Hacktoberfest 2026: los issues que los mantenedores marcaron para octubre, abiertos y aptos para principiantes. Explorar issues de Hacktoberfest

[bot] Instrument Guardrails AI (30,625 weekly downloads)

Abierto
  • ¿Algo no está bien?
#807 0 comentarios 0 reacciones 0 asignados Ver en GitHub

Los mantenedores suelen responder en 1 día

Nadie ha tomado este issue todavía.

Evaluación

Dificultad
4/5
Tiempo estimado
3-5 días
Aptitud para principiantes
48/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
observability

Línea de trabajo

Start in py/src/braintrust/integrations/ and inspect py/src/braintrust/wrappers/, then read the [tool.braintrust.matrix] section of py/pyproject.toml and py/noxfile.py for existing integration patterns. Done means the guarded call and parse entry points are instrumented, with the integration represented in the matrix and a nox session or other repository test coverage added.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

new-integration

Summary

guardrails-ai (import name guardrails, by Guardrails AI Inc.) is a framework for adding structural, type, and quality validation directly around LLM calls. Its primary execution surfaces are Guard.for_pydantic(output_class=...) / Guard().use(<validators>) to construct a guard, and then the callable form guard(model=..., messages=[...]), which invokes the LLM directly and returns a response with .validated_output — i.e. the Guard itself owns the LLM call, not just post-hoc validation of already-generated text. guard.parse(...) is the sibling entrypoint for validating a given string against attached validators.

This is a distinct project/package from NVIDIA NeMo Guardrails (nemoguardrails, tracked separately in #768) — different maintainer, different PyPI package, different API shape (Guard/validators around a direct LLM call vs. NeMo's LLMRails rails pipeline). This repository has zero instrumentation for Guardrails AI anywhere: no py/src/braintrust/integrations/guardrails* directory, no wrapper, no nox session, no cassette directory, and no entry in py/pyproject.toml's [tool.braintrust.matrix].

What's missing

  • guard(model=..., messages=[...]) (and async variant) — the primary "guarded LLM call" entrypoint; since Guardrails AI drives the LLM call itself, an unwrapped call here means the underlying model call, validated output, and any validator retries are entirely invisible to Braintrust tracing.
  • guard.parse(...) / guard.parse_async(...) — validates a given string against attached validators, returning a result with .validation_passed.
  • Guard.for_pydantic(output_class=...) / Guard().use(<validators>) — guard construction with attached validators (useful for capturing validator configuration as metadata on the guarded-call span).

Weekly downloads

Weekly downloads: 30,625 (as of 2026-09-28; https://pypistats.org/api/packages/guardrails-ai/recent)

Braintrust docs status

not_found. Checked https://www.braintrust.dev/docs/guides/tracing and https://www.braintrust.dev/docs/guides/tracing/integrations (both checked 2026-09-28) — neither the tracing-integrations list ("OpenTelemetry, Temporal, Vercel AI SDK, OpenRouter SDK, LangChain, LangChain4j, LangSmith, LlamaIndex, Apollo GraphQL, Cloudflare Workers AI, DSPy, Google Discovery Engine, Instructor, LiteLLM, Ruby LLM, Spring AI, Traceloop, TrueFoundry") nor the agent-framework list mentions Guardrails AI or Guardrails.

Upstream sources

Braintrust docs sources checked

Local repo files inspected

  • py/src/braintrust/integrations/ — no guardrails/ directory (only unrelated py/src/braintrust/integrations/__init__.py and the separately-tracked nemoguardrails gap in #768)
  • py/src/braintrust/wrappers/ — no Guardrails AI wrapper
  • py/pyproject.toml [tool.braintrust.matrix] — no guardrails-ai entry
  • py/noxfile.py — no test_guardrails session
  • Repo-wide case-insensitive grep for guardrails.ai/guardrails_ai under py/src/braintrust/**/*.py — zero matches
Lenguaje dominante
Python
Estrellas
20
Forks
18
Merge medio
21 h 8 min
PR fusionados (30 d)
81

Preparar el entorno

Primeros pasos

  1. Lee el issue completo y luego la guía de contribución del proyecto.
  2. Comenta en el issue que vas a ocuparte — evita que dos personas hagan lo mismo.
  3. Haz un fork del repositorio y trabaja en una rama.
  4. Abre un pull request que haga referencia al número del issue.

Más de braintrustdata/braintrust-sdk-python

Todos los issues de braintrustdata/braintrust-sdk-python

Issues similares

Más issues de Python

Recibe los nuevos issues en tu correo

Un resumen breve de issues de GitHub para principiantes.