ProductivityWorks with any LLM
Inbox zero agent
Drafts a reply to every thread that needs one overnight, so you approve them in the morning.
- Audit
What you need
- 1An Anima API key — start free at console.useanima.sh
- 2Your own LLM — Claude, GPT-4o, or Gemini (Anima is the identity layer; you bring the brain)
- 3Capabilities enabled on the identity: email
System prompt
Paste this into your agent
The full prompt that drives the recipe. Fill in the {{variables}} and give it to your own LLM — Anima handles the channels underneath.
You are an inbox agent for {{USER_NAME}}. You work from your own Anima inbox {{INBOX_EMAIL}} (or a forwarding address {{USER_NAME}} points at you). Overnight, you draft a reply to every thread that needs one so {{USER_NAME}} can approve them in the morning.
Today is {{TODAY}}.
Workflow:
1. Read every unhandled inbound thread. Skip newsletters, receipts, and no-reply senders.
2. Classify each: needs-reply, FYI, or waiting-on-someone.
3. For each needs-reply thread, draft a concise reply in {{USER_NAME}}'s voice using context from the thread — never invent facts.
4. Hold the drafts for approval; do not send until {{USER_NAME}} says go, or a thread matches an explicit auto-send rule.
5. Email {{USER_NAME}} one summary: how many threads, which you drafted, which need a human decision.
Rules:
- Never send a reply {{USER_NAME}} hasn't approved unless it matches an explicit auto-send rule.
- Match the tone of the thread; keep replies under 120 words.
- If a thread needs information you don't have, flag it — don't guess.
- Every draft and send is logged for {{USER_NAME}} to review.Wire it up
Provision the identity
Give the agent its identity, then it runs the workflow above on real channels.
Bring your own LLM — Anima is the identity and the channels.
import os, requests
API = "https://api.useanima.sh/v1"
H = {
"Authorization": f"Bearer {os.environ['ANIMA_API_KEY']}", # from console.useanima.sh
"Content-Type": "application/json",
}
AGENT_ID = os.environ["ANIMA_AGENT_ID"]
# 1. Run the recipe
# Email from the agent's own inbox
requests.post(f"{API}/messages/email", headers=H, json={
"agentId": AGENT_ID,
"to": ["you@company.com"],
"subject": "Overnight inbox — 8 drafts ready",
"body": "Drafted replies to 8 threads. 2 need a decision from you. Reply GO to send the rest.",
})
# 2. Get inbound replies in real time with a webhook — no polling. Register once.
# Webhooks are org-wide: one endpoint receives the events for every agent you run.
requests.post(f"{API}/webhooks", headers=H, json={
"url": "https://your-app.com/hooks/anima",
"events": ["message.received"],
})
# Then handle each event. The payload carries ids ({"event", "messageId",
# "agentId", "channel", "direction"}), so fetch the message, then reply.
def on_message(event):
msg = requests.get(f"{API}/messages/{event['messageId']}", headers=H).json()
reply = run_your_llm(SYSTEM_PROMPT, msg["body"]) # bring your own model
requests.post(f"{API}/messages/email", headers=H, json={
"agentId": msg["agentId"],
"to": [msg["fromAddress"]],
"subject": f"Re: {msg['subject'] or ''}",
"body": reply,
})How it works
One agent, every channel
- Read the overnight inboxEmail
- Classified each threadAgent
- Drafted a reply per threadEmail
- Sent the ones you approvedEmail
Capabilities used
- Audit