# What is the OpenClaw (Moltbot/Clawdbot) heartbeat feature? > Source: > Published: 2026-10-23 13:01:33+00:00 OpenClaw (formerly Moltbot/Clawdbot) became popular fast because it focuses on practical local automation: watch your machine, detect drift, and act before problems pile up. The heartbeat feature is central to that promise — and it's a pattern you can implement in your own agent infrastructure. A heartbeat is a periodic health and state signal. In OpenClaw, it does more than uptime pings: each tick runs a layered decision pipeline. This "cheap checks first, models only when needed" pattern is exactly what developers asked for in recent community discussions: better cost control, more predictable behavior, and fewer unnecessary LLM calls. If you are building agent infrastructure, this is the key idea: **heartbeats are control-plane primitives, not just monitoring events.** At runtime, implement the heartbeat as a loop with five stages: A practical event envelope looks like this: ``` { "agent_id": "desktop-a17", "heartbeat_id": "hb_01JX...", "ts": "2026-02-11T10:18:05Z", "probes": { "cpu_load": 0.72, "disk_free_gb": 21.4, "mail_queue_depth": 0, "service_api": { "status": 200, "latency_ms": 83 } }, "policy": { "state": "degraded", "reasons": [ "disk_free_below_warn" ] }, "escalation": { "llm_required": false, "confidence": 0.93 } } ``` The key system behaviors to enforce: Constrain every cheap check to be: Typical probe categories to implement: | Category | Example probes | |---|---| | Local runtime | process alive, memory pressure, thread count | | I/O health | disk free, inode pressure, permissions changes | | Integration health | target API status code, timeout, p95 latency | | Task health | queue lag, retry storm indicators | | Policy preconditions | valid credentials, cert expiry windows | Use a strict probe schema so downstream logic stays stable: ``` ProbeResult: name: string ok: boolean observed_at: datetime value: number|string|object|null severity_hint: info|warn|critical error: string|null ttl_ms: integer ``` `ttl_ms` matters. If data is fresh enough, skip duplicate checks during burst windows. Escalate only when deterministic logic cannot safely decide. Encode that as an explicit gate. Good escalation triggers: Bad escalation triggers: Most heartbeat pain comes from unstable transitions. Model agent health as a state machine with hysteresis: `healthy`` degraded``critical`` recovering` Define each transition with: Example: ``` php transitions: healthy->degraded: condition: disk_free_pct < 15 consecutive: 2 degraded->critical: condition: disk_free_pct < 8 consecutive: 1 degraded->healthy: condition: disk_free_pct > 20 consecutive: 3 critical->recovering: condition: remediation_applied == true recovering->healthy: condition: disk_free_pct > 20 consecutive: 2 ``` This drastically reduces noisy oscillation. If you expose heartbeat APIs, keep them explicit and idempotent where possible: `POST /v1/heartbeats` — ingest heartbeat event`GET /v1/agents/{id}/status` — latest computed state`POST /v1/heartbeats/{id}/ack` — operator acknowledgment`POST /v1/policies/simulate` — dry-run policy against a sample payload The simulate endpoint is the one teams skip and later regret: dry-running policies against sample payloads catches bad transitions before they page anyone. Community interest around sandboxing and safe agent execution is growing for good reason. Heartbeats often trigger actions, so security boundaries are non-negotiable. Minimum controls to implement: If a model is involved: In short: heartbeat detection can be flexible; heartbeat actions must be constrained. To debug heartbeat systems, instrument these metrics first: Heartbeat systems fail at boundaries: malformed payloads, replay events, and race conditions. Apidog helps you test those boundaries in one workspace. A practical flow: `ingest_valid_heartbeat_returns_200`` duplicate_idempotency_key_no_duplicate_action``critical_state_triggers_single_alert_with_cooldown`` invalid_signature_returns_401``novelty_trigger_causes_model_escalation_when_enabled` Because Apidog combines design, testing, mocking, and documentation, your API contract and behavior stay aligned as heartbeat logic evolves. If your team currently splits this across multiple tools, consolidating in Apidog cuts drift and speeds debugging. **Clock skew** — agent timestamps can drift. Accept bounded skew and store server-received time separately. **Network partitions** — heartbeats may arrive in bursts after reconnect. Use sequence numbers and reorder windows. **Backpressure storms** — if the policy engine slows down, queues can amplify lag. Apply admission control and degrade gracefully. **Silent probe failure** — "no data" is not "healthy." Encode unknown state explicitly. **Runaway remediation loops** — an action triggers the condition that triggers the same action repeatedly. Add per-action cooldown and max retry budgets. **Model drift in escalation outcomes** — keep evaluation fixtures for model-assisted decisions, and re-validate on model/version changes. The rename history caused confusion in package names, docs, and endpoint prefixes. If you maintain integrations: `event_version`) This reduces ecosystem breakage while the community converges on OpenClaw naming. A sane default for heartbeat rollout: Then tune by workload. Developer desktop agents and server agents usually need different policies. OpenClaw's heartbeat feature is valuable because it treats agent health as a disciplined control loop, not a chat-first workflow. The winning pattern is clear: That design gives you lower cost, higher predictability, and safer automation. When you implement heartbeat APIs, invest heavily in contracts, idempotency, policy simulation, and test automation. Apidog is a strong fit here because you can design OpenAPI specs, mock dependencies, run regression tests, and publish docs in one place. If you're building or integrating OpenClaw-style heartbeats now, start with strict deterministic rules and add model intelligence gradually. Reliability comes from constraints first, intelligence second.