Observability and Evaluation · Public

guardrails

Observability and evaluation project for turning logs, quality metrics, drift, or experiment results into reviewable signals.

ObservabilityEvaluationQuality metricsData driftExperiment tracking

Best fitDevelopers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.

Check whether this project matches your task before installing it.

What it can doObservability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage

Review the portable capability path.

Before continuingVerify in a sandbox

Do not treat a preview pack as a proven local install.

GitHub snapshot6.8k stars

599 forks · 77 contributors

Official first step Read manual preview Source repository

Publication status · 2026-05-25

What is guardrails?

guardrails helps developers observe, evaluate, or monitor AI/data application behavior and quality.
Best fit: Developers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.
Not for: Not for users without logs/sample data, privacy boundaries, or those who only need a chat UI.
Capability added to an AI workflow: Observability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage
First safe verification step: Verify collection, metric interpretation, export, and deletion paths with redacted sample data first.
Verification state: source, Quick Start, and sandbox install checks are recorded as passed.
Top risk: The main risk is sending sensitive logs, user data, or misleading metrics into production observability.
Evidence base: https://github.com/guardrails-ai/guardrails, https://github.com/guardrails-ai/guardrails#readme, Human Manual, Pitfall Log

Quick decision

Use this section to decide whether the project is worth a deeper read.

Best forDevelopers who need reviewable observability or evaluation workflows for AI apps, data pipelines, or experiments.

Match the project to your task before installing it.

CapabilityObservability setup paths, metric boundaries, sample-data redaction, evaluation checks, and failure triage

Observability and evaluation project for turning logs, quality metrics, drift, or experiment results into reviewable signals.

Repositoryguardrails-ai/guardrails

6.8k stars · 599 forks

What it can do

Translate the upstream project into concrete capabilities the user can judge before installing.

Getting Started with Guardrails

Related topics: Guard Class Reference, Validators System

Sources: [README.md](https://github.com/guardrails-ai/guardrails/blob/main/README.md), [CONTRIBUTING.md](https://github.com/guardrails-ai/guardrails/blob/main/CONTRIBUTING.md)

System Architecture

Related topics: Getting Started with Guardrails, Execution Pipeline

Sources: [README.md:1-20]()

Guard Class Reference

Related topics: Validators System, Schema Processing

Sources: [guardrails/guard.py:from_rail-method](https://github.com/guardrails-ai/guardrails/blob/main/guardrails/guard.py)

Validators System

Related topics: Guard Class Reference, Creating Custom Validators

Sources: [guardrails/validator_service/validator_service_base.py](https://github.com/guardrails-ai/guardrails/blob/main/guardrails/validator_service/validator_service_base.py)

Schema Processing

Related topics: Guard Class Reference, Execution Pipeline

Sources: [guardrails/schema/rail_schema.py:1-50]()

Sources: https://github.com/guardrails-ai/guardrails, Human Manual, Project Pack evidence, and downstream validation signals.

Community Discussion Evidence

Project-level external discussion stays visible on the detail page, not only inside the manual.

Stars6.8k stars

Forks599 forks

Contributors77 contributors

Licenseunknown

Community Discussion Evidence

12 source-linked items

Review these external discussions before using guardrails with real data or production workflows. They are review inputs, not standalone proof that the project is production-ready.

01
Feature request: OWASP ASI06 memory poisoning guard validator
github / github_issue
02
Proposal: Agent Threat Rules detection integration
github / github_issue
03
Best-practice: litellm pin excludes patched CVE versions, unverified-jwt
github / github_issue
04
Lift litellm <1.82.6 pin to allow post-incident safe releases (1.83+)
github / github_issue
05
[SECURITY] Supply Chain Compromise in guardrails-ai v0.10.1 on PyPI
github / github_issue
06
[bug] 429 Rate Limit Error from Opting into Metrics
github / github_issue
07
[bug] Failures and Delayed Responses from guard.validate Method Using Di
github / github_issue
08
[feat] Attempt to repair JSON before triggering NonParseableReAsk
github / github_issue
09
Feature Request: OWASP ASI06 memory write validation via Agent Memory Gu
github / github_issue
10
FIX action silently mutates output - should there be a 'quarantine' tier between LOG and FIX?
GitHub / issue
11
Portable evidence artifacts for validation outcomes
GitHub / issue
12
Proposal: PromptDefenseAudit Hub validator — static system prompt hardening check
GitHub / issue

How to start

Only source-backed commands are shown here. Verify them in an isolated environment first.

Try the prompt first

Test the workflow without installing the upstream project.

preview

Read the Human Manual

Understand inputs, outputs, limits, and failure modes.

manual

Take context to your AI host

Use the compiled assets in your preferred AI environment.

context

Run sandbox verification

Confirm install commands and rollback before using a primary environment.

verify

pip install guardrails-ai

Official start command · https://github.com/guardrails-ai/guardrails#readme · verified: yes

Human Manual

The English page must expose the real manual, not a short placeholder.

8+ sections · Human Manual

guardrails Manual

Guardrails is an open-source Python library that provides validation, correction, and structural guarantees for AI/LLM applications. It enables developers to define constraints on LLM outp...

Open the full manual

guardrails Human Manual
Table of Contents
Getting Started with Guardrails
Related Pages
Overview
Installation
Prerequisites
Standard Installation

Getting Started with Guardrails

Related topics: Guard Class Reference, Validators System

Sources: [README.md](https://github.com/guardrails-ai/guardrails/blob/main/README.md), [CONTRIBUTING.md](https://github.com/guardrails-ai/guardrails/blob/main/CONTRIBUTING.md)

System Architecture

Related topics: Getting Started with Guardrails, Execution Pipeline

Sources: [README.md:1-20]()

Guard Class Reference

Related topics: Validators System, Schema Processing

Sources: [guardrails/guard.py:from_rail-method](https://github.com/guardrails-ai/guardrails/blob/main/guardrails/guard.py)

Validators System

Related topics: Guard Class Reference, Creating Custom Validators

Sources: [guardrails/validator_service/validator_service_base.py](https://github.com/guardrails-ai/guardrails/blob/main/guardrails/validator_service/validator_service_base.py)

Schema Processing

Related topics: Guard Class Reference, Execution Pipeline

Sources: [guardrails/schema/rail_schema.py:1-50]()

AI Context Pack and portable assets

After deciding to continue, take the project context into your own AI host.

Complete pack plus user-owned assets

These files are planning and verification assets for Claude Code, Codex, Gemini, Cursor, ChatGPT, and other AI hosts.

Download complete pack Read Human Manual

BundleComplete Project Pack AssetAI Context Pack AssetBoundary & Risk Card AssetHuman Manual AssetPitfall Log AssetPrompt Preview AssetQuick Start ManifestPROJECT_PACK.json

Preflight checks

Treat this page as a planning asset, not proof that your local environment is ready.

The manual is generated from source-linked project files and Doramagic validation signals.
Community evidence warnings stay visible instead of being converted into marketing claims.
This English page is indexable because the locale quality gate passed and explicit English index approval is enabled.
Use the upstream repository as the final authority for installation commands, license, and version-specific behavior.