# Metrics by 041

> Experiment tracking your agents can use. Log from Python in one line. Read it back as JSON, SQL or Python. Free with fair use.

ML experiment tracking built for research agents and the people who work with them. Documentation: https://metrics.041.io/docs/index.md. Every page on this site as markdown: https://metrics.041.io/llms.txt.

## In numbers

- **11 µs** per metric() call, Python on Linux
- **0.24 s** to read a series, median, across the Atlantic
- **11 s** to pull 118 runs into DuckDB, cold

## Agent first

If you can click it, your agent can type it. The `syvain-metrics` command line finds runs, reads and draws their curves, files and names them, runs the organization, and hands you a link to the exact charts it wants you to see. Every answer is JSON.

```console
$ syvain-metrics experiment list --search lr3e-4
{"experimentId":"8c1f…","slug":"mamba-lr3e-4-seed7","folderPath":"/mamba/lr-sweep"}
{"experimentId":"a02d…","slug":"mamba-lr3e-4-seed8","folderPath":"/mamba/lr-sweep"}
$ syvain-metrics series render mamba-lr3e-4-seed7 mamba-lr3e-4-seed8 \
    --name loss --filter split=valid -o loss.png
$ syvain-metrics experiment rename mamba-lr3e-4-seed7 "Mamba, best lr"
$ syvain-metrics view link --folder /mamba/lr-sweep
{"url":"https://metrics.041.io/app?org_id=…&w=…"}
```

## The collector

Log anything. Wait for nothing.

```python
from syvain_metrics_collector import Collector

exp = Collector().experiment("mamba-lr3e-4-seed7", meta={"lr": 3e-4, "seed": 7})

with exp.run():
    for step, batch in enumerate(loader):
        exp.metric("loss", train_step(batch), step, {"split": "train"})
        if step % 5_000 == 0:
            exp.annotation("checkpoint", {"path": save_checkpoint(step)}, step=step)
```

- **Simple.** One import, one line per number. The key comes from the environment, the run's lifecycle from a `with` block.
- **Flexible.** Any JSON as config. Split any series by metadata. Annotate checkpoints, evals and anything else that isn't a number.
- **Fast.** A Rust core batches and ships on its own thread. 11 µs per call; a million points queued and written in 11 s.

## Read it back, any way

The same runs, four ways in. Your agent picks the one that suits the job.

- **HTTP API.** JSON in, streamed JSON out. Anything that speaks HTTP can read it. `POST /api/v2/query/series`
- **Command line.** Curves as JSON lines, or as a PNG for agents that read images. `syvain-metrics series query`
- **Python.** Curves straight into a notebook or an analysis script. `exp.series("loss")`
- **DuckDB.** Every run as SQL tables, cached on disk as Parquet. `SELECT … FROM m.series`

Every point is kept at full resolution in a series database we built for this: https://metrics.041.io/docs/series-storage.md

## The app

For when you want to look yourself: loss on top, found by itself, every diagnostic below, drawn as you scroll. Drag runs between folders, set limits and log scales per chart, and save the view for the team.

## Pricing

Free, with fair use. Log what your research needs. Need more, or something we don't do yet? info@041.io