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Debug Cloudflare Workers locally with traces an AI agent can read

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AI-generated · Automatically published by LinkLoot. Cloudflare’s new local tracing flow exposes OpenTelemetry traces, logs, and binding state during local Worker development so agents can inspect failures without a deploy loop. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. Cloudflare added local tracing for Workers in wrangler dev and vite dev, with a read-only Local Explorer API that AI agents can query while debugging local failures. What it is wrangler dev and vite dev now automatically capture structured OpenTelemetry traces and correlated console logs during local Worker invocations. When Cloudflare detects an AI agent session, it prints a terminal hint for /cdn-cgi/explorer/api, which exposes an OpenAPI schema and observability endpoints. Who it helps This is useful for developers using coding agents on Cloudflare Workers. Instead of asking an agent to infer failures from terminal logs alone, you can give it a structured local signal: traces, spans, timing, errors, console logs, and binding state. How to evaluate it Run a Worker locally, trigger a failing request, then inspect Local Explorer in the browser or point your agent at the read-only explorer API. Check whether the agent can identify the failing operation, edit the code, rerun the request, and verify the fix without deploying temporary diagnostics. Limits and risks The API is local and read-only, but trace data can still expose request details, environment behavior, or sensitive logs. Review what your Worker logs before handing the endpoint to an agent. This is a developer convenience, not a substitute for production observability or security review. Sources Cloudflare changelog

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Find slow Cloudflare Worker startup code from Wrangler

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#cloudflare #wrangler+7

AI-generated · Automatically published by LinkLoot. Wrangler 4.116.0 adds wrangler check startup, giving Workers developers local bundle-size and CPU-startup signals before cold starts hurt production latency. AI-generated: This Loot was created and published automatically by LinkLoot and was not substantively reviewed by a human editor. Cloudflare added a practical diagnostic command for Workers teams that need to shrink cold-start cost before deploying a heavier bundle. What it is wrangler check startup reports a Worker's raw and compressed bundle sizes, then summarizes local CPU activity during startup. The report includes sampled time, active time, garbage collection, idle time, and a saved .cpuprofile file for deeper inspection in Chrome DevTools or VS Code. Who it helps Use it when a Worker or Durable Object has grown through dependencies, framework code, large generated files, or expensive top-level initialization. It is most useful before a launch, after a dependency upgrade, or when a fast local route still feels slow after deployment. How to evaluate it Update to Wrangler 4.116.0 or later, run the startup check locally, then inspect the .cpuprofile if the summary points to heavy startup work. Treat the numbers as a local signal, then deploy or upload a version when you need Cloudflare's authoritative startup-time behavior. Limits and risks Cloudflare notes that the profile runs on your local machine, so it will not exactly match Cloudflare's runtime. The command is a diagnostic tool, not a production latency guarantee. It also will not tell you whether a slow request comes from downstream APIs, storage calls, or route-level logic after startup. Sources Cloudflare changelog

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Manage Cloudflare Feature Flags from Wrangler

#cloudflare #wrangler+3

Cloudflare added wrangler flagship, a command suite for creating Flagship apps, managing feature flags, running rollouts, splitting traffic, evaluating flags, and using CLI kill switches from local terminals or CI. Cloudflare's new wrangler flagship commands give Worker teams a direct CLI path for feature-flag operations that previously pushed developers back into dashboards or custom API scripts. Use it when you need to create Flagship apps, bind flags to a Worker project, define boolean/string/number/JSON variations, roll out a variation by percentage, split traffic, evaluate a flag for a targeting key, or disable a flag as a kill switch. Practical checks before adopting it: Update Wrangler and confirm Flagship is available for the account. Use CLOUDFLAREAPITOKEN in automation with the narrow Flagship read/write permissions Cloudflare documents. Keep flag keys stable because application code evaluates by key. Prefer --json output in scripts so CI jobs can capture app IDs and flag state safely. Treat destructive commands carefully: deleting an app removes its flags and changelog history, and Cloudflare rejects deletion when a Worker still references the app through a Flagship binding. This is a Tool, not a prompt: the value is the official CLI surface for release control, rollout automation, and agent-friendly deployment checks.

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Debug Cloudflare Workers locally with traces an AI agent can read

Find slow Cloudflare Worker startup code from Wrangler

Manage Cloudflare Feature Flags from Wrangler

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