Claude Code GitLab CI/CD

​

Why use Claude Code with GitLab?

• Instant MR creation: Describe what you need, and Claude proposes a complete MR with changes and explanation
• Automated implementation: Turn issues into working code with a single command or mention
• Project-aware: Claude follows your CLAUDE.md guidelines and existing code patterns
• Simple setup: Add one job to .gitlab-ci.yml and a masked CI/CD variable
• Enterprise-ready: Choose Claude API, Amazon Bedrock, or Google Cloud’s Agent Platform to meet data residency and procurement needs
• Secure by default: Runs in your GitLab runners with your branch protection and approvals

​

How it works

1. Event-driven orchestration: GitLab listens for your chosen triggers (for example, a comment that mentions @claude in an issue, MR, or review thread). The job collects context from the thread and repository, builds prompts from that input, and runs Claude Code.
2. Provider abstraction: Use the provider that fits your environment:
   • Claude API (SaaS)
   • Amazon Bedrock (IAM-based access, cross-region options)
   • Google Cloud’s Agent Platform (GCP-native, Workload Identity Federation)
3. Sandboxed execution: Each interaction runs in a container with strict network and filesystem rules. Claude Code enforces workspace-scoped permissions to constrain writes. Every change flows through an MR so reviewers see the diff and approvals still apply.

​

What can Claude do?

• Create and update MRs from issue descriptions or comments
• Analyze performance regressions and propose optimizations
• Implement features directly in a branch, then open an MR
• Fix bugs and regressions identified by tests or comments
• Respond to follow-up comments to iterate on requested changes

​

Setup

​

Quick setup

1. Add a masked CI/CD variable
   • Go to Settings → CI/CD → Variables
   • Add ANTHROPIC_API_KEY (masked, protected as needed)
2. Add a Claude job to .gitlab-ci.yml

stages:
  - ai

claude:
  stage: ai
  image: node:24-alpine3.21
  # Adjust rules to fit how you want to trigger the job:
  # - manual runs
  # - merge request events
  # - web/API triggers when a comment contains '@claude'
  rules:
    - if: '$CI_PIPELINE_SOURCE == "web"'
    - if: '$CI_PIPELINE_SOURCE == "merge_request_event"'
  variables:
    GIT_STRATEGY: fetch
  before_script:
    - apk update
    - apk add --no-cache git curl bash
    - curl -fsSL https://claude.ai/install.sh | bash
    # The installer places claude in ~/.local/bin, which isn't on PATH in this image
    - export PATH="$HOME/.local/bin:$PATH"
  script:
    # Optional: start a GitLab MCP server if your setup provides one
    - /bin/gitlab-mcp-server || true
    # Use AI_FLOW_* variables when invoking via web/API triggers with context payloads
    - echo "$AI_FLOW_INPUT for $AI_FLOW_CONTEXT on $AI_FLOW_EVENT"
    - >
      claude
      -p "${AI_FLOW_INPUT:-'Review this MR and implement the requested changes'}"
      --permission-mode acceptEdits
      --allowedTools "Bash Read Edit Write mcp__gitlab"
      --debug

​

Manual setup (recommended for production)

1. Configure provider access:
   • Claude API: Create and store ANTHROPIC_API_KEY as a masked CI/CD variable
   • Amazon Bedrock: Configure GitLab → AWS OIDC and create an IAM role for Amazon Bedrock
   • Google Cloud’s Agent Platform: Configure Workload Identity Federation for GitLab → GCP
2. Add project credentials for GitLab API operations:
   • Use CI_JOB_TOKEN by default, or create a Project Access Token with api scope
   • Store as GITLAB_ACCESS_TOKEN (masked) if using a PAT
3. Add the Claude job to .gitlab-ci.yml: use the Quick setup job for the Claude API, or a provider job from Configuration examples
4. (Optional) Enable mention-driven triggers:
   • Add a project webhook for “Comments (notes)” to your event listener (if you use one)
   • Have the listener call the pipeline trigger API with variables like AI_FLOW_INPUT and AI_FLOW_CONTEXT when a comment contains @claude

​

Example use cases

​

Turn issues into MRs

@claude implement this feature based on the issue description

​

Get implementation help

@claude suggest a concrete approach to cache the results of this API call

​

Fix bugs quickly

@claude fix the TypeError in the user dashboard component

​

Using with Amazon Bedrock and Google Cloud