gets access depends on your plan:
  • Foundation and other self-serve customers on a usage-based pricing plan: Vega is available by default. No further action is needed.
  • Eligible Enterprise customers: An account Admin, Owner, or member with the updateIsGeneralGenAiEnabled custom role action can accept the AI features terms and enable Vega using the following procedure.
  • All other customers: To request access, contact your account owner.
To accept the AI features terms and enable generative AI, including Vega:
  1. Log in to LaunchDarkly as an Admin, Owner, or member with the required action.
  2. Click the gear icon in the left sidebar to view Organization settings.
  3. Click Security and scroll to the “Enable generative AI use cases” section.
  4. Select the Enable generative AI use cases checkbox. By checking this box, you accept and agree to the Product-Specific Terms.
  5. Click Update.
Generative AI is now enabled. To disable generative AI, uncheck the Enable generative AI use cases checkbox and click Update. Projects that use the managed AI provider stop running Vega. Projects that use a custom AI provider aren’t affected.

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Roles and RBAC

To use Vega your role requires the Vega actions. The Admin and Owner base roles include Vega access by default. For all other roles, an administrator must explicitly add the talkToVega action to a custom role to grant Vega access to members. To learn more about custom roles, read Custom roles. When Vega accesses LaunchDarkly resources on your behalf, it respects your existing role permissions. For example, if Vega investigates a session replay or reads log data, it can only access resources that your role allows. If your role does not include the viewSession or viewLog actions, Vega cannot access that data either. You can:
  • Allow or deny Vega access for specific teams, projects, or environments.
  • Limit which users can connect or authorize GitHub integrations.
  • Configure whether users can invoke Vega in read-only or agent mode.
This helps ensure Vega can only be used by authorized developers with clearly defined permissions for both observability data and code access. To connect Vega to GitHub, read Connecting Vega to GitHub.

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Customizing Vega with repository instructions

When Vega accesses a connected GitHub repository, it reads any CLAUDE.md or AGENTS.md files in the root of the repository. These files give Vega context about your codebase, such as architecture decisions, coding conventions, important file paths, or areas to avoid. If you want Vega to produce better results when investigating issues or cleaning up flags in your repository, add a CLAUDE.md or AGENTS.md file with relevant instructions. For example:
  • Describe your project structure and key directories
  • Note any conventions for how feature flags are used in your codebase
  • Specify files or patterns that should not be modified
  • Provide context about your deployment process or environment setup
Vega uses these instructions to make more informed decisions when analyzing code and creating pull requests. To learn more, read Using CLAUDE.MD files or Agents.md.