> ## Documentation Index
> Fetch the complete documentation index at: https://simplellmfunc.cn/llms.txt
> Use this file to discover all available pages before exploring further.

# Quickstart

> From zero to a working LLM function call in 5 minutes

# Quickstart

## Install

```bash theme={null}
pip install SimpleLLMFunc
```

Or with Poetry:

```bash theme={null}
poetry add SimpleLLMFunc
```

## Configure a Model

Create `provider.json` in your project root:

```json theme={null}
{
  "openrouter": [
    {
      "model_name": "openai/gpt-4o",
      "api_keys": ["sk-your-key"],
      "base_url": "https://openrouter.ai/api/v1",
      "max_retries": 3,
      "retry_delay": 1.0,
      "rate_limit_capacity": 20,
      "rate_limit_refill_rate": 3.0
    }
  ]
}
```

This works with any OpenAI-compatible endpoint — OpenRouter, Together, local vLLM, etc.

## Your First LLM Function

```python theme={null}
import asyncio
from pydantic import BaseModel, Field
from SimpleLLMFunc import OpenAICompatible, llm_function


class Summary(BaseModel):
    headline: str = Field(description="One-sentence summary")
    key_points: list[str] = Field(description="3-5 bullet points")
    tone: str = Field(description="formal, casual, or technical")


models = OpenAICompatible.load_from_json_file("provider.json")
llm = models["openrouter"]["openai/gpt-4o"]


@llm_function(llm_interface=llm)
async def summarize(text: str) -> Summary:
    """
    Summarize the input text concisely.
    Focus on the most actionable information.
    """
    pass


async def main():
    result = await summarize("SimpleLLMFunc treats every LLM call as a typed Python function...")
    print(result.headline)
    print(result.key_points)


asyncio.run(main())
```

That's it. The decorator:

1. Builds a system prompt from the docstring + parameter types + return type schema
2. Sends the function arguments as the user message
3. Parses the LLM response into your Pydantic model

No message lists, no JSON mode flags, no output parsers.

## Direct Construction (No provider.json)

For quick scripts, construct the model directly:

```python theme={null}
from SimpleLLMFunc import APIKeyPool, OpenAICompatible

llm = OpenAICompatible(
    api_key_pool=APIKeyPool(
        api_keys=["sk-your-key"],
        provider_id="openai",
    ),
    model_name="gpt-4o",
    base_url="https://api.openai.com/v1",
)
```

## What's Next

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    Add tools and see the ReAct loop in action.
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    History management, streaming, and stateful conversations.
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