CI/CD Integration

Overview

Automate your LangGraph Platform deployments with continuous integration and deployment pipelines. Deploy on every commit, with automated testing and rollback capabilities.

Automated Deployments: Push to main → CI/CD tests → Auto-deploy to production

GitHub Actions

Quick Setup

Add LangSmith API Key

Go to GitHub repo → Settings → Secrets → New repository secretName: LANGCHAIN_API_KEY Value: Your LangSmith API key from smith.langchain.com/settings

Create Workflow File

mkdir -p .github/workflows
cp .github/workflows/deploy-langgraph-platform.yml .github/workflows/

Push to GitHub

git add .github/workflows/deploy-langgraph-platform.yml
git commit -m "Add LangGraph Platform CI/CD"
git push

Full Workflow

.github/workflows/deploy-langgraph-platform.yml

name: Deploy to LangGraph Platform

on:
  push:
    branches: [main]
  pull_request:
    branches: [main]
  workflow_dispatch:

jobs:
  test:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'

      - name: Install dependencies
        run: |
          uv sync

      - name: Run tests
        run: pytest tests/ -v

  deploy-staging:
    needs: test
    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    environment: staging
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'

      - name: Install LangGraph CLI
        run: uv tool install langgraph-cli

      - name: Deploy to Staging
        env:
          LANGCHAIN_API_KEY: $&#123;&#123; secrets.LANGCHAIN_API_KEY &#125;&#125;
        run: |
          langgraph deploy staging-mcp-server-langgraph --tag staging

      - name: Smoke Test Staging
        run: |
          langgraph deployment invoke staging-mcp-server-langgraph \
            --input '{"messages": [{"role": "user", "content": "test"}]}'

  deploy-production:
    needs: deploy-staging
    if: github.event_name == 'push' && github.ref == 'refs/heads/main'
    runs-on: ubuntu-latest
    environment: production
    steps:
      - uses: actions/checkout@v4

      - name: Set up Python
        uses: actions/setup-python@v5
        with:
          python-version: '3.11'

      - name: Install LangGraph CLI
        run: uv tool install langgraph-cli

      - name: Deploy to Production
        env:
          LANGCHAIN_API_KEY: $&#123;&#123; secrets.LANGCHAIN_API_KEY &#125;&#125;
        run: |
          langgraph deploy mcp-server-langgraph-prod \
            --tag production \
            --tag "$(git rev-parse --short HEAD)"

      - name: Verify Deployment
        run: |
          langgraph deployment get mcp-server-langgraph-prod

      - name: Smoke Test Production
        run: |
          langgraph deployment invoke mcp-server-langgraph-prod \
            --input '{"messages": [{"role": "user", "content": "Hello"}]}'

Environment Protection Rules

Set up deployment approvals in GitHub:

Go to Repository Settings

Settings → Environments → New environment

Create 'production' Environment

Name: production
Enable “Required reviewers”
Add reviewers (team leads)
Set deployment branch: main only

Create 'staging' Environment

Name: staging
No approvals needed
Auto-deploy on push to main

GitLab CI/CD

.gitlab-ci.yml

stages:
  - test
  - deploy-staging
  - deploy-production

variables:
  PYTHON_VERSION: "3.11"

test:
  stage: test
  image: python:${PYTHON_VERSION}
  script:
    - curl -LsSf https://astral.sh/uv/install.sh | sh
    - uv sync
    - uv run pytest tests/ -v

deploy-staging:
  stage: deploy-staging
  image: python:${PYTHON_VERSION}
  environment:
    name: staging
    url: https://staging-mcp-server-langgraph.langchain.com
  script:
    - curl -LsSf https://astral.sh/uv/install.sh | sh
    - uv tool install langgraph-cli
    - langgraph deploy staging-mcp-server-langgraph --tag staging
    - |
      langgraph deployment invoke staging-mcp-server-langgraph \
        --input '{"messages": [{"role": "user", "content": "test"}]}'
  only:
    - main

deploy-production:
  stage: deploy-production
  image: python:${PYTHON_VERSION}
  environment:
    name: production
    url: https://mcp-server-langgraph-prod.langchain.com
  when: manual
  script:
    - curl -LsSf https://astral.sh/uv/install.sh | sh
    - uv tool install langgraph-cli
    - langgraph deploy mcp-server-langgraph-prod --tag production --tag "$CI_COMMIT_SHORT_SHA"
    - langgraph deployment get mcp-server-langgraph-prod
  only:
    - main

CircleCI

.circleci/config.yml

version: 2.1

orbs:
  python: circleci/python@2.1.1

jobs:
  test:
    docker:
      - image: cimg/python:3.12
    steps:
      - checkout
      - python/install-packages:
          pkg-manager: pip
      - run:
          name: Run tests
          command: pytest tests/ -v

  deploy-staging:
    docker:
      - image: cimg/python:3.12
    steps:
      - checkout
      - run:
          name: Install LangGraph CLI
          command: uv tool install langgraph-cli
      - run:
          name: Deploy to Staging
          command: |
            langgraph deploy staging-mcp-server-langgraph --tag staging
      - run:
          name: Smoke Test
          command: |
            langgraph deployment invoke staging-mcp-server-langgraph \
              --input '{"messages": [{"role": "user", "content": "test"}]}'

  deploy-production:
    docker:
      - image: cimg/python:3.12
    steps:
      - checkout
      - run:
          name: Install LangGraph CLI
          command: uv tool install langgraph-cli
      - run:
          name: Deploy to Production
          command: |
            langgraph deploy mcp-server-langgraph-prod \
              --tag production \
              --tag "${CIRCLE_SHA1:0:7}"

workflows:
  test-and-deploy:
    jobs:
      - test
      - deploy-staging:
          requires:
            - test
          filters:
            branches:
              only: main
      - hold-production:
          type: approval
          requires:
            - deploy-staging
      - deploy-production:
          requires:
            - hold-production

Best Practices

Always Test Before Deploy

jobs:
  test:
    # Run unit tests, integration tests

  deploy:
    needs: test  # Only deploy if tests pass

Use Staging Environment

deploy-staging:
  # Deploy to staging first
  # Run smoke tests

deploy-production:
  needs: deploy-staging  # Only deploy to prod if staging succeeds

Tag Deployments

# Tag with git commit SHA
langgraph deploy my-agent-prod --tag "$(git rev-parse --short HEAD)"

# Tag with semantic version
langgraph deploy my-agent-prod --tag "v1.2.3"

# Tag with timestamp
langgraph deploy my-agent-prod --tag "deploy-$(date +%Y%m%d-%H%M%S)"

Require Approvals for Production

Use GitHub Environments or GitLab’s manual approval:

deploy-production:
  environment: production  # Requires approval
  when: manual

Run Smoke Tests

# After deployment, verify it works
langgraph deployment invoke my-agent-prod \
  --input '{"messages": [{"role": "user", "content": "health check"}]}' \
  --timeout 30

Notify on Deployment

    - name: Notify Slack
      if: success()
      uses: slackapi/slack-github-action@v1
      with:
        payload: |
          {
            "text": "✅ Deployed to production: $&#123;&#123; github.sha &#125;&#125;"
          }
      env:
        SLACK_WEBHOOK_URL: $&#123;&#123; secrets.SLACK_WEBHOOK &#125;&#125;

Rollback Strategy

Automatic Rollback on Failure

- name: Deploy to Production
  id: deploy
  run: langgraph deploy my-agent-prod --tag production

- name: Smoke Test
  id: test
  run: |
    langgraph deployment invoke my-agent-prod \
      --input '{"messages": [{"role": "user", "content": "test"}]}'

- name: Rollback on Failure
  if: failure() && steps.deploy.outcome == 'success'
  run: |
    # Get previous revision
    PREV_REVISION=$(langgraph deployment list | grep my-agent-prod | awk 'NR==2 {print $2}')
    # Rollback
    langgraph deployment rollback my-agent-prod --revision $PREV_REVISION

Manual Rollback

## List revisions
langgraph deployment list my-agent-prod

## Rollback to specific revision
langgraph deployment rollback my-agent-prod --revision 42

## Or rollback to previous
langgraph deployment rollback my-agent-prod

Multi-Environment Strategy

Development
Staging
Production

Trigger: Every push to develop branch

NOTE: Automatic deploys only run when the repository variable ENABLE_DEV_AUTODEPLOY is set to true. This keeps CI from waiting on environment approvals by default.

deploy-dev:
  if: github.ref == 'refs/heads/develop'
  run: langgraph deploy my-agent-dev --tag development

Monitoring Deployments

View deployment status:

## Get deployment info
langgraph deployment get my-agent-prod

## Stream logs
langgraph deployment logs my-agent-prod --follow

## View in LangSmith
## https://smith.langchain.com → Select project → View traces

Troubleshooting

Authentication failed

Symptom: 401 UnauthorizedSolution: Verify LANGCHAIN_API_KEY secret is set correctly in CI/CD platform.

Deployment timeout

Symptom: Deployment takes too longSolution:

Optimize dependencies in langgraph/requirements.txt
Reduce Docker image size
Check network connectivity

Smoke test fails

Symptom: Test invocation returns errorSolution:

Check deployment logs: langgraph deployment logs
Verify secrets are set
Test locally first with langgraph dev

Next Steps

Quickstart

Deploy your first agent

Monitoring

Monitor deployments

Secrets

Manage secrets in CI/CD

Configuration

Configure deployments

CI/CD Ready! Your LangGraph Platform deployments are now automated with testing, staging, and production workflows.

Getting Started

Deployment Options

LangGraph Platform

Kubernetes - GKE

Kubernetes - EKS & AKS

Kubernetes - Best Practices

Infrastructure as Code

Monitoring & Observability

Advanced Deployment

Configuration

Operations

Overview

GitHub Actions

Quick Setup

Full Workflow

Environment Protection Rules

GitLab CI/CD

CircleCI

Best Practices

Rollback Strategy

Automatic Rollback on Failure

Manual Rollback

Multi-Environment Strategy

Monitoring Deployments

Troubleshooting

Next Steps

Quickstart

Monitoring

Secrets

Configuration

Getting Started

Deployment Options

LangGraph Platform

Kubernetes - GKE

Kubernetes - EKS & AKS

Kubernetes - Best Practices

Infrastructure as Code

Monitoring & Observability

Advanced Deployment

Configuration

Operations

​Overview

​GitHub Actions

​Quick Setup

​Full Workflow

​Environment Protection Rules

​GitLab CI/CD

​CircleCI

​Best Practices

​Rollback Strategy

​Automatic Rollback on Failure

​Manual Rollback

​Multi-Environment Strategy

​Monitoring Deployments

​Troubleshooting

​Next Steps

Quickstart

Monitoring

Secrets

Configuration

Overview

GitHub Actions

Quick Setup

Full Workflow

Environment Protection Rules

GitLab CI/CD

CircleCI

Best Practices

Rollback Strategy

Automatic Rollback on Failure

Manual Rollback

Multi-Environment Strategy

Monitoring Deployments

Troubleshooting

Next Steps