A ProAgentStore agent that receives inbound leads via webhook, scores them with Workers AI (hot / warm / cold), stores them in a Durable Object, and fires outbound notifications for hot leads.
| Method | Path | Description |
|---|---|---|
| POST | /webhook/ingest | Receive a new lead (form submission) |
| GET | /leads | List all leads — supports ?status= and ?score= filters |
| GET | /leads/:id | Get a single lead with AI notes |
| PUT | /leads/:id/status | Manually update lead status |
| GET | /stats | Aggregate counts and conversion rate |
{
"name": "Jane Smith",
"email": "jane@acme.com",
"company": "Acme Corp",
"message": "We are evaluating CRM tools for our enterprise team — budget approved.",
"phone": "+1-555-0100",
"role": "VP of Sales"
}name and email are required. All other fields are optional and are stored as-is. Extra fields (phone, role, budget, etc.) are passed to the AI for scoring context.
The response includes the assigned score:
{
"id": "uuid",
"score": "hot",
"scoreValue": 88,
"status": "new"
}new → contacted → qualified → converted
or
new → disqualified
Update via PUT /leads/:id/status with body { "status": "contacted" }.
Set these via wrangler secret put and mirror in Doppler (pags project):
| Secret | Required | Description |
|---|---|---|
WEBHOOK_SECRET | No | If set, callers must send X-Webhook-Secret: <value> |
NOTIFY_WEBHOOK | No | URL to POST hot lead events to (Slack, n8n, Zapier) |
pnpm install
pnpm devTest the webhook locally:
curl -X POST http://localhost:8787/webhook/ingest \
-H "Content-Type: application/json" \
-d '{"name":"Jane Smith","email":"jane@acme.com","company":"Acme Corp","message":"Ready to buy, budget approved, need enterprise plan."}'pnpm deploy
# or push to main — GitHub Actions auto-deploysAfter first deploy, set secrets:
wrangler secret put WEBHOOK_SECRET
wrangler secret put NOTIFY_WEBHOOKWhen a lead scores hot, the agent POSTs to NOTIFY_WEBHOOK:
{
"event": "hot_lead",
"lead": {
"id": "uuid",
"name": "Jane Smith",
"email": "jane@acme.com",
"company": "Acme Corp",
"message": "...",
"scoreValue": 88,
"notes": "Enterprise company with budget approved. Decision maker (VP) ready to buy immediately.",
"createdAt": "2026-06-06T12:00:00.000Z"
}
}Uses @cf/meta/llama-3.3-70b-instruct-fp8-fast via Workers AI. The agent asks the model to output a score (0–100) and classification (hot/warm/cold) as JSON, with 2–3 sentence reasoning. If AI is unavailable, falls back to keyword heuristics.
Thresholds: 70+ = hot, 40–69 = warm, 0–39 = cold.