CLI Examples

# 1. Create new project
runagent init chatbot-agent --framework langgraph

# 2. Navigate to project
cd chatbot-agent

# 3. Install dependencies
pip install -r requirements.txt

# 4. Set up environment
cp .env.example .env
# Edit .env with your API keys

# 5. Test locally
runagent serve .

# 6. In another terminal, test the agent
curl -X POST http://localhost:8000/invoke \
  -H "Content-Type: application/json" \
  -d '{"query": "Hello!"}'

# 7. Deploy when ready
runagent deploy . --local

# List all available templates
runagent template list

# Get details about a template
runagent template info problem-solver

# Create project from specific template
runagent init my-solver --template problem-solver

# Update template to latest version
runagent template update problem-solver

# Run with debug logging
runagent serve . --log-level debug

# Test specific entrypoint
runagent serve . --entrypoint app.process_query

# Run with environment overrides
OPENAI_API_KEY=sk-test-key runagent serve .

# Check configuration validity
runagent validate .

# Deploy with specific name
runagent deploy . --name production-chatbot

# List all deployments
runagent list

# Get deployment details
runagent status 055b73d7-6239-4a94

# Stream logs from deployment
runagent logs 055b73d7-6239-4a94 --follow

# Delete deployment
runagent delete 055b73d7-6239-4a94

# Initialize project
runagent init support-bot --framework langgraph

# Add custom dependencies
cd support-bot
echo "pandas>=2.0.0" >> requirements.txt
echo "sqlalchemy>=2.0.0" >> requirements.txt

# Test with sample data
cat > test_input.json << EOF
{
  "query": "I need help with my order #12345",
  "user_id": "user_789",
  "context": "customer_support"
}
EOF

runagent serve .
curl -X POST http://localhost:8000/invoke \
  -H "Content-Type: application/json" \
  -d @test_input.json

# Initialize CrewAI project
runagent init research-crew --framework crewai

# Deploy the crew
runagent deploy research-crew/ --local

# Run the crew with complex input
runagent run <deployment-id> --input '{
  "task": "Research AI trends in 2024",
  "agents": ["researcher", "analyst", "writer"],
  "output_format": "report"
}'

# Create batch processor
runagent init batch-processor --template data-processor

# Process multiple files
for file in data/*.json; do
  runagent run <deployment-id> \
    --input-file "$file" \
    --output "results/$(basename $file)"
done

# Or use parallel processing
ls data/*.json | parallel -j 4 \
  'runagent run <deployment-id> --input-file {} --output results/{/}'

# .github/workflows/deploy.yml
name: Deploy Agent
on:
  push:
    branches: [main]

jobs:
  deploy:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v3
      
      - name: Setup Python
        uses: actions/setup-python@v4
        with:
          python-version: '3.9'
      
      - name: Install RunAgent
        run: pip install runagent
      
      - name: Deploy
        env:
          RUNAGENT_API_KEY: ${{ secrets.RUNAGENT_API_KEY }}
        run: |
          runagent deploy . --env production

# Development environment
runagent serve . --env development

# Staging deployment
runagent deploy . --env staging --name staging-agent

# Production deployment with specific config
RUNAGENT_ENV=production runagent deploy .

# Watch logs with filtering
runagent logs <id> --follow --filter "ERROR"

# Export logs for analysis
runagent logs <id> --format json > agent.log

# Check health status
watch -n 5 'runagent status <id> --format json | jq .health'

# Simple alerting script
#!/bin/bash
while true; do
  STATUS=$(runagent status <id> --format json | jq -r .status)
  if [ "$STATUS" != "running" ]; then
    echo "Agent down! Status: $STATUS" | mail -s "Agent Alert" [email protected]
  fi
  sleep 60
done

# Check RunAgent installation
runagent doctor

# Validate project structure
runagent validate . --verbose

# Test agent imports
runagent test-import .

# Check API connectivity
runagent ping

# Force stop local agent
runagent stop <id> --force

# Clean up orphaned processes
runagent cleanup

# Reset local configuration
runagent teardown && runagent setup

# Rebuild deployment
runagent rebuild <id>

# Quick shortcuts
alias ra='runagent'
alias ras='runagent serve .'
alias rad='runagent deploy .'
alias ral='runagent logs'
alias rat='runagent template'

# Project creation function
new-agent() {
  runagent init "$1" ${@:2} && cd "$1" && code .
}

# Quick test function
test-agent() {
  curl -X POST http://localhost:8000/invoke \
    -H "Content-Type: application/json" \
    -d "$1"
}

# Pretty print JSON output
runagent list --json | jq .

# Extract specific fields
runagent status <id> --json | jq '.deployment.created_at'

# Filter deployments
runagent list --json | jq '.deployments[] | select(.status=="running")'

# Stop all local deployments
runagent list --local --json | \
  jq -r '.deployments[].id' | \
  xargs -I {} runagent stop {}

# Export all configurations
for id in $(runagent list --json | jq -r '.deployments[].id'); do
  runagent export $id > "backup/$id.json"
done