braintrust
SDK for integrating Braintrust
Decision gist · record as of 2026-08-14
Yes, if you are building or evaluating AI applications and want structured logging and evaluation infrastructure. The package is actively maintained, has low install friction, uses a permissive license, and carries no known vulnerabilities. The main constraint is the requirement for Python 3.10+ and a Braintrust API key; if you don't need hosted evaluation or tracing, a lighter alternative may suffice.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or later.
- Requires BRAINTRUST_API_KEY environment variable to connect to the Braintrust service.
- Low friction install with a pure-wheel distribution.
License · maintenance · safety
MIT (permissive) — MIT license is permissive; you can use, modify, and distribute this package with minimal restrictions, provided you include the license notice.
last release 2026-08-11 (3 days) · last repo commit 2026-08-14 · 16 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 7,783,477 downloads/mo, #1,695 on PyPI
Alternatives
Verify before relying
pip install braintrust
from braintrust import Eval
def is_equal(expected, output):
return expected == output
Eval(
"Say Hi Bot",
data=lambda: [{"input": "Foo", "expected": "Hi Foo"}],
task=lambda input: "Hi " + input,
scores=[is_equal],
)- Whether the 10 runtime dependencies (requests, chevron, tqdm, etc.) introduce any transitive vulnerabilities or breaking changes.
- Performance characteristics and scalability limits for large-scale evaluation runs.
- Whether optional extras (cli, openai-agents, otel, temporal) are stable or experimental.
What it is and what it does
Braintrust is an SDK that integrates AI applications with a hosted evaluation and tracing platform. It lets you define evaluation tasks with input data, a task function, and scoring functions, then run those evaluations against the Braintrust backend to log results, trace execution, and compare model outputs against expected behavior. The core workflow involves wrapping your AI task in an Eval object, defining how to score correctness, and running the evaluation via CLI or programmatically.
The package depends on 10 runtime libraries including requests for HTTP communication, jsonschema for validation, tqdm for progress display, and sseclient-py for streaming responses. It supports optional integrations for OpenAI agents, OpenTelemetry, and Temporal workflows via extras. The SDK requires Python 3.10 and an API key to authenticate with the Braintrust service.
Use it for
- Run automated evaluations of LLM outputs against expected results and custom scoring functions.
- Trace execution paths and log intermediate steps of AI applications for debugging and analysis.
- Compare multiple model versions or prompts by running the same evaluation suite against each.
- Monitor AI application behavior in production by instrumenting with Braintrust logging.
- Integrate evaluation workflows into CI/CD pipelines using the CLI or programmatic API.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building or evaluating AI applications and want structured logging and evaluation infrastructure.
The package is actively maintained, has low install friction, uses a permissive license, and carries no known vulnerabilities. The main constraint is the requirement for Python 3.10+ and a Braintrust API key; if you don't need hosted evaluation or tracing, a lighter alternative may suffice.
Install
braintrust on PyPI
Before you install
Low friction install with a pure-wheel distribution. Active maintenance with a release 3 days old and commits current as of 2026-08-14. Requires Python 3.10 or later.
Requires Python 3.10 or later. Requires BRAINTRUST_API_KEY environment variable to connect to the Braintrust service.
License in practice
MIT license is permissive; you can use, modify, and distribute this package with minimal restrictions, provided you include the license notice.
Quickstart
pip install braintrust
from braintrust import Eval
def is_equal(expected, output):
return expected == output
Eval(
"Say Hi Bot",
data=lambda: [{"input": "Foo", "expected": "Hi Foo"}],
task=lambda input: "Hi " + input,
scores=[is_equal],
)Verify before relying
- Whether the 10 runtime dependencies (requests, chevron, tqdm, etc.) introduce any transitive vulnerabilities or breaking changes.
- Performance characteristics and scalability limits for large-scale evaluation runs.
- Whether optional extras (cli, openai-agents, otel, temporal) are stable or experimental.
Package facts
| License | MIT permissive |
| Python support | Supports the current Python release >=3.10.0 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 10 packagesrequestschevrontqdmexceptiongroupjsonschemapackagingsseclient-pypython-slugifytyping_extensionswrapt |
| Maintenance | Actively maintained 3 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 7,783,477 / month, #1,695 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Operating System :: OS IndependentProgramming Language :: Python :: 3Programming Language :: Python :: 3.10 |
Evidence: braintrust-0.33.0-py3-none-any.whl
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