flowtrace 0.2.0

System-level execution tracer for Python 3.12+ (PEP 669)

🌐 Also available in: Русская версия

# 🌀 FlowTrace — Visual Execution Tracing for Python 3.12+

FlowTrace is a system-level tracer built on Python’s Monitoring API (PEP 669). It doesn’t “profile time by default”. Instead, it reconstructs what happened in your program — calls, returns, structure — with minimal overhead and zero monkey-patching.

Status: experimental alpha. Python 3.12+ only.

---

Installation

pip install flowtrace

---

Quick Start

1) One-line decorator

from flowtrace import trace

@trace
def fib(n):
    return n if n < 2 else fib(n-1) + fib(n-2)

fib(3)

Output:

→ fib(3)
  → fib(2)
    → fib(1) → 1
    → fib(0) → 0
  ← fib(2) → 1
  → fib(1) → 1
← fib(3) → 2

---

2) Timing when you need it

from flowtrace import trace

@trace(measure_time=True)
def compute(a, b):
    return a * b

compute(6, 7)

Output:

→ compute(6, 7) [0.000265s] → 42

---

3) Manual session

from flowtrace import start_tracing, stop_tracing, print_tree

def fib(n):
    return n if n < 2 else fib(n-1) + fib(n-2)

start_tracing()
fib(3)
events = stop_tracing()
print_tree(events)

Output:

→ fib()
  → fib()
    → fib()  → 1
    → fib()  → 0
  ← fib()  → 1
  → fib()  → 1
← fib()  → 2

---

Global configuration

import flowtrace
flowtrace.config(show_args=False, show_result=True, show_timing=True)

Controls which information is collected globally. All flags default to True.

Flag	Description
show_args	capture and display call arguments
show_result	capture and display return values
show_timing	measure and display function duration

Function-level overrides

@flowtrace.trace(show_args=True)
def foo(x): ...

Local flags temporarily override global ones for this function only; child calls inherit the global configuration.

Example

import flowtrace

flowtrace.config(show_args=False, show_result=True, show_timing=True)

@flowtrace.trace
def a(x): return b(x) + 1

@flowtrace.trace(show_args=True)
def b(y): return y * 2

a(10)

Output:

→ a() [0.000032s] → 21
  → b(y=10) [0.000010s] → 20
  ← b(y=10)
← a()

---

Why FlowTrace?

• Not a profiler: profilers answer “how long”. FlowTrace answers “what, in which order, and why”.

• Direct line to the VM: listens to bytecode-level events via sys.monitoring (PEP 669).

• No code intrusion: no sys.settrace, no monkey-patching, no stdout noise.

---

API (current)

from flowtrace import trace, config, start_tracing, stop_tracing, get_trace_data, print_tree

• @trace(measure_time: bool = True) Decorate a function to include its calls in the trace. When measure_time=True, durations for this function’s calls are recorded.

• start_tracing() / stop_tracing() -> list[CallEvent] Start/stop a process-wide tracing session. By default no timing is recorded here — only structure.

• get_trace_data() -> list[CallEvent] Access the last recorded events.

• print_tree(events) Pretty-print a hierarchical call tree.

Event model (CallEvent):

id: int
kind: str
func_name: str
parent_id: int | None
args_repr: str | None
result_repr: str | None
duration: float | None
collect_args: bool
collect_result: bool
collect_timing: bool

---

Design choices (snapshot)

• Only PY_START / PY_RETURN: we do not listen to CALL to keep the core lean. Argument strings are provided by the decorator right before the call starts.

• Timing is opt-in: perf_counter() is used only when measure_time=True. Starting/stopping a session alone does not add timing overhead.

• Filter user code: internal modules and site-packages are excluded from the default output.

---

Design notes

• Zero-overhead when disabled: arguments, results, and timing are gathered only if their flags are True.

• Named argument binding: readable form like a=5, b=2 via inspect.signature, cached at decoration time.

• No cascades: per-function flags affect only that decorated function.

---

Roadmap

• Async/coroutine transitions.

• JSON export for post-processing.

• Include/exclude filters & colorized output.

• Minimal CLI helpers.

---

Contributing

We welcome small, surgical PRs. The codebase is intentionally compact to be an approachable learning tool for exploring Python 3.12+ internals.