braintrust-grep 0.1.0

Regex and structural search over Braintrust logs, working around BTQL's lack of regex.

braintrust-grep

Tool for regex and structural search over Braintrust logs.

Braintrust's query language (BTQL) has no regex in log filters, and its LIKE/substring operator can silently match nothing on structured (JSON-object) fields.

So the reliable pattern I've found is: prefilter server-side with MATCH, pull logs scoped to a created window, then match locally in Python.

braintrust-grep does this for you while also handling the rate limit, query timeout, gzip redirects, pagination, and span deep-linking for you.

I built this while hunting down hallucinations and other discrepancies in an 'AI' (LLM) powered extraction pipeline.

Install

You can install on Github today, PyPi package coming soon.

As a CLI tool

uv tool install git+https://github.com/aflansburg/braintrust-grep

# …or run it once without installing:
uvx --from git+https://github.com/aflansburg/braintrust-grep btgrep --help

As a library:

uv add "braintrust-grep @ git+https://github.com/aflansburg/braintrust-grep"

Then set your credentials:

export BRAINTRUST_API_KEY=...     # required
export BRAINTRUST_ORG_NAME=...    # optional, for deep-links !!!! Case Sensitive it seems !!!!

Once published to PyPI this becomes uv tool install braintrust-grep / uvx braintrust-grep. The braintrust-grep command is an alias for btgrep, so uvx braintrust-grep resolves without --from.

Quickstart (CLI)

# Find spans whose output matches a regex, last 14 days (shorter time intervals are more ideal: 1h, 3h, etc.):
btgrep search -p my-project --since 14d --regex 'output=timeout|refused'

# AND several field-scoped predicates (regex + "key empty or missing"):
btgrep search -p my-project --since 7d \
    --match-fulltext output=evidence \
    --regex 'input.raw_text=user_\d+' \
    --empty output.result

Subcommands stream JSONL on stdin/stdout and diagnostics go to stderr.

It's a subcommand pattern we decided to support common in pipeline tooling like jq or the OG grep | sed | awk.

btgrep search -p my-project --since 14d --regex 'output=foo' \
  | btgrep enrich -p my-project --root-field input.doc_id --root-field input.patient_id \
  | btgrep export -o out.csv -p my-project --org MyOrg   # project id auto-resolved

You can write up a declarative spec (copy one from examples/ and fill in your project/org/patterns):

btgrep hunt --spec examples/find_errors.yaml -o out.csv

Gnarly regex? Put it in a file and reference it with @: --regex 'output=@pattern.re' (no shell-quoting pain).

Python API

from braintrust_grep import (
    BtqlClient, ClientOptions, resolve_project_id, search, enrich,
    AllOf, Regex, MetadataObjectEmpty, resolve_window,
)

client = BtqlClient(ClientOptions.from_env())
pid = resolve_project_id("my-project")
window = resolve_window("14d", "now")

predicate = AllOf(
    Regex("output", r'"symptoms"\s*:\s*\['),
    MetadataObjectEmpty("input.raw_text", keys=("pages", "documentdata"),
                        marker="=== METADATA ==="),
)
matches = list(search(
    client, pid, window=window, predicate=predicate,
    match=[("output", "evidence"), ("output", "symptoms")],  # server MATCH prefilter
))
enriched = enrich(client, matches, project_id=pid,
                  root_fields=["input.doc_id", "input.patient_id"])

Predicates

• Regex(field, pattern, flags): regex over a dotted field (non-strings are JSON-serialized first, so you can match structure). field="*" matches the whole row.
• EmptyOrMissing(field): true when the key is absent or the value is ""/[]/{}/null. (Regex can't express "missing".)
• MetadataObjectEmpty(field, keys, marker=None): parses JSON found in field — a JSON column, a JSON string, or a block embedded in text after an optional marker (e.g. the field may be raw text with a preceding "=== METADATA ===") — and matches when every named key is empty-or-missing. Schema-agnostic: set field/keys/marker to your own.
• AllOf / AnyOf / Not: compose them.

Constraints & gotchas (why this library exists)

• 30-second query timeout. A single query over 30 seconds will return HTTP 504. Fast-path to staying under that: created-range + MATCH + cursor pagination. Wide id IN/root_span_id IN/LIKE/span-name scans will blow your rate limit budget. Enrichment uses narrow time-bucketed IN batches.
• LIKE is blind to structured fields. output LIKE '%x%' returns 0 when output is a JSON object. Use MATCH. The query builder warns if you LIKE a structured field.
• Rate limit ~20 requests / 60s per org (HTTP 429). The client paces itself and honors Retry-After; retries 408/429/500/502/503/504 with capped backoff.
• Large results 303-redirect to a gzipped object: handled transparently.
• Per-span payloads up to 20 MB (raw_text can be a whole document). The scan selects only the columns your predicates read. Never select: *.
• Row id ≠ span_id. Deep-links need r=<root_span_id>&s=<span_id>, not the row id. span_deeplink() takes care of this for you.

Configuration (env)

Var	Purpose
BRAINTRUST_API_KEY	Auth (required)
BRAINTRUST_ORG_NAME	Org slug for deep-links
BRAINTRUST_BTQL_URL	Override the BTQL endpoint

ClientOptions exposes min_request_interval, max_retries, page_size, retry_statuses, backoff knobs, etc. .env is loaded automatically if python-dotenv is installed (never required).

Development

uv sync --extra dev                 # install with dev + test deps
uv run ruff check . && uv run ruff format --check .

Running the tests

The suite is offline. No Braintrust credentials or network required. HTTP is faked with httpx.MockTransport and time with a FakeClock, so it runs in well under a second.

uv run pytest                       # all tests
uv run pytest --cov                 # with a coverage report
uv run pytest -v                    # verbose (list each test)
uv run pytest tests/test_client.py  # one file
uv run pytest -k "retry or gzip"    # tests matching an expression
uv run pytest -x -q                 # stop at first failure, quiet

TBD

Async client; parallel bucket fetching (the rate limit makes concurrency low-value); result caching/resume; non-project_logs sources (experiments, datasets); a custom-predicate plugin system beyond --spec; an interactive TUI.

Contributing

Issues and PRs welcome. Please:

1. Fork and branch off main.
2. uv sync --extra dev, then keep ruff check, ruff format, and pytest green.
3. Add a test for any behavior change (the suite is offline — mock HTTP with httpx.MockTransport and time with FakeClock; see tests/conftest.py).
4. Open a PR describing the change and, for a bug fix, the failing case it covers.

License

MIT — see LICENSE.