Docker

# Set your API key
export OPENAI_API_KEY=your-api-key

# Create config for OpenAI
cat <<'EOF' > config.yaml
# yaml-language-server: $schema=https://agentgateway.dev/schema/config

llm:
  models:
  - name: "*"
    provider: openAI
    params:
      apiKey: $OPENAI_API_KEY
EOF

# Run agentgateway
docker run -v "$PWD/config.yaml:/config.yaml" -p 3000:3000 \
  -e OPENAI_API_KEY=$OPENAI_API_KEY \
  cr.agentgateway.dev/agentgateway:v0.0.0-latest-dev -f /config.yaml

# Test with a chat completion
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-4o-mini","messages":[{"role":"user","content":"Hello!"}]}'

# Set your API key
export ANTHROPIC_API_KEY=your-api-key

# Create config for Anthropic
cat <<'EOF' > config.yaml
# yaml-language-server: $schema=https://agentgateway.dev/schema/config

llm:
  models:
  - name: "*"
    provider: anthropic
    params:
      apiKey: $ANTHROPIC_API_KEY
EOF

# Run agentgateway
docker run -v "$PWD/config.yaml:/config.yaml" -p 3000:3000 \
  -e ANTHROPIC_API_KEY=$ANTHROPIC_API_KEY \
  cr.agentgateway.dev/agentgateway:v0.0.0-latest-dev -f /config.yaml

# Test with a chat completion
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"claude-sonnet-4-20250514","messages":[{"role":"user","content":"Hello!"}]}'

# Set your xAI API key
export XAI_API_KEY=your-api-key

# Create config for xAI
cat <<'EOF' > config.yaml
# yaml-language-server: $schema=https://agentgateway.dev/schema/config

llm:
  models:
  - name: "*"
    provider: openAI
    params:
      apiKey: $XAI_API_KEY
      baseUrl: "https://api.x.ai"
EOF

# Run agentgateway
docker run -v "$PWD/config.yaml:/config.yaml" -p 3000:3000 \
  -e XAI_API_KEY=$XAI_API_KEY \
  cr.agentgateway.dev/agentgateway:v0.0.0-latest-dev -f /config.yaml

# Test with a chat completion
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"grok-2-latest","messages":[{"role":"user","content":"Hello!"}]}'

# Start Ollama (if not already running)
ollama serve &

# Pull a model
ollama pull llama3.2

# Create config for Ollama
cat <<'EOF' > config.yaml
# yaml-language-server: $schema=https://agentgateway.dev/schema/config

llm:
  models:
  - name: "*"
    provider: openAI
    params:
      baseUrl: "http://host.docker.internal:11434"
EOF

# Run agentgateway (use host.docker.internal to reach Ollama on the host)
docker run -v "$PWD/config.yaml:/config.yaml" -p 3000:3000 \
  --add-host=host.docker.internal:host-gateway \
  cr.agentgateway.dev/agentgateway:v0.0.0-latest-dev -f /config.yaml

# Test with a chat completion
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"llama3.2","messages":[{"role":"user","content":"Hello!"}]}'

# Set your Azure OpenAI credentials
export AZURE_OPENAI_API_KEY=your-api-key
export AZURE_DEPLOYMENT=your-deployment-name
export AZURE_RESOURCE_NAME=your-resource-name

# Create config for Azure OpenAI
cat <<'EOF' > config.yaml
# yaml-language-server: $schema=https://agentgateway.dev/schema/config

llm:
  models:
  - name: "*"
    provider: azure
    params:
      model: $AZURE_DEPLOYMENT
      azureResourceName: $AZURE_RESOURCE_NAME
      azureResourceType: openAI
      apiKey: $AZURE_OPENAI_API_KEY
EOF

# Run agentgateway
docker run -v "$PWD/config.yaml:/config.yaml" -p 3000:3000 \
  -e AZURE_OPENAI_API_KEY=$AZURE_OPENAI_API_KEY \
  -e AZURE_DEPLOYMENT=$AZURE_DEPLOYMENT \
  -e AZURE_RESOURCE_NAME=$AZURE_RESOURCE_NAME \
  cr.agentgateway.dev/agentgateway:v0.0.0-latest-dev -f /config.yaml

# Test with a chat completion
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"gpt-4o","messages":[{"role":"user","content":"Hello!"}]}'

# Set your AWS credentials
export AWS_ACCESS_KEY_ID=your-access-key
export AWS_SECRET_ACCESS_KEY=your-secret-key
export AWS_REGION=us-east-1

# Create config for Amazon Bedrock
cat <<'EOF' > config.yaml
# yaml-language-server: $schema=https://agentgateway.dev/schema/config

llm:
  models:
  - name: "*"
    provider: bedrock
    params:
      awsRegion: $AWS_REGION
EOF

# Run agentgateway
docker run -v "$PWD/config.yaml:/config.yaml" -p 3000:3000 \
  -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
  -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
  -e AWS_REGION=$AWS_REGION \
  cr.agentgateway.dev/agentgateway:v0.0.0-latest-dev -f /config.yaml

# Test with a chat completion
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"anthropic.claude-3-5-sonnet-20241022-v2:0","messages":[{"role":"user","content":"Hello!"}]}'

# Set your API key
export GEMINI_API_KEY=your-api-key

# Create config for Google Gemini
cat <<'EOF' > config.yaml
# yaml-language-server: $schema=https://agentgateway.dev/schema/config

llm:
  models:
  - name: "*"
    provider: gemini
    params:
      apiKey: $GEMINI_API_KEY
EOF

# Run agentgateway
docker run -v "$PWD/config.yaml:/config.yaml" -p 3000:3000 \
  -e GEMINI_API_KEY=$GEMINI_API_KEY \
  cr.agentgateway.dev/agentgateway:v0.0.0-latest-dev -f /config.yaml

# Test with a chat completion
curl http://localhost:3000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model":"gemini-2.0-flash","messages":[{"role":"user","content":"Hello!"}]}'