Now in Private Beta

Real-time data infrastructure for AI agents

Entrpy watches data sources and notifies your agents the instant something changes — no polling, no scraping.

Request API access Read docs

entrpy-simulator-v1.0

BTCUSD Feed

Trading Agent v1

Awaiting trigger...

Logs will print here on trigger

Workflow

Built for autonomous operations

Traditional APIs require periodic polling, consuming CPU and rate limits. Entrpy reverses the model.

Define the source topic and optional logical thresholds. Your agent registers a callback endpoint instantly.

Detect

Entrpy's high-speed poller and ingestion loop continuously monitors data feeds for any state changes.

Notify

The moment conditions are met, a signed webhook dispatch delivers structured payload directly to the agent.

Developer First

Integrate in minutes

Whether using traditional webhooks or the new Model Context Protocol (MCP) standard, Entrpy speaks your agent's language.

subscribe.sh

# Register a threshold webhook trigger for BTC price
curl -X POST "https://api.entrpy.dev/v1/subscribe" \
  -H "X-API-Key: ent_live_8f3d02b54ca0" \
  -H "Content-Type: application/json" \
  -d '{
    "topic": "coingecko.btc-usd",
    "condition_op": "<",
    "condition_value": 60000.0,
    "webhook_url": "https://agent.local/webhook"
  }'

// FastMCP native tool call from Claude Desktop
// "Watch BTCUSD, notify if it drops below 60000"
{
  "server": "entrpy-mcp",
  "tool": "subscribe_to_topic",
  "arguments": {
    "topic": "BTCUSD",
    "condition_op": "<",
    "condition_value": 60000.0,
    "webhook_url": "https://agent.local/webhook"
  }
}

Why Entrpy

Designed for the agentic era

AI agents interact with the web differently than humans. We built Entrpy from the ground up for LLM execution environments.

No polling

Prevent wasteful token execution and expensive API lookups. Agents sleep until the event trigger wakes them up.

No scraping

Skip the complexity of headless browsers and unreliable HTML parses. Receive clean, reliable, structured telemetry.

Agent-native

Fully compatible with standard LLM tools. Easily deployable as a Model Context Protocol (MCP) server on any framework.