---
id: trackio
version: "0.35.0"
license: unclear
license_treatment: permissive
maintenance: active
---

# trackio — A lightweight, local-first, and free experiment tracking library built on top of Hugging Face Datasets and Spaces.

License: permissive · Maintenance: active · Downloads: 606.5K/mo

## What it is and what it does

Trackio is a lightweight experiment tracking library designed for machine learning workflows and autonomous agents. It stores experiment metadata, metrics, and media in a local SQLite database, avoiding the need for account creation or cloud signup. The library provides a wandb-compatible API (init, log, finish), so existing logging code can often be used without modification by importing trackio as wandb. It includes a Gradio-based web dashboard for viewing results, a CLI for querying experiment data with SQL, and optional integration with Hugging Face Spaces for collaborative sharing—all free.

The package depends on a modest set of runtime libraries: starlette and uvicorn for the dashboard server, gradio-client and huggingface-hub for Space integration, numpy and pillow for media handling, orjson for serialization, brotli for compression, python-multipart for form parsing, and tomli for config. It is designed to handle high-throughput parallel logging and to be LLM-friendly, with programmatic access to run management and SQL-based data querying for autonomous analysis.

Use it for:

- Log metrics from parallel training runs locally without cloud infrastructure or authentication.
- Replace wandb in existing scripts by importing trackio as wandb, keeping logging code unchanged.
- Query experiment data programmatically via CLI SQL interface for LLM-driven analysis and decision-making.
- Share experiment results by syncing local projects to a free Hugging Face Space for team collaboration.
- Embed live experiment dashboards on websites or blogs using Space URLs with query parameter filtering.
- Track autonomous agent experiments with high-throughput logging and direct database access for inspection.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

Trackio is a local-first experiment tracking library that logs metrics, media, and configs to a SQLite database with a web dashboard, and optionally syncs to Hugging Face Spaces for sharing.

Yes. Trackio is actively maintained, has no known vulnerabilities, installs with low friction, and offers a genuinely useful local-first alternative to cloud-dependent trackers. The wandb API compatibility makes adoption frictionless. MIT licensing imposes no restrictions. It is well-suited for researchers, agents, and teams who want experiment tracking without signup or infrastructure overhead.

## Install

pip install trackio
uv add trackio
poetry add trackio

## Installing trackio

Before you install: Low friction: pure Python wheel with no compiled dependencies. Active maintenance—released 1 day ago with 1642 GitHub stars. Requires Python 3.10 or higher.

License in practice: MIT license (permissive): you can use, modify, and distribute Trackio freely in commercial and private projects without restriction.

Quickstart:

pip install trackio

import trackio

trackio.init(project="my-project", config={"lr": 0.001})
trackio.log({"loss": 0.5})
trackio.finish()
trackio.show()

Requires Python 3.10 or higher.

Verify before relying:

- Throughput limits for parallel experiments and SQLite write concurrency under real workloads.
- Performance characteristics when querying large experiment databases via CLI.
- Compatibility with custom frontends beyond the minimal starter template.
- Behavior and data consistency when syncing offline projects to Spaces with concurrent writes.

## Package facts

- License: not declared (permissive)
- Python support: supports_current
- Install friction: low
- Maintenance: active
- Downloads: 606.5K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

experiment tracking library, local metrics logging, wandb alternative, ml experiment dashboard, training run monitoring, hugging face experiment tracking, sqlite experiment database, experiment-tracking, ml-ops, local-first

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