Hacktoberfest 2026: die Issues, die Maintainer für den Oktober markiert haben – offen und einsteigerfreundlich. Hacktoberfest-Issues durchsuchen

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

Offen
  • Stimmt etwas nicht?
#807 0 Kommentare 0 Reaktionen 0 zugewiesene Personen Auf GitHub ansehen

Maintainer antworten meist innerhalb von 1 Tag

Dieses Issue hat noch niemand übernommen.

Bewertung

Schwierigkeit
4/5
Geschätzter Aufwand
3-5 Tage
Anfängerfreundlichkeit
48/100
Issue-Typ
Feature
Klarheit
Größtenteils klar
Aktivitätsstatus
Aktiv
Tech-Stack
python
Bereich
observability

Rechercherichtung

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.

Vom Indexierungsmodell aus dem Issue-Text verfasst.

Beschreibung

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
Vorherrschende Sprache
Python
Sterne
22
Forks
24
Ø Merge
16 Std.
Gemergte PRs (30 T.)
101

Entwicklungsumgebung

Erste Schritte

  1. Lesen Sie das ganze Issue und danach den Beitragsleitfaden des Projekts.
  2. Schreiben Sie ins Issue, dass Sie es übernehmen — das erspart doppelte Arbeit.
  3. Forken Sie das Repository und arbeiten Sie in einem Branch.
  4. Öffnen Sie einen Pull Request, der die Issue-Nummer nennt.

Mehr aus braintrustdata/braintrust-sdk-python

Alle Issues in braintrustdata/braintrust-sdk-python

Ähnliche Issues

Weitere Issues zu Python

Neue Issues direkt in Ihr Postfach

Eine kurze Übersicht über anfängerfreundliche GitHub-Issues.