routeflow 0.3.2

Real-time execution flow visualization for FastAPI applications.

FastAPI documents the contract of an API — routes, params, response shapes — but says nothing about what actually happens when a request runs. As soon as an app grows past a few endpoints, execution logic spreads across services, middleware, dependencies, and background tasks, and debugging means mentally reconstructing a flow that should have been visible in the first place.

routeflow makes that flow visible: mark a function with @track, and every request that calls it gets traced — order, timing, logs, and errors — and rendered live as an interactive node graph in a tab next to your Swagger docs.

Status: functional for local development — the tracer, middleware, in-memory store, and flow-view UI all work end to end.

Quickstart

pip install routeflow

from fastapi import FastAPI
from routeflow import RouteFlow
from routeflow.tracing import track

app = FastAPI()
RouteFlow(app)


@track
def charge_card(amount: int) -> None:
    ...  # call out to whatever actually charges the card


@app.post("/orders")
def create_order(amount: int):
    charge_card(amount)
    return {"amount": amount, "status": "ok"}

Run it — uvicorn myapp:app, or uvicorn[standard] / pip install websockets if you only have plain uvicorn; the flow view's live updates need a WebSocket implementation that plain uvicorn doesn't include, and silently can't connect without one — hit POST /orders, then open:

http://127.0.0.1:8000/flow/

That's the flow view — pick an endpoint in the sidebar, pick a trace, and you'll see the request's actual call tree: which functions ran, in what order, how long each took, and — if something raised — exactly where.

What @track gives you

• Nested spans, for free. Call another @track-decorated function from inside one, and the call tree builds itself via contextvars — no manual parent/child wiring.
• Sync and async. Works on both def and async def the same way.
• Arguments, captured and redactable. Call args are recorded on the span by name; pass redact= to mask specific ones (a password, a token) or capture_args=False to skip a function entirely.
• Errors, observed not altered. An exception is recorded on the span and then always re-raised unchanged — RouteFlow never changes what your code does, only what you can see about it.

Hand-adding @track to a whole existing codebase is real boilerplate — track_module covers that without becoming a blanket auto-trace-everything switch (which would undermine the redaction story above: nobody reviewed those functions for "does this take a password"):

from routeflow.tracing import track_module
import myapp.services.orders as orders

track_module(orders, exclude={"_internal_helper"})

Only wraps functions actually defined in that module (not ones merely imported into it), skips anything already @track-ed, and is still a deliberate, reviewable call site — a git diff shows exactly which module opted in.

The flow view

• Sidebar lists every endpoint that's been hit, with request count, p95 latency, and error rate.
• Pick an endpoint to see its recent traces; pick a trace to see its node graph — timing and status on every node, an error highlighted exactly where it happened.
• Updates live over a WebSocket as new requests come in — no refresh needed.
• Sits in its own tab next to your existing Swagger /docs, light/dark themed.

Turning it off

RouteFlow is on by default — it's a dev tool, and "add one line, it just works" is the point. But traces can include captured arguments and full stack traces, so it must never ship to production silently:

ROUTEFLOW_ENABLED=0

set in the environment disables it completely — no middleware installed, no route mounted, your app handed back untouched. RouteFlow(app, enabled=False) does the same from code, e.g. enabled=settings.debug.

No authentication — know this before running it

The /__routeflow__ API and the /flow UI have no authentication at all. Anyone who can reach the port your app is running on can read every captured trace — including any unredacted arguments. This is a deliberate trade-off for a local, dev-only tool (the same one Django's Debug Toolbar and Werkzeug's debugger make), not an oversight, but it's worth being explicit about rather than something you only discover by reading server.py:

• It's why redact=/mask()/capture_args=False (above) matter as much as they do — there's no auth gate standing behind them.
• Never expose the port RouteFlow (or your app) is running on to an untrusted network. ROUTEFLOW_ENABLED=0 in production is the real boundary, not "nobody will guess the URL."

How much history it keeps

Every request is traced — there's no sampling yet. max_traces controls how many finished traces stay in memory at once (default 500):

RouteFlow(app, max_traces=200)

It's a ring buffer, not a hard cutoff: once full, the oldest trace is dropped as each new one lands, so the flow view always shows your most recent activity rather than erroring out or growing without bound on a long-running dev server.

Try it

examples/demo_app.py is a small shop app with a nested call tree and one request that fails on purpose, so there's something worth looking at the first time you open the flow view:

uv run --with fastapi --with "uvicorn[standard]" python examples/demo_app.py

For something closer to a real production call tree — service/repository/ gateway/client layers, concurrent asyncio.gather fan-out, argument redaction, a deterministic failure and a flaky one — see examples/production_demo.py (same run command, different filename).

CLI

No daemon, no config file — every command that talks to a running app takes an explicit --url (default http://127.0.0.1:8000):

routeflow doctor                                # check your local env
routeflow open --url http://127.0.0.1:8000       # open the flow view
routeflow traces --url http://127.0.0.1:8000     # list recent traces
routeflow export --url http://127.0.0.1:8000 --out traces.json  # save them

routeflow doctor is the one that doesn't need a running app — it checks your local environment for the gotchas this project has actually hit (missing WebSocket support being the big one — see the uvicorn[standard] note above). export is currently the only way to keep trace history past a restart; there's no persistence otherwise.

How it works

See ARCHITECTURE.md for the mechanism: the contextvars-based span model, the ASGI middleware that opens/closes a trace per request, the in-memory ring buffer, and the mounted REST/WebSocket server the flow view reads from.

Development

This project uses uv for dependency management.

uv sync --extra dev    # install package + dev deps into .venv
uv run pytest -q       # run tests
uv run routeflow about # run the CLI