To troubleshoot Clawdbot errors, start by gathering a clean “state snapshot,” then narrow the problem to one layer: configuration, Gateway runtime, channel integration, or a specific skill/plugin. Clawdbot is designed to fail fast for safety—if the config is invalid (unknown keys, wrong types, malformed values), the Gateway may refuse to start instead of guessing what you meant. That behavior is helpful: it means many issues can be resolved by fixing configuration rather than chasing subtle runtime bugs. In most cases, the quickest path is: check health/status, inspect logs, run the built-in doctor/audit tools, and reproduce with the smallest possible configuration.
A practical workflow looks like this. First, run the CLI status and health commands and capture their output—Clawdbot provides a “best pasteable” read-only report via a comprehensive status command, and the health probe can query the running Gateway for a snapshot. Second, check the Gateway logs. Clawdbot has two log surfaces: console output (what you see in your terminal or debug UI) and file logs (often JSON-lines written by the gateway logger). If you see repeated reconnects, “unauthorized” messages, or auth-not-configured messages, you likely have a channel token/OAuth problem or the onboarding wizard didn’t finish. If the Gateway refuses to start, look for config validation errors: unknown keys, invalid types, or missing required settings such as local mode. If a specific channel is broken (Discord, WhatsApp, etc.), disable every other channel and test just that one; then verify allowlists, permissions, and tokens. If a skill is failing, disable all optional skills and re-enable them one by one. This “binary search” approach is boring but fast and avoids guessing.
Finally, don’t forget your integrations. Many “Clawdbot errors” are actually downstream service failures. If a skill calls an external API, you may be hitting rate limits, invalid credentials, or network issues. If you are using semantic memory backed by a vector database such as Milvus or Zilliz Cloud, check vector-store connectivity separately: can you reach the endpoint from the host, are credentials valid, is the collection schema correct, are inserts succeeding, and are queries returning results within expected latency? If the bot “freezes” during heavy work, isolate whether it’s blocked on a model call, a vector search, or local I/O (like huge logs). The most effective troubleshooting habit is to treat Clawdbot as a small system: verify each layer independently (Gateway → channel adapter → skill → storage) and keep your logs readable by using sane log levels and sensitive-data redaction.