Metadata-Version: 2.4
Name: batchwatch
Version: 0.1.0
Summary: Client for batchwatch.dev - measure queue time on LLM batch APIs without ever blocking your job
Author: Andreas Graae
License: MIT License
        
        Copyright (c) 2026 Andreas Graae
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in the Software without restriction, including without limitation the rights
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        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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Project-URL: Homepage, https://batchwatch.dev
Project-URL: Source, https://github.com/batchwatch/client
Keywords: llm,batch,openai,anthropic,queue,latency,observability
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: System :: Monitoring
Requires-Python: >=3.8
Description-Content-Type: text/markdown
License-File: LICENSE
Provides-Extra: test
Requires-Dist: pytest>=7; extra == "test"
Dynamic: license-file

# batchwatch — Python client

Client for [batchwatch.dev](https://batchwatch.dev): crowdsourced measurement
of queue time on LLM batch APIs.

Batch endpoints cost 50% of the synchronous ones, but "completes within 24
hours" is impossible to plan around. batchwatch measures what the queue
actually does and answers one question: *should I use batch for this job?*

Standard library only. No dependencies, and none planned.

## Install

Not published to PyPI yet. Until it is:

    pip install git+https://github.com/batchwatch/client#subdirectory=python

or copy `src/batchwatch/` into your project — it is two files.

## Two lines

```python
from batchwatch import Batchwatch

bw = Batchwatch(token="tk_...")        # token optional; falls back to $BATCHWATCH_TOKEN

# 1. before you submit — does this belong in the queue?
if bw.should_batch("gpt-5.6-sol", max_wait="15m"):
    job = client.batches.create(...)
else:
    answer = client.chat.completions.create(...)

# 2. measure it, so the next person gets a better answer
with bw.track("gpt-5.6-sol", input_tokens=9720) as t:
    result = wait_for(job)
    t.done(output_tokens=result.usage.completion_tokens)
```

Get a key with no email and no card:

    curl -X POST https://batchwatch.dev/v1/keys -d '{"label":"my pipeline"}'

## It fails open, always

If batchwatch is down, slow, or broken, your job must not notice. That is the
first requirement, ahead of collecting any data at all.

- Every submission runs on a daemon thread. `track()` does no network I/O on
  your thread.
- Two-second timeout by default (`BATCHWATCH_TIMEOUT`).
- Every batchwatch error is swallowed and logged at `DEBUG` on the
  `batchwatch` logger. Nothing is printed unless you ask for it.
- `should_batch()` is the one synchronous call, because you are waiting for
  the answer. If it cannot answer, you get your own `default` back — never a
  guess. The default is `False`, "run it synchronously": being wrong that way
  costs money, being wrong the other way blows a deadline.
- An exception raised inside your own `with` block is recorded as `failed`
  and re-raised untouched. We swallow our errors, never yours.

`tests/test_fail_open.py` proves it against a port nothing listens on and
against a socket that accepts but never answers.

## It never sends your content

No prompts, no completions, no system prompts, no tool calls, no file names.
The request body is built from a fixed allowlist — provider, model, mode,
endpoint, request count, token counts, timestamps, status — and everything
else is dropped in `_rens()` on the way out. There is no field to put text
in.

`tests/test_no_content.py` asserts it on the bytes a real HTTP server
received, and includes a positive control so the test cannot pass by the
client simply sending nothing.

## `output_tokens` defaults to `None`, never `0`

You know your input tokens. You cannot know your output tokens before the
model has answered. So the default is absence, not zero.

Zero is not a harmless placeholder here: output costs five to six times as
much as input, so a saving computed on zero output is systematically too
low — measured at 3.4x too low on a real model — and nothing in the response
would tell you. If you know a ceiling, pass `max_tokens` instead and the
answer comes back labelled as a ceiling.

## Spooling

When a measurement cannot be delivered, the completed record is appended to a
JSONL file and replayed later through `POST /v1/calls/complete`. Losing
measurements exactly when the network is bad means losing them exactly when
they are most interesting.

- Default path: `$BATCHWATCH_SPOOL`, or `batchwatch-spool.jsonl` in the
  system temp directory. Set `BATCHWATCH_SPOOL=""` or pass `spool=None` to
  turn it off.
- The spool is replayed automatically, at most once a minute, right after a
  successful call — that is the moment we know the network is up. Call
  `bw.flush_spool()` yourself from a shutdown hook if you want it drained on
  exit.
- **Spooling requires a token.** `/v1/calls/complete` takes your own
  timestamps, so it is closed to anonymous callers; without a key a spool
  file could never be sent, and writing one would just leak disk. Without a
  token, undeliverable measurements are dropped and logged at `DEBUG`.
- The file is capped at 5 MB. Beyond that, measurements are dropped rather
  than filling your disk.
- A replayed measurement can arrive twice if the original `PATCH` reached the
  server but the response did not. That is deliberate: a duplicate is visible
  in the dataset, a lost measurement is not.
- Threads are handled. Two *processes* sharing one spool file may send a
  record twice — give each process its own `BATCHWATCH_SPOOL` if that matters.

## Configuration

| Argument | Environment | Default |
|---|---|---|
| `token` | `BATCHWATCH_TOKEN` | none (anonymous) |
| `base_url` | `BATCHWATCH_URL` | `https://batchwatch.dev` |
| `timeout` | `BATCHWATCH_TIMEOUT` | `2.0` seconds |
| `spool` | `BATCHWATCH_SPOOL` | `<tempdir>/batchwatch-spool.jsonl` |
| `enabled` | — | `True` |

`enabled=False` turns every network call into a no-op, which is what you want
in CI.

## API

- `should_batch(model, max_wait=None, default=False, **kw) -> bool`
- `advice(model, max_wait=None, provider="openai", input_tokens=None, output_tokens=None, max_tokens=None, risk="p90") -> dict | None`
- `wait_now(model, provider="openai", mode="batch") -> dict | None`
- `track(model, provider="openai", mode="batch", requests=1, input_tokens=None, endpoint=None)` — context manager
  - `t.done(output_tokens=None, status="completed", ttfb_ms=None)`
  - `t.failed()`
  - `t.started(input_tokens=...)` when the count is only known after submission
- `flush(timeout=5.0) -> bool` — wait for outstanding submissions before exit
- `flush_spool(timeout=None) -> int` — send what is on disk, returns accepted

## Tests

    python -m pytest -q

25 tests, no network beyond loopback. They start real HTTP servers on
ephemeral ports rather than monkeypatching `urllib`: the thing under test is
network behaviour, so the network should be in the test.

## Licence

MIT
