docker-compose.yml

services:
  postgres:
    image: postgres:14-alpine
    restart: always
    # comment out if you want to externally connect DB
    # ports:
    #   - 5432:5432
    volumes:
      - ./postgres-data:/var/lib/postgresql/data
    environment:
      - PGDATA=/var/lib/postgresql/data/pgdata
      - POSTGRES_USER=skyvern
      - POSTGRES_PASSWORD=skyvern
      - POSTGRES_DB=skyvern
    healthcheck:
      test: ["CMD-SHELL", "pg_isready -U skyvern"]
      interval: 5s
      timeout: 5s
      retries: 5

  skyvern:
    image: public.ecr.aws/skyvern/skyvern:0.1.82
    restart: on-failure
    # comment out if you want to externally call skyvern API
    ports:
      - 8000:8000
      - 9222:9222 # for cdp browser forwarding
    volumes:
      - ./artifacts:/data/artifacts
      - ./videos:/data/videos
      - ./har:/data/har
      - ./log:/data/log
      - ./.streamlit:/app/.streamlit
      # Uncomment the following two lines if you want to connect to any local changes
      # - ./skyvern:/app/skyvern
      # - ./alembic:/app/alembic
    environment:
      - DATABASE_STRING=postgresql+psycopg://skyvern:skyvern@postgres:5432/skyvern
      - BROWSER_TYPE=chromium-headful
      - ENABLE_CODE_BLOCK=true
      # - BROWSER_TYPE=cdp-connect
      # Use this command to start Chrome with remote debugging:
      # "C:\Program Files\Google\Chrome\Application\chrome.exe" --remote-debugging-port=9222 --user-data-dir="C:\chrome-cdp-profile" --no-first-run --no-default-browser-check
      # /Applications/Google\ Chrome.app/Contents/MacOS/Google\ Chrome --remote-debugging-port=9222 --user-data-dir="/Users/yourusername/chrome-cdp-profile" --no-first-run --no-default-browser-check
      # - BROWSER_REMOTE_DEBUGGING_URL=http://host.docker.internal:9222/

      # =========================
      #       LLM Settings
      # =========================
      # OpenAI Support:
      # If you want to use OpenAI as your LLM provider, uncomment the following lines and fill in your OpenAI API key.
      - ENABLE_OPENAI=true
      - LLM_KEY=OPENAI_GPT4O
      - OPENAI_API_KEY=${OPENAI_API_KEY}
      #  Gemini Support:
      # Gemini is a new LLM provider that is currently in beta. You can use it by uncommenting the following lines and filling in your Gemini API key.
      - LLM_KEY=GEMINI
      - ENABLE_GEMINI=true
      - GEMINI_API_KEY=YOUR_GEMINI_KEY
      - LLM_KEY=GEMINI_2.5_PRO_PREVIEW_03_25
      # If you want to use other LLM provider, like azure and anthropic:
      # - ENABLE_ANTHROPIC=true
      # - LLM_KEY=ANTHROPIC_CLAUDE3.5_SONNET
      # - ANTHROPIC_API_KEY=<your_anthropic_key>
      # Microsoft Azure OpenAI support:
      # If you'd like to use Microsoft Azure OpenAI as your managed LLM service integration with Skyvern, use the environment variables below.
      # In your Microsoft Azure subscription, you will need to provision the OpenAI service and deploy a model, in order to utilize it.
      # 1. Login to the Azure Portal
      # 2. Create an Azure Resource Group
      # 3. Create an OpenAI resource in the Resource Group (choose a region and pricing tier)
      # 4. From the OpenAI resource's Overview page, open the "Azure AI Foundry" portal (click the "Explore Azure AI Foundry Portal" button)
      # 5. In Azure AI Foundry, click "Shared Resources" --> "Deployments"
      # 6. Click "Deploy Model" --> "Deploy Base Model" --> select a model (specify this model "Deployment Name" value for the AZURE_DEPLOYMENT variable below)
      # - ENABLE_AZURE=true
      # - LLM_KEY=AZURE_OPENAI                          # Leave this value static, don't change it
      # - AZURE_DEPLOYMENT=<your_azure_deployment>      # Use the OpenAI model "Deployment Name" that you deployed, using the steps above
      # - AZURE_API_KEY=<your_azure_api_key>            # Copy and paste Key1 or Key2 from the OpenAI resource in Azure Portal
      # - AZURE_API_BASE=<your_azure_api_base>          # Copy and paste the "Endpoint" from the OpenAI resource in Azure Portal (eg. https://xyzxyzxyz.openai.azure.com/)
      # - AZURE_API_VERSION=<your_azure_api_version>    # Specify a valid Azure OpenAI data-plane API version (eg. 2024-08-01-preview) Docs: https://learn.microsoft.com/en-us/azure/ai-services/openai/reference
      # Amazon Bedrock Support:
      # Amazon Bedrock is a managed service that enables you to invoke LLMs and bill them through your AWS account.
      # To use Amazon Bedrock as the LLM provider for Skyvern, specify the following environment variables.
      # 1. In the AWS IAM console, create a new AWS IAM User (name it whatever you want)
      # 2. Assign the "AmazonBedrockFullAccess" policy to the user
      # 3. Generate an IAM Access Key under the IAM User's Security Credentials tab
      # 4. In the Amazon Bedrock console, go to "Model Access"
      # 5. Click Modify Model Access button
      # 6. Enable "Claude 3.5 Sonnet v2" and save changes
      # - ENABLE_BEDROCK=true
      # - LLM_KEY=BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET   # This is the Claude 3.5 Sonnet "V2" model. Change to BEDROCK_ANTHROPIC_CLAUDE3.5_SONNET_V1 for the non-v2 version.
      # - AWS_REGION=us-west-2                         # Replace this with a different AWS region, if you desire
      # - AWS_ACCESS_KEY_ID=FILL_ME_IN_PLEASE
      # - AWS_SECRET_ACCESS_KEY=FILL_ME_IN_PLEASE
      # Ollama Support:
      # Ollama is a local LLM provider that can be used to run models locally on your machine.
      # - LLM_KEY=OLLAMA
      # - ENABLE_OLLAMA=true
      # - OLLAMA_MODEL=qwen2.5:7b-instruct
      # - OLLAMA_SERVER_URL=http://host.docker.internal:11434
      # Open Router Support:
      # - ENABLE_OPENROUTER=true
      # - LLM_KEY=OPENROUTER
      # - OPENROUTER_API_KEY=<your_openrouter_api_key>
      # - OPENROUTER_MODEL=mistralai/mistral-small-3.1-24b-instruct
      # Groq Support:
      # - ENABLE_GROQ=true
      # - LLM_KEY=GROQ
      # - GROQ_API_KEY=<your_groq_api_key>
      # - GROQ_MODEL=llama-3.1-8b-instant

      # Maximum tokens to use: (only set for OpenRouter aand Ollama)
      # - LLM_CONFIG_MAX_TOKENS=128000

      # Bitwarden Settings
      # If you are looking to integrate Skyvern with a password manager (eg Bitwarden), you can use the following environment variables.
      # - BITWARDEN_SERVER=http://localhost  # OPTIONAL IF YOU ARE SELF HOSTING BITWARDEN
      # - BITWARDEN_SERVER_PORT=8002 # OPTIONAL IF YOU ARE SELF HOSTING BITWARDEN
      # - BITWARDEN_CLIENT_ID=FILL_ME_IN_PLEASE
      # - BITWARDEN_CLIENT_SECRET=FILL_ME_IN_PLEASE
      # - BITWARDEN_MASTER_PASSWORD=FILL_ME_IN_PLEASE
      
    depends_on:
      postgres:
        condition: service_healthy
    healthcheck:
      test: ["CMD", "test", "-f", "/app/.streamlit/secrets.toml"]
      interval: 5s
      timeout: 5s
      retries: 5

  skyvern-ui:
    image: public.ecr.aws/skyvern/skyvern:0.1.82
    restart: on-failure
    ports:
      - 8080:8080
      - 9090:9090
    volumes:
      - ./artifacts:/data/artifacts
      - ./videos:/data/videos
      - ./har:/data/har
      - ./.streamlit:/app/.streamlit
    environment:
      - VITE_ENABLE_CODE_BLOCK=true
    # if you want to run skyvern on a remote server,
    # you need to change the host in VITE_WSS_BASE_URL and VITE_API_BASE_URL to match your server ip
    # If you're self-hosting this behind a dns, you'll want to set:
    #   A route for the API: api.yourdomain.com -> localhost:8000
    #   A route for the UI: yourdomain.com -> localhost:8080
    #   A route for the artifact API: artifact.yourdomain.com -> localhost:9090 (maybe not needed)
      - VITE_WSS_BASE_URL=ws://localhost:8000/api/v1
    #   - VITE_ARTIFACT_API_BASE_URL=http://localhost:9090
    #   - VITE_API_BASE_URL=http://localhost:8000/api/v1
    #   - VITE_SKYVERN_API_KEY=<get this from "settings" in the Skyvern UI>
    depends_on:
      skyvern:
        condition: service_healthy