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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesYes. Cloudflare documents an ASGI adapter for Python Workers, including a FastAPI integration using workers.asgi.entrypoint(app). That makes Workers a supported option for ASGI services, but not a drop-in replacement for a conventional long-running ASGI server: Python runs through Pyodide in a V8 isolate, and framework compatibility, bindings, runtime versions, platform limits, and workload testing all matter before production.
How an ASGI app fits the Workers runtime
Python Workers run Python through Pyodide, a CPython implementation compiled to WebAssembly, inside a V8 isolate. The ASGI adapter connects an ASGI application to the Worker request lifecycle; it does not turn the Worker into a persistent server process that you manage like a conventional ASGI deployment. Cloudflare’s runtime documentation describes the execution and initialization model in How Python Workers Work.
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At deployment, Cloudflare uploads the application code and packages, validates the code, runs the entrypoint and top-level imports, and snapshots WebAssembly memory. The deployed snapshot is then used to reduce initialization work at request time. This is an initialization strategy, not a latency guarantee: the documentation does not establish how quickly a particular service will start or respond under its real traffic and dependency mix.
The practical consequence is that import-time code is part of your deployment design. Review module-level initialization and side effects rather than assuming they happen only when an individual request arrives. Work that can safely be completed during deployment may fit the snapshot model; anything that depends on request-specific data or runtime bindings must be handled in the appropriate request context.
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Build a small FastAPI Worker
Cloudflare’s FastAPI guide shows the ASGI adapter pattern and a Python Worker entrypoint. A minimal application shape is:
from fastapi import FastAPI
from workers import asgi
app = FastAPI()
@app.get("/")
async def root():
return {"status": "ok"}
Default = asgi.entrypoint(app)The important integration is Default = asgi.entrypoint(app): it exposes the ASGI application as the Worker entrypoint. Follow the current project layout and dependency declarations in Cloudflare’s FastAPI guide; the documented sample declares FastAPI as an application dependency and workers-py plus workers-runtime-sdk as development dependencies.
Cloudflare’s Python overview describes uv and Node as setup prerequisites. Its initialization and deployment flow uses the Python Wrangler tooling:
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- Initialize a project with
uvx --from workers-py pywrangler init, then follow the generated project prompts and files. - Check the Wrangler configuration for the Python entry file, a compatibility date, and the
python_workerscompatibility flag. Cloudflare states that the flag is required. - Run locally with
uv run pywrangler devand send a request to the local address reported by the development server. The FastAPI guide’s sample request is a smoke check that confirms basic routing, not a production validation suite. - Deploy with
uv run pywrangler deployafter reviewing the runtime date, dependencies, secrets, bindings, and limits applicable to the service.
See the current setup and deployment steps in Write Cloudflare Workers in Python and the framework-specific details in the FastAPI guide.
Choose a framework adapter that matches the application
FastAPI and other ASGI applications
FastAPI has an explicit Cloudflare guide using the ASGI entrypoint adapter. For another ASGI framework, verify its dependencies and runtime behavior rather than assuming that every package or server feature available on a standard CPython host will work unchanged in Pyodide. The platform supports the ASGI integration path; that alone does not establish compatibility for every application dependency.
Django
Cloudflare documents Django with both ASGI and WSGI adapters. For ASGI, the guide uses get_asgi_application() and exposes the result through Default = asgi.entrypoint(app). Python Workers is optimized for ASGI, while WSGI compatibility is retained for Django. The Django documentation also describes accessing bindings through scope["env"] and Django backends for D1 and Durable Objects via the django-cf package. See the official Django guide for the supported configuration details.
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Flask and other WSGI frameworks
Cloudflare’s September 2, 2026 changelog announcement says Python Workers can use web frameworks following WSGI or ASGI specifications, including Django and Flask. Treat that as framework-interface support, not a guarantee that every extension, native dependency, or deployment assumption will work. Validate the specific application and libraries you intend to ship. The announcement is at Python Workers now support WSGI web frameworks like Django and Flask.
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Plan compatibility dates and Python support
A compatibility date is a runtime control, not inert configuration. Cloudflare uses compatibility flags enabled after specified dates to gate new Python and Pyodide versions. Its runtime guide describes a five-year support window for Python releases; after that, security patches stop. Existing applications outside that window continue under Cloudflare’s stated runtime policy, but Cloudflare does not recommend those versions for new projects and does not guarantee against degraded latency or CPU time.
- Record the configured compatibility date and the runtime version it selects.
- Review the date and supported Python release when updating dependencies or changing runtime behavior.
- Do not move a production service to a new compatibility date without running your application’s tests and representative workload checks against it.
Cloudflare explains the runtime version gates and support policy in How Python Workers Work. The applicable date and supported release can change, so check the current documentation when planning an upgrade.
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Design state and service access around bindings
Workers expose platform capabilities to applications through bindings. The Python overview lists integration categories including KV, D1, Durable Objects, environment variables and secrets, service bindings, Workers AI, Vectorize, R2, Durable Workflows, and Queues. Choose among them from the service’s actual persistence, consistency, coordination, and asynchronous-work needs; the availability of a binding does not by itself determine the right database or architecture.
- Use a storage or database integration that matches the data model and consistency requirements, rather than treating every binding as interchangeable.
- Consider Durable Objects when the service needs coordinated state, and queues or workflows when work should be handled asynchronously.
- Use service bindings for Worker-to-Worker integration where that fits the service boundary.
- Keep credentials out of source control. For Django, Cloudflare’s guide demonstrates reading a secret from
workers.envand setting it withuv run pywrangler secret put DJANGO_SECRET_KEY.
For binding setup and the platform integration inventory, consult Cloudflare’s Python Workers overview and the relevant service documentation linked from it.
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Limits can affect routing, development workflows, and observability even when the ASGI application itself is compatible. The following examples are listed on Cloudflare’s limits page, checked October 7, 2026; they are not a complete checklist and may depend on the applicable plan or feature.
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| Limit example | Published value | Why it matters |
|---|---|---|
| Routes per zone | 1,000 | Relevant when a zone has many Worker routes. |
Routes per zone with wrangler dev --remote |
50 | Can constrain remote development setups that rely on many routes. |
| Log data per request | 256 KB | Large request-associated log output can hit a logging limit. |
Review the live Workers limits documentation for the current values, your plan, and feature-specific constraints before launch. These figures describe platform limits, not ASGI performance.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Decide whether Workers fits this microservice
Workers is a reasonable candidate when the application can run within the Python Workers runtime, can use bindings for the services it needs, and benefits from the Worker deployment model. It is a weaker assumption when the service depends on unverified packages, host-specific behavior, a conventional persistent process, or operational characteristics you have not measured. Framework support answers whether there is an integration path; it does not answer whether that path meets a particular service-level objective.
Compare the actual deployment choices on the dimensions that affect your service:
- Runtime: Workers uses Pyodide/WebAssembly in a V8 isolate, with compatibility-date version gates. Confirm that dependencies and application behavior fit that runtime.
- Framework integration: FastAPI has a documented ASGI adapter; Django has documented ASGI and WSGI paths. Confirm support for the framework version and all material dependencies you use.
- State and service access: Identify the required persistence, coordination, and service-to-service patterns, then verify the relevant bindings and their behavior for the application.
- Operations: Check current routing, logging, and other limits, as well as any plan-specific constraints that apply to the deployment.
- Measured fit: Test representative traffic, dependency behavior, initialization, and external calls. No comparative performance benchmark or workload-specific cost comparison is established by the cited platform documentation.
Validate the production workload, not just the quick start
A successful local request verifies that the app starts and a route responds; it does not establish production readiness. Before launch, validate the service under conditions that reflect its real dependencies and operating requirements.
- Exercise application paths: Test normal responses, validation failures, error handling, and any framework middleware or lifecycle behavior the service relies on.
- Verify dependencies: Confirm that each package and any nonstandard runtime assumption works in Python Workers, not only in a local conventional Python environment.
- Check initialization: Review imports and startup work in the deployment flow, and test behavior after deployment rather than inferring latency from the snapshot mechanism.
- Test bindings and failures: Verify authentication, permissions, expected data behavior, timeouts, and recovery for each storage, service, or queue integration.
- Run representative load and failure tests: Use the service’s expected request mix, payloads, external calls, concurrency, and failure cases. Measure the outcomes against your own operational targets; no platform-wide benchmark can substitute for this.
- Recheck platform constraints: Review the current compatibility, limits, pricing, and observability documentation for the account and features being deployed.
Cloudflare’s Python Workers overview, runtime guide, and limits page are the appropriate starting points for checking current platform details.
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