Know when your agents fail, overspend, or go silent. Heartbeat monitoring, cost tracking, and failure detection — in 3 lines of code.
Three primitives. Full visibility into your AI agent fleet.
Your agents ping AgentBeat on every run. If they go silent, you get alerted.
Track LLM spend per agent, per run. Set budgets. Get alerts before you blow them.
3 failures in 5 runs? AgentBeat catches it and alerts you via Telegram, Slack, email, or webhook.
Use the Python SDK or the HTTP API. Your agents start reporting in under 5 minutes.
from agentbeat import AgentBeat
ab = AgentBeat(
"https://api.agentbeat.dev",
"my-agent",
"token"
)
with ab.run() as ctx:
ctx.items_processed = 50
ctx.add_cost(0.12)
ctx.model = "gpt-4o"# Start a run
curl -X POST https://api.agentbeat.dev/a/my-agent/start \
-H "X-Agent-Token: your-token"
# Complete with metadata
curl -X POST https://api.agentbeat.dev/a/my-agent/complete \
-H "X-Agent-Token: your-token" \
-H "Content-Type: application/json" \
-d '{"items_processed": 42, "cost_usd": 0.12}'Built for engineers who ship AI agents to production and need to sleep at night.
Your agents ping AgentBeat on every run. If they go silent, you get alerted instantly.
Track LLM spend per agent, per run. Set budgets and get alerts before you blow them.
Track individual steps within a run. See exactly where your multi-step agent pipeline broke.
3 failures in 5 runs? AgentBeat catches it and opens an incident automatically.
Get notified via Email, Telegram, Slack, or Webhook. Never miss a critical failure.
First-class Python SDK with context managers. Or use the simple HTTP API from any language.
Start free. Scale when you need to.
For side projects and solo developers.
For teams running production agents.
For organizations with critical workloads.