observabilipy 0.14.0

Framework-agnostic metrics and structured log collection with hexagonal architecture

observabilipy

Framework-agnostic metrics and structured log collection with hexagonal architecture.

Develop observability features decoupled from your infrastructure. Use embedded storage (SQLite, in-memory) during development, then optionally expose endpoints for scraping by Prometheus, Grafana Alloy, or other observability platforms when you're ready.

Features

• Prometheus-style metrics - /metrics endpoint in text format
• Structured logs - /logs endpoint in NDJSON (Grafana Alloy compatible)
• Framework adapters - FastAPI, Django, generic ASGI
• Storage backends - In-memory, SQLite (with WAL), Ring buffer
• Retention policies - Automatic cleanup with EmbeddedRuntime

Installation

git clone https://github.com/PhilHem/observabilipy.git
cd observabilipy
uv sync

For framework support:

uv sync --extra fastapi
uv sync --extra django

Quick Start

from fastapi import FastAPI
from observability.adapters.frameworks.fastapi import create_observability_router
from observability.adapters.storage.in_memory import (
    InMemoryLogStorage,
    InMemoryMetricsStorage,
)

app = FastAPI()
log_storage = InMemoryLogStorage()
metrics_storage = InMemoryMetricsStorage()

app.include_router(create_observability_router(log_storage, metrics_storage))

Run with uvicorn and visit /metrics and /logs.

Recording Metrics and Logs

import time
from observability.core.models import LogEntry, MetricSample

# Record a log entry
await log_storage.write(
    LogEntry(
        timestamp=time.time(),
        level="INFO",
        message="User logged in",
        attributes={"user_id": 123, "ip": "192.168.1.1"},
    )
)

# Record a metric sample
await metrics_storage.write(
    MetricSample(
        name="http_requests_total",
        timestamp=time.time(),
        value=1.0,
        labels={"method": "GET", "path": "/api/users"},
    )
)

Storage Backends

Backend	Use Case
InMemoryLogStorage / InMemoryMetricsStorage	Development and testing
SQLiteLogStorage / SQLiteMetricsStorage	Persistent storage with WAL mode for concurrent access
RingBufferLogStorage / RingBufferMetricsStorage	Fixed-size buffer for memory-constrained environments

All backends implement the same port interfaces and are interchangeable.

Examples

See the examples/ directory:

Example	Description
minimal_example.py	Dummy metrics and logs generator for testing
cgroups_example.py	Container CPU and memory metrics from cgroups v2
fastapi_example.py	Basic FastAPI setup with in-memory storage
django_example.py	Django integration
asgi_example.py	Generic ASGI middleware
sqlite_example.py	Persistent storage with SQLite
ring_buffer_example.py	Fixed-size storage for constrained environments
embedded_runtime_example.py	Background retention cleanup