metrun 0.1.10

Execution Intelligence Tool — Bottleneck Engine + Human Report Generator

metrun — Execution Intelligence Tool

AI Cost Tracking

• 🤖 LLM usage: $1.3500 (9 commits)
• 👤 Human dev: ~$657 (6.6h @ $100/h, 30min dedup)

Generated on 2026-04-07 using openrouter/qwen/qwen3-coder-next

---

metrun doesn't just show you data — it tells you what the problem is and how to fix it.

What is metrun?

metrun is a Python performance analysis library that turns raw profiling data into an intelligible execution report: bottleneck scores, dependency graphs, critical path highlighting, and actionable fix suggestions — all in one tool.

❌ traditional profilers → "here is your data"
✅ metrun               → "here is your problem and why it exists"

---

Features

Feature	Description
🧠 Bottleneck Engine	Builds an execution graph, computes score = time + calls + nested amplification, ranks hotspots
📊 Human Report Generator	Emoji-annotated report with time %, call count, score and diagnosis per function
🧨 Critical Path	Finds the hottest nested call chain root → leaf
💡 Fix Suggestion Engine	Library-specific advice per diagnosis: lru_cache, asyncio, numba, viztracer, scalene …
🔥 ASCII Flamegraph	Terminal-friendly proportional bar chart, zero extra dependencies
🖼️ SVG Flamegraph	Interactive SVG via flameprof
🔌 cProfile Bridge	Use stdlib cProfile as the profiling backend; feed results into the Bottleneck Engine
📋 TOON Metric Tree	metrun scan auto-profiles and generates metrun.toon.yaml — compact bottleneck map for the TOON ecosystem
⌨️ CLI	metrun profile, metrun inspect, metrun scan, metrun flame commands

---

Installation

pip install metrun            # core (click included)
pip install metrun[flamegraph] # + SVG flamegraph support (flameprof)

---

Quick Start

Decorator tracing

from metrun import trace, get_records, analyse, print_report

@trace
def slow_query(n):
    return sum(i * i for i in range(n))

@trace
def handler(items):
    return [slow_query(i) for i in items]

handler(list(range(100)))

bottlenecks = analyse(get_records())
print_report(bottlenecks)

Context-manager tracing

from metrun import section, get_records, analyse, print_report

with section("data_load"):
    data = load_from_db()

with section("transform"):
    result = process(data)

print_report(analyse(get_records()))

Full enhanced report

from metrun import analyse, get_records, print_report

records = get_records()
bottlenecks = analyse(records)

print_report(
    bottlenecks,
    show_graph=True,           # dependency graph
    show_critical_path=True,   # hottest call chain
    records=records,
    show_suggestions=True,     # fix advice
)

---

Example output

🔥 METRUN PERFORMANCE REPORT
=============================

🔴 slow_query
   → time:      0.8200s  (78.2%)
   → calls:     12,430
   → score:     12.9
   → diagnosis: 🔥 loop hotspot

── Critical Path ─────────────────────────────
🧨 Critical Path  (depth=2, hottest leaf: 0.8200s)

  handler  [1.0500s, 1 calls]
    └─ slow_query  [0.8200s, 12430 calls]   ← 🔥 hottest leaf (0.8200s)

── Fix Suggestions ───────────────────────────
  💡 Fix suggestions for: slow_query
     1. Cache repeated results with lru_cache [functools]
           from functools import lru_cache

           @lru_cache(maxsize=None)
           def slow_query(x): ...

     2. Vectorise the loop with NumPy [numpy]
           import numpy as np
           result = np.sum(arr ** 2)

---

Auto-diagnosis labels

Label	Trigger
🔥 loop hotspot	calls ≥ 1 000
🌲 dependency bottleneck	≥ 3 direct children in the execution graph
🐢 slow execution	≥ 30 % of total wall time (time_pct ≥ 0.30), low calls
✅ nominal	below all thresholds

Score formula:

score = (total_time / max_time) × 10  +  log10(calls + 1)  +  n_children × 0.5

---

ASCII Flamegraph

from metrun import render_ascii, print_ascii

print_ascii(bottlenecks, title="My App Flamegraph")

🔥 My App Flamegraph
────────────────────────────────────────────────────────
  slow_query    ████████████████████████████████████████  78.2%  score=12.9
  handler       █████████████████████████████████████████ 100.0%  score=9.4
  serialize     ██░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░░   5.1%  score=2.1
────────────────────────────────────────────────────────

---

SVG Flamegraph (via flameprof)

from metrun.cprofile_bridge import CProfileBridge
from metrun import render_svg

bridge = CProfileBridge()
with bridge.profile_block():
    my_function()

render_svg(bridge.get_stats(), "flame.svg")
# Open flame.svg in a browser for the interactive flamegraph

---

cProfile Bridge

Integrate with stdlib cProfile or any existing .prof dump:

from metrun.cprofile_bridge import CProfileBridge
from metrun import analyse, print_report

bridge = CProfileBridge()

@bridge.profile_func
def my_function():
    ...

my_function()

# Analyse with the Bottleneck Engine
bottlenecks = analyse(bridge.to_records())
print_report(bottlenecks)

# Save for snakeviz / flameprof CLI
bridge.save("profile.prof")

Compatible with these popular tools (no code changes needed):

Tool	Command
snakeviz — interactive web viewer	snakeviz profile.prof
flameprof — SVG flamegraph	flameprof profile.prof > flame.svg
py-spy — sampling profiler	py-spy record -o flame.svg -- python script.py
viztracer — full trace + HTML flamegraph	see below
scalene — line-level CPU+memory	python -m scalene script.py

---

Language-neutral records interchange

metrun can export and import normalised profiling data as JSON.

• metrun profile my_script.py --export-records profile.json
  • saves the collected records as language-neutral JSON.
• metrun inspect --records profile.json
  • loads a JSON or JSONL records file produced by metrun or another runtime.
• metrun inspect --records profile.json --export-records normalized.json
  • loads records, normalises them, and writes them back out as language-neutral JSON.

The importer accepts top-level records, functions, nodes, or items collections, plus single-record objects and mapping-of-records payloads. The language field is preserved when present.

Example payload:

{
  "schema_version": 1,
  "language": "javascript",
  "records": [
    {
      "name": "root",
      "total_time": 1.0,
      "calls": 1,
      "children": ["child"],
      "parents": [],
      "language": "javascript"
    },
    {
      "name": "child",
      "total_time": 0.25,
      "calls": 4,
      "children": [],
      "parents": ["root"],
      "language": "javascript"
    }
  ]
}

For JSONL, write one record per line:

{"name":"root","total_time":1.0,"calls":1,"children":["child"],"language":"javascript"}
{"name":"child","total_time":0.25,"calls":4,"parents":["root"],"language":"javascript"}

---

VizTracer integration

# pip install viztracer
from viztracer import VizTracer

with VizTracer(output_file="trace.json"):
    my_function()

# vizviewer trace.json  →  opens interactive HTML flamegraph

---

Critical Path

from metrun import find_critical_path, print_critical_path, get_records

path = find_critical_path(get_records())
print_critical_path(path)

🧨 Critical Path  (depth=3, hottest leaf: 0.4200s)

  handler  [0.9100s, 1 calls]
    └─ db_query  [0.6300s, 50 calls]
      └─ serialize  [0.4200s, 50 calls]   ← 🔥 hottest leaf (0.4200s)

---

Fix Suggestion Engine

from metrun import analyse, get_records, suggest, format_suggestions

for b in analyse(get_records()):
    tips = suggest(b)
    print(format_suggestions(b.name, tips))

Suggestion catalogue per diagnosis:

Diagnosis	Suggestions
🔥 loop hotspot	functools.lru_cache, numpy vectorisation, numba @jit
🌲 dependency bottleneck	concurrent.futures, asyncio.gather, batching
🐢 slow execution	cProfile + snakeviz, algorithmic review, joblib.Memory
Score ≥ 8 (any)	scalene, viztracer

---

CLI

# Profile a script — bottleneck report (user code only, stdlib filtered)
metrun profile my_script.py

# Profile + ASCII flamegraph in terminal
metrun profile my_script.py --ascii-flame

# Profile + save SVG flamegraph
metrun profile my_script.py --flame flame.svg

# Full enhanced report: bottlenecks + critical path + suggestions
metrun inspect my_script.py

# Export normalised records for another runtime or later analysis
metrun profile my_script.py --export-records profile.json

# Analyse language-neutral JSON or JSONL records
metrun inspect --records profile.json
metrun inspect --records profile.jsonl

# Load, normalise, and re-export language-neutral records
metrun inspect --records profile.json --export-records normalized.json

# Include Python stdlib / C-builtins in the report
metrun profile my_script.py --include-stdlib
metrun inspect my_script.py --include-stdlib

# Auto-scan and generate metrun.toon.yaml metric tree
metrun scan my_script.py --output project/

# Scan from pre-collected records
metrun scan --records profile.json --output project/

# Convert existing .prof dump to SVG
metrun flame profile.prof -o flame.svg

---

Automatic project scanning & TOON output

metrun scan profiles a Python script (or loads pre-collected records) and generates a metrun.toon.yaml file containing a compact metric tree that describes the project's performance bottlenecks.

How it works

1. Endpoint recognition — metrun identifies root functions (entry points) as any function with no recorded callers. In decorator mode these are the top-level @trace-d functions; in cProfile mode they are the call-tree roots after stdlib filtering.
2. Profiling — the script is executed under cProfile (via CProfileBridge) and the resulting call tree is converted to FunctionRecord entries.
3. Bottleneck analysis — the BottleneckEngine scores every function and assigns a diagnosis label.
4. Critical path — a DFS walk finds the hottest root→leaf chain.
5. TOON rendering — all results are formatted into a compact .toon.yaml file with sections: SUMMARY, BOTTLENECKS, CRITICAL-PATH, SUGGESTIONS, ENDPOINTS, and TREE.

Example output

# metrun | 2b | top: handler 🌲 | python | 2026-04-07

SUMMARY:
  bottlenecks: 2
  top_score: 11.3
  top_name: handler
  top_diagnosis: 🌲 dependency bottleneck
  total_time: 1.5500s
  total_calls: 101

BOTTLENECKS[2]:
  🌲 handler                        score=11.3   time=0.8000s (51.6%)  calls=1       dependency bottleneck
  🐢 slow_query                     score=10.3   time=0.7500s (48.4%)  calls=100     slow execution

CRITICAL-PATH (depth=2, leaf=0.7500s):
  handler → slow_query ← 🔥

SUGGESTIONS[2]:
  handler: Run independent child calls concurrently [concurrent.futures]
  slow_query: Profile deeper with cProfile + snakeviz [cProfile / snakeviz]

ENDPOINTS[1]:
  handler  calls=1  time=0.8000s  children=1

TREE:
  🌲 handler  0.8000s  ×1
  │ ├─ 🐢 slow_query  0.7500s  ×100

Python API

from metrun import analyse, get_records, generate_toon, save_toon

bottlenecks = analyse(get_records())
toon = generate_toon(bottlenecks, get_records())
save_toon(toon, "project/metrun.toon.yaml")

Integration with project.sh

metrun scan demo.py --output project/

The generated metrun.toon.yaml sits alongside other TOON files (analysis.toon.yaml, duplication.toon.yaml, validation.toon.yaml, etc.) and gives a performance perspective on the project.

---

Architecture

  @trace / section()          cProfile.Profile
       │                            │
       ▼                            ▼
 ExecutionTracer              CProfileBridge
  (FunctionRecord)             .to_records()
       │                            │
       └──────────┬─────────────────┘
                  ▼
         BottleneckEngine.analyse()
          score + diagnosis + rank
                  │
       ┌──────────┼──────────────┐
       ▼          ▼              ▼
  print_report  find_critical  suggest()
  (report.py)    _path()      (suggestions.py)
                            
  ASCII/SVG flamegraph ← flamegraph.py

The two tracing backends (ExecutionTracer for decorator/section API and CProfileBridge for cProfile API) both produce the same Dict[str, FunctionRecord] structure consumed by the engine.

Module overview

metrun/
├── profiler.py        # ExecutionTracer — decorator + context-manager tracing
├── bottleneck.py      # BottleneckEngine — score, diagnosis, ranking
├── report.py          # Human Report Generator
├── critical_path.py   # Critical path analysis (DFS on call graph)
├── suggestions.py     # Fix Suggestion Engine
├── flamegraph.py      # ASCII + SVG (flameprof) flamegraphs
├── cprofile_bridge.py # cProfile ↔ metrun bridge
├── toon.py            # TOON metric-tree generator (metrun.toon.yaml)
└── cli.py             # Click CLI entry-point

Known limitations

Limitation	Detail
Name collisions in cProfile mode	CProfileBridge.to_records() uses function name only as key (no file:lineno) — functions with the same name in different modules are merged
Decorator tracing is opt-in	Only functions decorated with @trace or wrapped in section() appear in get_records() — not the full call tree
Thread-local call stack	Each thread has an independent call stack; cross-thread parent→child links are not recorded
No async support	asyncio coroutines are not automatically traced by the decorator backend

cProfile filtering

By default CProfileBridge.to_records() and the CLI commands strip Python stdlib, C-builtins, anonymous entries (<module>, <genexpr>, etc.) and metrun's own internals — so the report focuses on user code only. Call graph connectivity is maintained through bridging: filtered intermediate nodes (e.g. decorator wrappers) are transparently traversed when rebuilding parent→child links.

records = bridge.to_records()                    # user code only (default)
records = bridge.to_records(exclude_stdlib=False) # full call tree

License

Licensed under Apache-2.0.