pytigo 0.3.0

Python client for the documented Tigo REST API v3

pytigo

pytigo is a Python client for the documented Tigo REST API v3. It is focussed on near-real time monitoring (for time-series systems like Prometheus).

It is centered on the official API described in Tigo-API-V3.pdf, using:

• base URL: https://api2.tigoenergy.com/api/v3/
• token auth from users/login
• documented systems, objects, sources, and data endpoints

Supported official API areas:

• users/login
• users/logout
• users/get
• systems/list
• systems/view
• systems/layout
• objects/system
• objects/types
• sources/system
• data/summary
• data/aggregate
• data/combined
• alerts/system
• alerts/types

Install

pip install pytigo

Quick start

from pytigo import TigoClient

client = TigoClient(username="[e-mail]", password="super-secret")
auth = client.login()

systems = client.list_systems()
system = systems[0]

layout = client.get_layout(system.system_id)
objects = client.get_objects(system.system_id)
sources = client.get_sources(system.system_id)
summary = client.get_summary(system.system_id)
aggregate = client.get_aggregate(
    system.system_id,
    start="2026-03-31T00:00:00",
    end="2026-03-31T23:59:59",
    level="day",
    param="Pin",
)
combined = client.get_combined(
    system.system_id,
    start="2026-03-31T00:00:00",
    end="2026-03-31T23:59:59",
    agg="day",
)
alerts = client.get_alerts(system.system_id, limit=10)
alert_types = client.get_alert_types()

print(auth.user_id)
print(system.name)
print(layout.inverters[0].label if layout.inverters else None)
print(len(objects))
print(sources[0].serial if sources else None)
print(summary.daily_energy_dc)
print(aggregate.rows[0].values)
print(combined.rows[0].values)
print(alerts[0].title if alerts else None)
print(alert_types[0].title if alert_types else None)

Notes

The library returns typed Python models for JSON endpoints and a parsed table model for CSV-style telemetry endpoints like data/aggregate and data/combined.

data/aggregate and data/combined are especially useful for exporter/monitoring use cases because they preserve timestamped telemetry in a table-shaped format that is easy to flatten for Prometheus, Grafana, or ETL pipelines.