fastapi-watchtrace 0.0.2

Request tracing and visualization for FastAPI applications.

WatchTrace

WatchTrace is a project-aware FastAPI runtime observability tool that traces requests across your application and maps them to user-defined classes and functions.

It helps developers:

• understand request flow
• inspect function-level execution
• debug complex pipelines
• visualize backend behavior without framework noise

---

🚀 Installation

pip install fastapi-watchtrace

or

uv add fastapi-watchtrace

---

⚙️ Requirements

• Use async def endpoints for best tracing accuracy
• Sync endpoints (def) may lead to incomplete or unstable traces

---

🧪 Example Usage

For this below file structure

> tree
.
├── README.md
├── main.py
├── pyproject.toml
└── uv.lock

Import and initialize watchtrace

from fastapi import FastAPI
from watchtrace import setup_watchtrace

app = FastAPI(
    title="My Server",
    version="1.0.0",
    description="My Server",
)

setup_watchtrace(
    app,
    source_root=".",
    code_index_path=".code_index.json",
    output_dir=".server_profile", # <- here you are mentioning where to store the profile logs
    enable_ui=True,
)


@app.get("/")
async def healthcheck():
    return {
        "status": "ok",
    }

Then run you server

uvicorn main:app --reload --port 8000

Once the server is up, you can visit this url http://localhost:8000/__watchtrace/

---

🎬 Request Flow Visualization

Below is an example of WatchTrace tracing a complex FastAPI request flow:

---

✨ Features

• 🔍 Function-level request tracing
• 🧠 Project-aware filtering (no framework noise)
• 📊 Visual request flow graph
• 🧵 Supports async FastAPI pipelines
• 📦 Zero external dependencies for users (no UI server needed)
• ⚡ Lightweight and easy to integrate

---

💡 Why WatchTrace?

Modern backend systems are complex:

• nested service calls
• ML pipelines
• async execution flows

Traditional logs don’t show how execution actually flows.

WatchTrace solves this by giving you:

A visual, step-by-step breakdown of what happened inside your backend.

---

---

🛠️ Local Development

Clone the repository:

git clone https://github.com/HOTSONHONET/WatchTrace.git
cd WatchTrace

Install dependencies using uv:

uv sync

Run the frontend in development mode:

cd frontend/watchtrace-ui
npm install
npm run dev

Build the frontend UI:

npm run build

The generated UI assets will be available inside:

frontend/watchtrace-ui/dist

Run the example ML pipeline server:

uv run uvicorn examples.ml_pipeline_server.main:app --reload

Open WatchTrace UI:

http://localhost:8000/__watchtrace/

---

✅ Testing

Run all unit tests:

uv run --with pytest pytest tests/unit_test

Build the package locally:

uv build

Validate package metadata:

uv run --with twine twine check dist/*

Run wheel smoke test:

./scripts/smoke_test_wheel.sh

The smoke test validates:

• packaged wheel installation
• WatchTrace UI mounting
• internal API availability
• generated frontend asset serving

---

📦 Releasing a New Version

Update the version inside pyproject.toml:

version = "0.0.1"

Build the package:

rm -rf dist build src/*.egg-info
uv build

Validate the package:

uv run --with twine twine check dist/*

Upload manually to PyPI:

uv run --with twine twine upload dist/*

Or create a release tag for CI/CD publishing:

git tag v0.0.1
git push origin v0.0.1

WatchTrace uses semantic versioning:

MAJOR.MINOR.PATCH

Examples:

• 0.1.0 → new feature
• 0.1.1 → bug fix
• 1.0.0 → stable public release

---

🔗 Links

• GitHub: https://github.com/HOTSONHONET/WatchTrace
• PyPI: https://pypi.org/project/fastapi-watchtrace/

---

📄 License

MIT License