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Commitdbc9af1

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Update UI links, remove some TS / PR 2
Updated UI links, removed some TS
2 parents091a665 +6ed2c14 commitdbc9af1

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‎README.md‎

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#OpenAI Guardrails
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>**Choose your language:**
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>-[Python](#python)
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>-[TypeScript](#typescript)
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---
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#Python
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##Overview
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OpenAI Guardrails is a Python package for adding robust, configurable safety and compliance guardrails to LLM applications. It provides a drop-in wrapper for OpenAI's Python client, enabling automatic input/output validation and moderation using a wide range of guardrails.
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##Documentation
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For full details, advanced usage, and API reference, see here:[OpenAI GuardrailsAlphaDocumentation](https://oaig-whisper-yonder-xnjpy2.vercel.app/docs/).
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For full details, advanced usage, and API reference, see here:[OpenAI Guardrails Documentation](https://openai.github.io/openai-guardrails-python/).
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##Quick Start: Using OpenAI Guardrails (Python)
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1.**Generate your guardrail spec JSON**
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- Use the[Guardrails web UI](https://oaig-whisper-yonder-xnjpy2.vercel.app/) (pw:guardrails) to create a JSON configuration file describing which guardrails to apply and how to configure them.
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- Use the[Guardrails web UI](https://platform.openai.com/guardrails) to create a JSON configuration file describing which guardrails to apply and how to configure them.
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- The wizard outputs a file like`guardrail_specs.json`.
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2.**Install dependencies**
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- The client will automatically apply all configured guardrails to inputs and outputs.
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- If a guardrail is triggered, a`GuardrailTripwireTriggered` exception will be raised. You should handle this exception to gracefully manage blocked or flagged content.
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>**Note:** The Guardrails web UI(in`frontend/`)is hosted [here](https://oaig-whisper-yonder-xnjpy2.vercel.app/). Youdo not need to run the web UI yourself to use the Python package.
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>**Note:** The Guardrails web UI is hosted [here](https://platform.openai.com/guardrails). Youdo not need to run the web UI yourself to use the Python package.
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---
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---
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# TypeScript
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## Overview
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Guardrails TypeScript is a Node.js/TypeScript framework for secure AI calls with OpenAI Guardrails. It provides a port of the Python Guardrails framework with enhanced type safety, Node.js integration, and a high-level client API mirroring the Python version.
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> **Note:** The TypeScript package is currently in development and not yet published to npm. Use the local installation methods below.
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## Quick Start: Using OpenAI Guardrails (TypeScript)
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### 1. Install Locally
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Clone the repository and install dependencies:
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```bash
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# From the root of the repo
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cd guardrails-ts
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npm install
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npm run build
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```
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You can also install it into your own project from the local path:
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```bash
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npm install /absolute/path/to/guardrails/guardrails-ts
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```
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### 2. Using the Guardrails Client in TypeScript
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The TypeScript client now provides a high-level API similar to Python:
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```typescript
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import { GuardrailsOpenAI } from'@guardrails/guardrails-ts';
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import * as path from'path';
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// Create a GuardrailsOpenAI client with your config
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const client = await GuardrailsOpenAI.create(path.resolve('guardrails_config.json'), {
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apiKey: process.env.OPENAI_API_KEY,
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});
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try {
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const response = await client.chat.completions.create({
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model:'gpt-5',
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messages: [{ role:'user', content:'...' }],
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});
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console.log(response.llm_response.choices[0].message.content);
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} catch (e) {
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// Handle blocked or flagged content
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console.error('Guardrail triggered:', e);
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}
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// Example: Using the new OpenAI Responses API with Guardrails
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try {
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const resp = await client.responses.create({
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model:'gpt-5',
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input:'...',
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// Optionally, add file_search or other tool arguments as needed
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});
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console.log(resp.llm_response.output_text);
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} catch (e) {
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console.error('Guardrail triggered (responses API):', e);
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}
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```
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### 3. CLI Usage
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You can also use the CLI for validation and evaluation:
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```bash
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# Run directly with npm (from guardrails-ts directory)
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npm run cli -- --help
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npm run cli -- validate config.json
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npm run cli -- eval --config-path config.json --dataset-path dataset.jsonl
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# Or install globally for CLI access
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npm install -g .
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guardrails-ts --help
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guardrails-ts validate config.json
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guardrails-ts eval --config-path config.json --dataset-path dataset.jsonl
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```
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### 4. Running Examples
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```bash
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# Build the package first
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npm run build
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# Run example scripts (from guardrails-ts/examples)
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cd examples
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npx tsx simple-runtime-usage.ts
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npx tsx guardrails-demo.ts
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```
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---
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## What Does the TypeScript Package Provide?
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- **GuardrailsOpenAI** and **GuardrailsAzureOpenAI**: Drop-in replacements for OpenAI's`OpenAI` and`AzureOpenAI` clients, with automatic guardrail enforcement (mirrors Python API).
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-**Automatic input/output validation**: Guardrails are applied to all relevant API calls (e.g.,`chat.completions.create`,`responses.create`, etc.).
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-**Configurable guardrails**: Choose which checks to enable, and customize their parameters via the JSON spec.
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-**Tripwire support**: Optionally block or mask unsafe content, or just log/flag itfor review.
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-**CLI tool**: Validate configs, run evaluations, and more from thecommand line.
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-**Evaluation framework**: Test guardrail performance on datasets and measure metrics like precision, recall, and F1 scores.
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---
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## Available Guardrails
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Below is a list of all built-in guardrails you can configure. Each can be enabled/disabled and customized in your JSON spec.
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## License
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For the duration of this early access alpha, `guardrails`(including both the Python and TypeScript packages)is distributed under the Alpha Evaluation Agreement that your organization signed with OpenAI.
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For the duration of this early access alpha,`guardrails` is distributed under the Alpha Evaluation Agreement that your organization signed with OpenAI.
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Both thePythonand TypeScript packages are intended to be MIT-licensed in the future, subject to change.
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ThePythonpackage is intended to be MIT-licensedin the future, subject to change.
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## Disclaimers
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‎docs/agents_sdk_integration.md‎

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##Next Steps
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- Use the[Guardrails Wizard](https://guardrails-vercel-git-main-openai.vercel.app/guardrails) to generate your configuration
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- Use the[Guardrails Wizard](https://platform.openai.com/guardrails) to generate your configuration
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- Explore available guardrails for your use case
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- Learn about pipeline configuration in our[quickstart](./quickstart.md)
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- For more details on the OpenAI Agents SDK, refer to the[Agent SDK documentation](https://openai.github.io/openai-agents-python/).

‎docs/evals.md‎

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--models gpt-5 gpt-5-mini gpt-5-nano
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```
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Test with included demo files in our[github repository](https://github.com/OpenAI-Early-Access/guardrails/tree/main/src/guardrails/evals/eval_demo)
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Test with included demo files in our[github repository](https://github.com/openai/openai-guardrails-python/tree/main/src/guardrails/evals/eval_demo)
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##Dependencies
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The evals tool requires a few extra packages that are not installed with the core`guardrails` package. Please install the following before running evaluations or benchmarks:
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###Benchmark Mode
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When running benchmark mode (ROC curves, precision at recall thresholds, visualizations), you need additional packages:
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```bash
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pip installnumpy pandas matplotlib seaborn scikit-learn
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pip install"guardrails[benchmark]"
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These dependencies are only needed for the evals and benchmarking workflows (metrics and visualization), not for using`guardrails` at runtime.
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This installs:
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-`numpy>=1.24.0` - Core scientific computing
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-`scikit-learn>=1.3.0` - Machine learning metrics
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-`matplotlib>=3.7.0` - Visualization
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-`seaborn>=0.12.0` - Statistical plots
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-`pandas>=2.0.0` - Data processing
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**Note**: These dependencies are only needed for benchmarking workflows (metrics calculation and visualization), not for basic evaluation mode.
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##Arguments
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Export a configuration from the Guardrails Wizard UI and pass its path via`--config-path`.
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- Open the[Wizard UI](https://oaig-whisper-yonder-xnjpy2.vercel.app/guardrails)
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- Open the[Wizard UI](https://platform.openai.com/guardrails)
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- Configure the guardrails you want to evaluate
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- Use Export to download the config file (JSON)
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- Run the evaluator with`--config-path /path/to/exported_config.json`
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##Next Steps
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- See the[API Reference](./ref/eval/guardrail_evals.md) for detailed documentation
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- Use[Wizard UI](https://oaig-whisper-yonder-xnjpy2.vercel.app/guardrails) for configuring guardrails without code
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- Use[Wizard UI](https://platform.openai.com/guardrails) for configuring guardrails without code

‎docs/examples.md‎

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#Examples
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Ready-to-run examples demonstrating Guardrails in various scenarios. See the[`guardrails/examples/`](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/) folder for complete implementations.
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Ready-to-run examples demonstrating Guardrails in various scenarios. See the[`guardrails/examples/`](https://github.com/openai/openai-guardrails-python/tree/main/examples/) folder for complete implementations.
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##Example Implementations
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-[hello_world.py](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/basic/hello_world.py) — Basic pipeline configuration with input/output guardrails
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-[agents_sdk.py](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/basic/agents_sdk.py) — Integration with OpenAI Agents SDK
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-[pii_mask_example.py](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/basic/pii_mask_example.py) — PII detection and scrubbing
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-[structured_outputs_example.py](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/basic/structured_outputs_example.py) — Using responses.parse with guardrails
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-[Streaming (fast)](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/implementation_code/fast) — Stream output while guardrails run
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-[Blocking (slow)](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/implementation_code/slow) — Validate fully before showing output
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-[hello_world.py](https://github.com/openai/openai-guardrails-python/tree/main/examples/basic/hello_world.py) — Basic pipeline configuration with input/output guardrails
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-[agents_sdk.py](https://github.com/openai/openai-guardrails-python/tree/main/examples/basic/agents_sdk.py) — Integration with OpenAI Agents SDK
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-[pii_mask_example.py](https://github.com/openai/openai-guardrails-python/tree/main/examples/basic/pii_mask_example.py) — PII detection and scrubbing
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-[structured_outputs_example.py](https://github.com/openai/openai-guardrails-python/tree/main/examples/basic/structured_outputs_example.py) — Using responses.parse with guardrails
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-[Streaming](https://github.com/openai/openai-guardrails-python/tree/main/examples/implementation_code/streaming) — Stream output while guardrails run
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-[Blocking](https://github.com/openai/openai-guardrails-python/tree/main/examples/implementation_code/blocking) — Validate fully before showing output
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##Hallucination Detection Example
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Complete implementation using real documents as knowledge sources:
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[`examples/hallucination_detection/`](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/hallucination_detection)
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[`examples/hallucination_detection/`](https://github.com/openai/openai-guardrails-python/tree/main/examples/hallucination_detection)
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##Getting Started
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1. Follow the Quickstart guide:[Python](./quickstart.md)
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2. Explore repositories:[Python examples](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/)
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2. Explore repositories:[Python examples](https://github.com/openai/openai-guardrails-python/tree/main/examples/)
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3. Run scripts to see Guardrails in action
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Each example is self-contained with clear configuration and usage patterns.

‎docs/index.md‎

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#Guardrails
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**Guardrails** is a safety framework for LLM applications that automatically validates inputs and outputs using configurable checks. Use the[Guardrails Wizard](https://guardrails-vercel-git-main-openai.vercel.app/guardrails) to create configurations, then drop in our client classes for automatic validation.
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**Guardrails** is a safety framework for LLM applications that automatically validates inputs and outputs using configurable checks. Use the[Guardrails Wizard](https://platform.openai.com/guardrails) to create configurations, then drop in our client classes for automatic validation.
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![Guardrails Wizard](assets/images/guardrails_wizard_screenshot.png)
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-[Quickstart](./quickstart.md) - Full quickstart guide
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-[Examples](./examples.md) - See real implementations
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-[Guardrails Wizard](https://guardrails-vercel-git-main-openai.vercel.app/guardrails) - Create configurations visually
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-[Guardrails Wizard](https://platform.openai.com/guardrails) - Create configurations visually
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##Disclaimers
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‎docs/quickstart.md‎

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##Create Pipeline Configuration
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The fastest way is using the[Guardrails Wizard](https://guardrails-vercel-git-main-openai.vercel.app/guardrails) - a no-code tool for creating configurations.
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The fastest way is using the[Guardrails Wizard](https://platform.openai.com/guardrails) - a no-code tool for creating configurations.
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Or define manually:
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‎docs/ref/checks/hallucination_detection.md‎

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###Complete Example
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See[`examples/hallucination_detection/`](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/hallucination_detection) for the full implementation.
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See[`examples/hallucination_detection/`](https://github.com/openai/openai-guardrails-python/tree/main/examples/hallucination_detection) for the full implementation.
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###Notes
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‎docs/streaming_output.md‎

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See complete examples:
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-[Non-streaming (safe)](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/implementation_code/slow)
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-[Streaming (fast)](https://github.com/OpenAI-Early-Access/guardrails/tree/main/guardrails/examples/implementation_code/fast)
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-[Non-streaming (blocking)](https://github.com/openai/openai-guardrails-python/tree/main/examples/implementation_code/blocking)
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-[Streaming](https://github.com/openai/openai-guardrails-python/tree/main/examples/implementation_code/streaming)

‎examples/implementation_code/slow/slow_completions.py‎renamed to ‎examples/implementation_code/blocking/blocking_completions.py‎

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"""Minimal non-streaming implementation with guardrails using the new GuardrailsClient."""
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"""Minimal non-streaming(blocking)implementation with guardrails using the new GuardrailsClient."""
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# Please integrate openai-guardrails into my LLM calls.
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