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observability-clients

A Python library of simple HTTP API clients for common Observability stack workloads (Prometheus, Mimir, Loki, Grafana, Tempo, Pyroscope, Alertmanager), useful for integration tests and ad-hoc scripts alike.

Overview

observability-clients provides simple, dataclass-based clients for the HTTP APIs of common Observability software:

Module Class Description
observability_clients.api.prometheus Prometheus Query metrics, check alert rules, active alerts, and scrape targets
observability_clients.api.mimir Mimir Query metrics and check alerts via Mimir's Prometheus-compatible API
observability_clients.api.loki Loki Query logs, check labels, label values, and alert rules
observability_clients.api.grafana Grafana Check dashboards, datasources, and alert rules
observability_clients.api.tempo Tempo Search traces, check tags, and tag values
observability_clients.api.pyroscope Pyroscope Query profiles, check profile types, labels, and label values
observability_clients.api.alertmanager Alertmanager Check alerts and silences

Each class exposes:

  • HTTP methods that return parsed API responses (e.g., query(), get_rules())
  • Check methods that return bool (e.g., has_metric(), has_dashboard())

All HTTP methods include automatic retries (via tenacity) on connection errors and timeouts.

Installation

pip install observability-clients

Quick Start

from observability_clients import Prometheus, Loki, Grafana, Tempo

# Check that Prometheus has a specific metric
prom = Prometheus(url="http://localhost:9090")
assert prom.has_metric("up", labels={"job": "my-app"})

# Check that Loki has logs matching a query
loki = Loki(url="http://localhost:3100")
assert loki.has_log_line('{job="my-app"}', pattern="started successfully")

# Check that Grafana has a specific dashboard
grafana = Grafana(url="http://localhost:3000", headers={"Authorization": "Bearer <token>"})
assert grafana.has_dashboard(title="My Dashboard")

# Check that Tempo has traces for a service
tempo = Tempo(url="http://localhost:3200")
assert tempo.has_traces('{ resource.service.name = "my-app" }')

Usage

Prometheus / Mimir

from observability_clients import Prometheus, Mimir

prom = Prometheus(url="http://localhost:9090")

# HTTP methods (return parsed API response)
result = prom.query("up")
rules = prom.get_rules()
alerts = prom.get_alerts()
targets = prom.get_targets()

# Check methods (return bool)
prom.has_metric("up")
prom.has_metric("http_requests_total", labels={"method": "GET"})
prom.has_alert_rule("HighLatency", group="my-group")
prom.has_active_alert("HighLatency")
prom.has_target(job="node-exporter", health="up")

# Mimir has the same interface (different endpoints)
mimir = Mimir(url="http://localhost:8080")
mimir.has_metric("up")

Loki

from observability_clients import Loki

loki = Loki(url="http://localhost:3100")

# HTTP methods
result = loki.query('{job="my-app"}')
labels = loki.get_labels()
values = loki.get_label_values("job")

# Check methods
loki.has_log_line('{job="my-app"}')
loki.has_log_line('{job="my-app"}', pattern=r"error.*timeout")
loki.has_label("job")
loki.has_label_value("job", "my-app")
loki.has_alert_rule("HighErrorRate")

Grafana

from observability_clients import Grafana

grafana = Grafana(
    url="http://localhost:3000",
    headers={"Authorization": "Bearer <token>"},
)

# HTTP methods
dashboards = grafana.search_dashboards(query="My Dashboard")
datasources = grafana.get_datasources()
alert_rules = grafana.get_alert_rules()

# Check methods
grafana.has_dashboard(title="My Dashboard")
grafana.has_dashboard(uid="abc123")
grafana.has_datasource(name="Prometheus")
grafana.has_datasource(type="loki")
grafana.has_alert_rule("HighCPU", group="infra-alerts")

Tempo

from observability_clients import Tempo

tempo = Tempo(url="http://localhost:3200")

# HTTP methods
traces = tempo.search('{ resource.service.name = "frontend" }')
trace = tempo.get_trace("abc123def456")
tags = tempo.get_tags()
values = tempo.get_tag_values("http.method")

# Check methods
tempo.has_trace("abc123def456")
tempo.has_traces('{ resource.service.name = "frontend" }')
tempo.has_tag("service.name")
tempo.has_tag("service.name", scope="resource")
tempo.has_tag_value("http.method", "GET")

Pyroscope

from observability_clients import Pyroscope

pyroscope = Pyroscope(url="http://localhost:4040")

# HTTP methods
flamegraph = pyroscope.render('process_cpu:cpu:nanoseconds:cpu:nanoseconds{service_name="myapp"}')
profile_types = pyroscope.get_profile_types()
labels = pyroscope.get_labels()
values = pyroscope.get_label_values("service_name")

# Check methods
pyroscope.has_profile('process_cpu:cpu:nanoseconds:cpu:nanoseconds{service_name="myapp"}')
pyroscope.has_profile_type("process_cpu:cpu:nanoseconds:cpu:nanoseconds")
pyroscope.has_label("service_name")
pyroscope.has_label_value("service_name", "myapp")

Development

This project uses uv and just for development:

# Set up the virtual environment
just venv

# Run all quality checks (format, lint, test)
just check

# Run tests with coverage
just coverage

Metadata

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