Quick Start

# You need:
- Node.js 18+ installed
- A Cloudflare account (free tier works)
- 5 minutes

# That's it.

# One command to get started - launches the wizard
npx @ensemble-edge/ensemble

# Or create a Conductor project directly
npx @ensemble-edge/ensemble conductor init my-ai-workflow
cd my-ai-workflow

my-ai-workflow/
  ensembles/
    hello-world.yaml      # Your first ensemble
  components/
    prompts/
      hello.md            # Your first prompt
  wrangler.toml           # Cloudflare config
  package.json
  conductor.config.ts     # Conductor config

ensemble: company-intel
description: Analyze a company from its domain

agents:
  # Fetch company data
  - name: fetch
    operation: http
    config:
      url: https://api.company-data.com/lookup?domain=${input.domain}
      method: GET
      cache_ttl: 3600  # Cache for 1 hour

  # Analyze with AI
  - name: analyze
    operation: think
    config:
      model: claude-3-5-sonnet-20241022
      prompt: |
        Analyze this company data and provide:
        - Industry classification
        - Key products/services
        - Market position (1-5 scale)
        - Growth indicators

        Company data:
        ${fetch.output}

        Respond in JSON format.
      response_format:
        type: json_object

  # Return results
output:
  company: ${input.domain}
  analysis: ${analyze.output}
  cached: ${fetch.cached}

# .dev.vars
ANTHROPIC_API_KEY=sk-ant-...

# Via dashboard: Workers & Pages > Your Worker > Settings > Variables

ANTHROPIC_API_KEY: <your-key>

name = "my-ai-workflow"
main = "src/index.ts"
compatibility_date = "2024-11-01"

# KV for caching (optional but recommended)
[[kv_namespaces]]
binding = "CACHE"
id = "your_kv_namespace_id"

# AI Gateway for observability (optional)
[ai]
binding = "AI_GATEWAY"

wrangler kv:namespace create "CACHE"
# Copy the ID to wrangler.toml

# Start local dev server
ensemble wrangler dev --local-protocol http

# In another terminal, test it
curl http://localhost:8787/ensembles/company-intel \
  -H "Content-Type: application/json" \
  -d '{"domain": "stripe.com"}'

{
  "company": "stripe.com",
  "analysis": {
    "industry": "Financial Technology / Payments",
    "products": ["Payment processing", "Billing", "Connect"],
    "market_position": 5,
    "growth_indicators": "Strong - expanding globally"
  },
  "cached": false
}

# Deploy to Cloudflare
ensemble wrangler deploy

# Get your worker URL
# Example: https://my-ai-workflow.your-subdomain.workers.dev

curl https://my-ai-workflow.your-subdomain.workers.dev/ensembles/company-intel \
  -H "Content-Type: application/json" \
  -d '{"domain": "openai.com"}'

# Edgit is already included in the Ensemble CLI!
edgit init

# Version your prompt
edgit tag create company-analysis-prompt v1.0.0

# Tag and push to production
edgit tag set company-analysis-prompt prod v1.0.0
edgit push --tags --force

agents:
  - name: analyze
    operation: think
    component: company-analysis-prompt@v1.0.0  # Versioned!
    config:
      model: claude-3-5-sonnet-20241022

ensemble: company-intel

# Test two prompt versions simultaneously
agents:
  - name: analyze-v1
    operation: think
    component: company-analysis-prompt@v1.0.0
    config:
      model: claude-3-5-sonnet-20241022

  - name: analyze-v2
    operation: think
    component: company-analysis-prompt@v2.0.0
    config:
      model: claude-3-5-sonnet-20241022

output:
  v1_result: ${analyze-v1.output}
  v2_result: ${analyze-v2.output}
  # Compare results and pick winner

agents:
  # Database storage
  - name: store
    operation: data
    config:
      backend: d1
      binding: ANALYTICS
      operation: execute
      sql: |
        INSERT INTO companies (domain, analysis, created_at)
        VALUES (?, ?, ?)
      params:
        - ${input.domain}
        - ${analyze.output}
        - ${Date.now()}

  # Send email notification
  - name: notify
    operation: email
    config:
      to: team@company.com
      subject: "New company analyzed: ${input.domain}"
      body: ${analyze.output}

agents:
  # Web scraping with 3-tier fallback
  - name: scrape
    agent: scraper
    config:
      url: https://${input.domain}
      extract:
        title: "h1"
        description: "meta[name=description]"

  # Quality validation
  - name: validate
    agent: validator
    config:
      evaluator_type: completeness
      threshold: 0.8
    input:
      content: ${analyze.output}