> For the complete documentation index, see [llms.txt](https://docs.fastrouter.ai/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.fastrouter.ai/integrations/app/instructor.md).
# Instructor
### What is Instructor?
[Instructor](https://python.useinstructor.com/) is a library for getting structured, validated outputs from LLMs. It patches the OpenAI client so you can request a Pydantic model as the response type and get back a validated object, with automatic retries on validation failure.
By routing Instructor through FastRouter, you get:
* **100+ models** from OpenAI, Anthropic, Google, xAI, Meta, Groq, Mistral, and more through one endpoint—compare which model produces the most reliable structured outputs
* **Observability** for every request: cost, tokens, latency, and model selection tracked in real time
* **Reliability** through automatic failover across providers, response caching, and intelligent routing
* **Governance** with per-key budgets, rate limits, model restrictions, role-based access, and project isolation
This guide covers connecting Instructor (Python) to FastRouter by patching an OpenAI client pointed at FastRouter.
**Prerequisites**
* A FastRouter.ai account ([sign up](https://fastrouter.ai))
* Python 3.9 or higher
***
#### Quick Start
**Step 1: Create a Project and Virtual Environment**
You'll only need to do this once:
```bash
mkdir my_project
cd my_project
python -m venv .venv
```
Activate the virtual environment. Do this every time you start a new terminal session.
On macOS or Linux:
```bash
source .venv/bin/activate
```
On Windows:
```bash
.venv\Scripts\activate
```
**Step 2: Install Instructor**
```bash
pip install instructor
```
**Step 3: Get Your FastRouter API Key**
1. Sign up or log in at [fastrouter.ai](https://fastrouter.ai)
2. Navigate to your project's **Keys** page
3. Click **Create User Key**
4. Copy the key immediately. FastRouter does not display the key again after creation.
Export it in your terminal:
```bash
export FASTROUTER_API_KEY=sk-add-your-key-here
```
**Step 4: Patch an OpenAI Client Pointed at FastRouter**
Create a standard OpenAI client with FastRouter's base URL, then patch it with Instructor. Save this as `instructor_example.py`:
```python
import os
import instructor
from openai import OpenAI
from pydantic import BaseModel
client = instructor.from_openai(
OpenAI(
base_url="https://api.fastrouter.ai/api/v1",
api_key=os.environ["FASTROUTER_API_KEY"],
)
)
class CityInfo(BaseModel):
city: str
country: str
population: int
result = client.chat.completions.create(
model="openai/gpt-5.2",
response_model=CityInfo,
messages=[{"role": "user", "content": "Tell me about Tokyo."}],
)
print(result)
```
**Step 5: Run the Script**
```bash
python instructor_example.py
```
You get back a validated `CityInfo` object. The request appears in your [FastRouter Dashboard](https://dashboard.fastrouter.ai/) with token usage and cost.
***
#### Use Instructor with 100+ Models
FastRouter uses the `provider/model-name` format. Switch providers by changing the `model` argument—useful for finding which model produces the most reliable structured outputs:
```python
result = client.chat.completions.create(
model="anthropic/claude-4.5-sonnet",
response_model=CityInfo,
messages=[{"role": "user", "content": "Tell me about Tokyo."}],
)
```
[Explore the full model catalog](https://fastrouter.ai/models)
**Automatic Model Selection**
Let FastRouter pick the best model for each request based on query complexity, domain, and cost:
```python
model="fastrouter/auto"
```
[Explore automatic model selection](https://docs.fastrouter.ai/automatic-model-selection)
***
#### FAQs
**Configuration & Setup**
**Can I use multiple models with the same API key?**
Yes. The API key controls access and budget. Pass a different `model` on each call, all sharing one key.
**Can I restrict a key to only use specific models?**
Yes. When creating or editing a key, use the **Select Models** setting to limit which models the key can access. FastRouter rejects requests to unauthorized models.
**Structured Outputs**
**My calls hit Instructor's retry limit. How do I fix it?**
Validation reliability varies by model. Try a more capable model, simplify the schema, or add field descriptions. Because FastRouter gives you every provider through one key, comparing models on your schema takes minutes.
**Performance & Reliability**
**Does FastRouter add latency?**
FastRouter adds near-zero gateway overhead, negligible compared to model inference time.
***
#### Next Steps
* [Explore the full model catalog](https://fastrouter.ai/models)
* [Set up Fallback Models](https://docs.fastrouter.ai/fallback-models) for high availability
* [Configure Alerts](https://docs.fastrouter.ai/alerts) for spend and performance monitoring
* [Run a Free Audit](https://fastrouter.ai/audit) on your existing LLM traffic to identify savings
* [Join the Discord community](https://discord.gg/QfTgEtMyyU)
---
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