AI features based on 3rd-party integrations

Local setup

Required: Install AI Gateway

Follow this instruction to install AI Gateway with GDK.

Required: Setup Google Cloud Platform in AI Gateway

To obtain a Google Cloud service key for local development, follow the steps below:

  1. Set up a Google Cloud project
    1. Option 1 (recommended for GitLab team members): request access to our existing group Google Cloud project (ai-enablement-dev-69497ba7) by using this template This project has Vertex APIs and Vertex AI Search (for Duo Chat documentation questions) already enabled.
    2. Option 2: Create a sandbox Google Cloud project by following the instructions in the handbook. If you are using an individual Google Cloud project, you may also need to enable the Vertex AI API:
      1. Visit welcome page, choose your project (for example: jdoe-5d23dpe).
      2. Go to APIs & Services > Enabled APIs & services.
      3. Select Enable APIs and Services.
      4. Search for Vertex AI API.
      5. Select Vertex AI API, then select Enable.
  2. Install the gcloud CLI
    1. If you already use asdf for runtime version management, you can install gcloud with the asdf gcloud plugin
  3. Authenticate locally with Google Cloud using the gcloud auth application-default login command.
  4. Update the application settings file in AI Gateway:
  # <GDK-root>/gitlab-ai-gateway/.env

  # PROJECT_ID = "ai-enablement-dev-69497ba7" for GitLab team members with access
  # to the group using the access request method described above

  # PROJECT_ID = "your-google-cloud-project-name" for those with their own sandbox
  # Google Cloud project.


Required: Setup Anthropic in AI Gateway

After filling out an access request, you can sign up for an Anthropic account and create an API key. Update the application settings file in AI Gateway:

# <GDK-root>/gitlab-ai-gateway/.env

Required: Setup AI Gateway endpoint in GitLab-Rails

Update following variables in the env.runit file in your GDK root:

# <GDK-root>/env.runit

By default, the above URL works as-is. You can also change it to a different URL by updating the application settings file in AI Gateway:

# <GDK-root>/gitlab-ai-gateway/.env

Required: Setup Licenses in GitLab-Rails

Follow the process to obtain an EE license for your local instance and upload the license.

  1. To verify that the license is applied go to Admin Area > Subscription and check the subscription plan.

Required: Enable feature flags in GitLab-Rails

Enable all AI-related feature flags:

rake gitlab:duo:enable_feature_flags

After the setup is complete, you can test clients in GitLab-Rails if it can correctly reach to AI Gateway:

  1. Run gdk start.
  2. Login to Rails console e.g. gdk rails console.
  3. Talk to a model:
  # Talk to Anthropic model, unit_primitive: 'duo_chat').complete(prompt: "\n\nHuman: Hi, How are you?\n\nAssistant:")

  # Talk to Vertex AI model, unit_primitive: 'documentation_search').text_embeddings(content: "How can I create an issue?")

  # Test `/v1/chat/agent` endpoint [{role: "user", content: "Hi, how are you?"}])
See this doc for registering unit primitives in cloud connector.

Optional: Create a test group in GitLab-Rails

If you are running GDK in SaaS mode (recommended), you need to enable Duo features for at least one group. To do this, run:

GITLAB_SIMULATE_SAAS=1 RAILS_ENV=development bundle exec rake 'gitlab:duo:setup[<test-group-name>]'

Replace <test-group-name> with the name of any top-level group. If the group doesn’t exist, it creates a new one. You might need to re-run the script multiple times; it prints error messages with links on how to resolve the error. Membership to a group with Duo features enabled is what enables many AI features. To enable AI feature access locally, make sure that your test user is a member of the group with Duo features enabled.

Optional: Enable logging in AI Gateway

Update the application settings file in AI Gateway:

# <GDK-root>/gitlab-ai-gateway/.env

For example, you can watch the log file with the following command.

tail -f ai-gateway.log | fblog -a prefix -a suffix -a current_file_name -a suggestion -a language -a input -a parameters -a score -a exception

Optional: Enable authentication and authorization in AI Gateway

AI Gateway has authentication and authorization flow to verify if clients have permission to access the features. This is enforced in any live environments hosted by GitLab infra team. To test this flow in your local development environment, see the following options.

In development environments (e.g. GDK), this process is disabled by default. To confirm this, set AIGW_AUTH__BYPASS_EXTERNAL to true in the application setting file (<GDK-root>/gitlab-ai-gateway/.env) in AI Gateway.

Option-1: Use your GitLab instance as a provider

Assuming that you are running the AI Gateway with GDK, apply the following configuration to GDK:

# <GDK-root>/env.runit

Update the application settings file in AI Gateway:

# <GDK-root>/gitlab-ai-gateway/.env
export AIGW_GITLAB_URL=<your-gdk-url>

and gdk restart.

Option-2: Use your customer dot instance as a provider

CustomersDot setup is helpful when you want to test or update functionality related to cloud licensing or if you are running GDK in non-SaaS mode.

Internal video tutorial

  1. Follow Instruct your local CustomersDot instance to use the GitLab application.
  2. Activate GitLab Enterprise license
    1. To test Self Managed instances, follow Cloud Activation steps using the cloud activation code you received earlier.
    2. To test SaaS, follow Activate GitLab Enterprise license with your license file.


Tips for local development

  1. When responses are taking too long to appear in the user interface, consider restarting Sidekiq by running gdk restart rails-background-jobs. If that doesn’t work, try gdk kill and then gdk start.
  2. Alternatively, bypass Sidekiq entirely and run the service synchronously. This can help with debugging errors as GraphQL errors are now available in the network inspector instead of the Sidekiq logs. To do that, temporarily alter the perform_for method in Llm::CompletionWorker class by changing perform_async to perform_inline.

Feature development (Abstraction Layer)

Feature flags

Apply the following feature flags to any AI feature work:

  • A general flag (ai_duo_chat_switch) that applies to all GitLab Duo Chat features. It’s enabled by default.
  • A general flag (ai_global_switch) that applies to all other AI features. It’s enabled by default.
  • A flag specific to that feature. The feature flag name must be different than the licensed feature name.

See the feature flag tracker epic for the list of all feature flags and how to use them.


To connect to the AI provider API using the Abstraction Layer, use an extendable GraphQL API called aiAction. The input accepts key/value pairs, where the key is the action that needs to be performed. We only allow one AI action per mutation request.

Example of a mutation:

mutation {
  aiAction(input: { summarizeComments: { resourceId: "gid://gitlab/Issue/52" } }) {

As an example, assume we want to build an “explain code” action. To do this, we extend the input with a new key, explainCode. The mutation would look like this:

mutation {
    input: {
      explainCode: { resourceId: "gid://gitlab/MergeRequest/52", code: "foo() { console.log() }" }
  ) {

The GraphQL API then uses the Anthropic Client to send the response.

How to receive a response

The API requests to AI providers are handled in a background job. We therefore do not keep the request alive and the Frontend needs to match the request to the response from the subscription.

Determining the right response to a request can cause problems when only userId and resourceId are used. For example, when two AI features use the same userId and resourceId both subscriptions will receive the response from each other. To prevent this interference, we introduced the clientSubscriptionId.

To match a response on the aiCompletionResponse subscription, you can provide a clientSubscriptionId to the aiAction mutation.

  • The clientSubscriptionId should be unique per feature and within a page to not interfere with other AI features. We recommend to use a UUID.
  • Only when the clientSubscriptionId is provided as part of the aiAction mutation, it will be used for broadcasting the aiCompletionResponse.
  • If the clientSubscriptionId is not provided, only userId and resourceId are used for the aiCompletionResponse.

As an example mutation for summarizing comments, we provide a randomId as part of the mutation:

mutation {
    input: {
      summarizeComments: { resourceId: "gid://gitlab/Issue/52" }
      clientSubscriptionId: "randomId"
  ) {

In our component, we then listen on the aiCompletionResponse using the userId, resourceId and clientSubscriptionId ("randomId"):

subscription aiCompletionResponse(
  $userId: UserID
  $resourceId: AiModelID
  $clientSubscriptionId: String
) {
    userId: $userId
    resourceId: $resourceId
    clientSubscriptionId: $clientSubscriptionId
  ) {

The subscription for chat behaves differently.

To not have many concurrent subscriptions, you should also only subscribe to the subscription once the mutation is sent by using skip().

Current abstraction layer flow

The following graph uses VertexAI as an example. You can use different providers.

flowchart TD A[GitLab frontend] -->B[AiAction GraphQL mutation] B --> C[Llm::ExecuteMethodService] C --> D[One of services, for example: Llm::GenerateSummaryService] D -->|scheduled| E[AI worker:Llm::CompletionWorker] E -->F[::Gitlab::Llm::Completions::Factory] F -->G[`::Gitlab::Llm::VertexAi::Completions::...` class using `::Gitlab::Llm::Templates::...` class] G -->|calling| H[Gitlab::Llm::VertexAi::Client] H --> |response| I[::Gitlab::Llm::GraphqlSubscriptionResponseService] I --> J[GraphqlTriggers.ai_completion_response] J --> K[::GitlabSchema.subscriptions.trigger]

How to implement a new action

Register a new method

Go to the Llm::ExecuteMethodService and add a new method with the new service class you will create.

class ExecuteMethodService < BaseService
    # ...
    amazing_new_ai_feature: Llm::AmazingNewAiFeatureService

Create a Service

  1. Create a new service under ee/app/services/llm/ and inherit it from the BaseService.
  2. The resource is the object we want to act on. It can be any object that includes the Ai::Model concern. For example it could be a Project, MergeRequest, or Issue.
# ee/app/services/llm/amazing_new_ai_feature_service.rb

module Llm
  class AmazingNewAiFeatureService < BaseService

    def perform
      ::Llm::CompletionWorker.perform_async(,,, :amazing_new_ai_feature)

    def valid?
      super && Ability.allowed?(user, :amazing_new_ai_feature, resource)

Authorization in GitLab-Rails

We recommend to use policies to deal with authorization for a feature. Currently we need to make sure to cover the following checks:

  1. For GitLab Duo Chat feature, ai_duo_chat_switch is enabled.
  2. For other general AI features, ai_global_switch is enabled.
  3. Feature specific feature flag is enabled.
  4. The namespace has the required license for the feature.
  5. User is a member of the group/project.
  6. experiment_features_enabled settings are set on the Namespace.

For our example, we need to implement the allowed?(:amazing_new_ai_feature) call. As an example, you can look at the Issue Policy for the summarize comments feature. In our example case, we want to implement the feature for Issues as well:

# ee/app/policies/ee/issue_policy.rb

module EE
  module IssuePolicy
    extend ActiveSupport::Concern
    prepended do
      with_scope :global
      condition(:ai_available) do
        ::Feature.enabled?(:ai_global_switch, type: :ops)

      with_scope :subject
      condition(:amazing_new_ai_feature_enabled) do
        ::Feature.enabled?(:amazing_new_ai_feature, subject_container) &&

      rule do
        ai_available & amazing_new_ai_feature_enabled & is_project_member
      end.enable :amazing_new_ai_feature
See this section about authentication and authorization in AI Gateway.

Pairing requests with responses

Because multiple users’ requests can be processed in parallel, when receiving responses, it can be difficult to pair a response with its original request. The requestId field can be used for this purpose, because both the request and response are assured to have the same requestId UUID.


AI requests and responses can be cached. Cached conversation is being used to display user interaction with AI features. In the current implementation, this cache is not used to skip consecutive calls to the AI service when a user repeats their requests.

query {
  aiMessages {
    nodes {

This cache is especially useful for chat functionality. For other services, caching is disabled. You can enable this for a service by using the cache_response: true option.

Caching has following limitations:

  • Messages are stored in Redis stream.
  • There is a single stream of messages per user. This means that all services currently share the same cache. If needed, this could be extended to multiple streams per user (after checking with the infrastructure team that Redis can handle the estimated amount of messages).
  • Only the last 50 messages (requests + responses) are kept.
  • Expiration time of the stream is 3 days since adding last message.
  • User can access only their own messages. There is no authorization on the caching level, and any authorization (if accessed by not current user) is expected on the service layer.

Check if feature is allowed for this resource based on namespace settings

There is one setting allowed on root namespace level that restrict the use of AI features:

  • experiment_features_enabled

To check if that feature is allowed for a given namespace, call:

Gitlab::Llm::StageCheck.available?(namespace, :name_of_the_feature)

Add the name of the feature to the Gitlab::Llm::StageCheck class. There are arrays there that differentiate between experimental and beta features.

This way we are ready for the following different cases:

  • If the feature is not in any array, the check will return true. For example, the feature was moved to GA.

To move the feature from the experimental phase to the beta phase, move the name of the feature from the EXPERIMENTAL_FEATURES array to the BETA_FEATURES array.

Implement calls to AI APIs and the prompts

The CompletionWorker will call the Completions::Factory which will initialize the Service and execute the actual call to the API. In our example, we will use VertexAI and implement two new classes:

# /ee/lib/gitlab/llm/vertex_ai/completions/amazing_new_ai_feature.rb

module Gitlab
  module Llm
    module VertexAi
      module Completions
        class AmazingNewAiFeature < Gitlab::Llm::Completions::Base
          def execute
            prompt =[:user_input]).to_prompt

            response =, unit_primitive: 'amazing_feature').text(content: prompt)

            response_modifier =

              user, nil, response_modifier, options: response_options
# /ee/lib/gitlab/llm/vertex_ai/templates/amazing_new_ai_feature.rb

module Gitlab
  module Llm
    module VertexAi
      module Templates
        class AmazingNewAiFeature
          def initialize(user_input)
            @user_input = user_input

          def to_prompt
            You are an assistant that writes code for the following context:

            context: #{user_input}

Because we support multiple AI providers, you may also use those providers for the same example:, unit_primitive: 'your_feature'), unit_primitive: 'your_feature')

Add AI Action to GraphQL




Refer to the secure coding guidelines for Artificial Intelligence (AI) features.