diff --git a/.github/workflows/deploy.yml b/.github/workflows/deploy.yml
new file mode 100644
index 0000000..2d88187
--- /dev/null
+++ b/.github/workflows/deploy.yml
@@ -0,0 +1,43 @@
+name: Deploy to GitHub Pages
+
+on:
+ push:
+ branches:
+ - main
+ paths:
+ - 'docs/**'
+
+jobs:
+ deploy:
+ runs-on: ubuntu-latest
+ permissions:
+ contents: write
+ defaults:
+ run:
+ working-directory: docs
+
+ steps:
+ - uses: actions/checkout@v4
+
+ - name: Setup Node.js
+ uses: actions/setup-node@v4
+ with:
+ node-version: '20'
+ cache: 'npm'
+ cache-dependency-path: docs/package-lock.json
+
+ - name: Install dependencies
+ run: npm ci
+
+ - name: Build
+ run: npm run build
+
+ - name: Deploy to GitHub Pages
+ uses: peaceiris/actions-gh-pages@v3
+ with:
+ github_token: ${{ secrets.GITHUB_TOKEN }}
+ publish_dir: ./docs/dist
+ force_orphan: true
+ user_name: 'github-actions[bot]'
+ user_email: 'github-actions[bot]@users.noreply.github.com'
+ commit_message: 'Deploy documentation to GitHub Pages'
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index c50bc18..53319da 100644
--- a/.gitignore
+++ b/.gitignore
@@ -8,3 +8,5 @@ __pycache__/
.pytest_cache
**.egg-info
dist/
+.venv/
+node_modules/
\ No newline at end of file
diff --git a/docs/.gitignore b/docs/.gitignore
new file mode 100644
index 0000000..01f41fa
--- /dev/null
+++ b/docs/.gitignore
@@ -0,0 +1,2 @@
+.next/
+.DS_Store
diff --git a/docs/app/[[...mdxPath]]/page.jsx b/docs/app/[[...mdxPath]]/page.jsx
new file mode 100644
index 0000000..72db16f
--- /dev/null
+++ b/docs/app/[[...mdxPath]]/page.jsx
@@ -0,0 +1,23 @@
+import { generateStaticParamsFor, importPage } from 'nextra/pages'
+import { useMDXComponents as getMDXComponents } from '../../mdx-components'
+
+export const generateStaticParams = generateStaticParamsFor('mdxPath')
+
+export async function generateMetadata(props) {
+ const params = await props.params
+ const { metadata } = await importPage(params.mdxPath)
+ return metadata
+}
+
+const Wrapper = getMDXComponents().wrapper
+
+export default async function Page(props) {
+ const params = await props.params
+ const result = await importPage(params.mdxPath)
+ const { default: MDXContent, toc, metadata } = result
+ return (
+
+
+
+ )
+}
diff --git a/docs/app/layout.jsx b/docs/app/layout.jsx
new file mode 100644
index 0000000..5ca0fa4
--- /dev/null
+++ b/docs/app/layout.jsx
@@ -0,0 +1,47 @@
+import { Footer, Layout, Navbar } from 'nextra-theme-docs'
+import { Banner, Head } from 'nextra/components'
+import { getPageMap } from 'nextra/page-map'
+import 'nextra-theme-docs/style.css'
+
+export const metadata = {
+ // Define your metadata here
+ // For more information on metadata API, see: https://nextjs.org/docs/app/building-your-application/optimizing/metadata
+}
+
+const navbar = (
+ AI SDK - Python}
+ // ... Your additional navbar options
+ />
+)
+const footer =
+
+export default async function RootLayout({ children }) {
+ return (
+
+
+ {/* Your additional tags should be passed as `children` of `` element */}
+
+
+
+ {children}
+
+
+
+ )
+}
\ No newline at end of file
diff --git a/docs/content/_meta.js b/docs/content/_meta.js
new file mode 100644
index 0000000..0cf63ca
--- /dev/null
+++ b/docs/content/_meta.js
@@ -0,0 +1,15 @@
+import { GitHubIcon } from 'nextra/icons'
+
+export default {
+ index: {
+ title: 'AI SDK',
+ type: 'doc'
+ },
+ guides: 'Guides',
+ providers: 'Providers'
+}
+
+// Custom component for italicized text
+function Italic({ children, ...props }) {
+ return {children}
+}
\ No newline at end of file
diff --git a/docs/content/guides/_meta.js b/docs/content/guides/_meta.js
new file mode 100644
index 0000000..091077c
--- /dev/null
+++ b/docs/content/guides/_meta.js
@@ -0,0 +1,6 @@
+export default {
+ overview: 'Overview',
+ models: 'Models and Providers',
+ text_generation: 'Text Generation',
+ structured_outputs: 'Structured Outputs',
+}
\ No newline at end of file
diff --git a/docs/content/guides/models.mdx b/docs/content/guides/models.mdx
new file mode 100644
index 0000000..0b5bce8
--- /dev/null
+++ b/docs/content/guides/models.mdx
@@ -0,0 +1,87 @@
+import { Tabs } from 'nextra/components'
+
+# Models and Providers
+
+The Python AI SDK allowss you to call many different LLM models through a unified interface which makes it easy
+to switch between models.
+
+The following providers are currently supported:
+
+* [OpenAI](https://platform.openai.com/docs/guides/text)
+* [Anthropic](https://docs.anthropic.com/en/docs/welcome)
+* [OpenRouter](https://openrouter.ai/docs/quickstart)
+
+## Using a model
+
+In order to use a model from a specific provider, you will first need to import the provider and then initialize the model which
+can then be used within the `generate_text` and `generate_object` functions.
+
+
+
+ ```python
+ from ai_sdk import generate_text
+ from ai_sdk.openai import openai
+
+ response = generate_text(
+ model=openai("gpt-4o"),
+ prompt="Hello, world!"
+ )
+
+ print(response.text)
+ ```
+
+
+ ```python
+ from ai_sdk import generate_text
+ from ai_sdk.anthropic import anthropic
+
+ response = generate_text(
+ model=anthropic("claude-3-5-sonnet-20240620"),
+ prompt="Hello, world!"
+ )
+
+ print(response.text)
+ ```
+
+
+ ```python
+ from ai_sdk import generate_text
+ from ai_sdk.openrouter import openrouter
+
+ response = generate_text(
+ model=openrouter("openai/gpt-4o"),
+ prompt="Hello, world!"
+ )
+
+ print(response.text)
+ ```
+
+
+
+## Advanced Usage
+
+### Customizing provider settings
+
+You can customize the provider settings by utilizing the `create__provider` function. This allows you to
+pass in additional settings like API keys, base URLs, etc. For example, if you want to use the OpenAI provider with a
+custom base URL, you can do the following:
+
+```python
+from ai_sdk.openai import create_openai_provider, OpenAIProviderSettings
+
+provider = create_openai_provider(
+ settings=OpenAIProviderSettings(
+ base_url="https://api.openai.com",
+ api_key="your_api_key"
+ )
+)
+
+response = generate_text(
+ model=provider("gpt-4o"),
+ prompt="Hello, world!"
+)
+
+print(response.text)
+```
+
+For a full list of available settings for each provider, please refer to each provider's documentation.
\ No newline at end of file
diff --git a/docs/content/guides/overview.mdx b/docs/content/guides/overview.mdx
new file mode 100644
index 0000000..829c310
--- /dev/null
+++ b/docs/content/guides/overview.mdx
@@ -0,0 +1,62 @@
+import { Cards } from 'nextra/components'
+import { FiMessageSquare, FiGrid, FiTool } from 'react-icons/fi'
+import { Callout } from 'nextra/components'
+
+# Overview
+
+The AI SDK was heavily inspired by the [AI SDK](https://sdk.vercel.ai/) built by the Vercel team. This SDK is
+a partial port of that JavaScript SDK to Python.
+
+## Installation
+
+```bash
+pip install ai-sdk-python
+```
+
+## Usage
+
+The AI SDK is designed to make it easy to work with different AI providers whether that is to generate text
+or structured outputs. All you need to do is import the library and call the `generate_text` function to get
+started:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.openai import openai
+
+response = generate_text(
+ model=openai("gpt-4o"),
+ prompt="Hello, world!"
+)
+
+print(response.text)
+```
+
+You can learn more about using the AI SDK in our guides:
+
+
+ }
+ title="Prompts"
+ href="/guides/prompts"
+ arrow
+ />
+ }
+ title="Structured Outputs"
+ href="/guides/structured-outputs"
+ arrow
+ />
+ }
+ title="Tools"
+ href="/guides/tools"
+ arrow
+ />
+
+
+You can also find a full list of all the providers we support
+in the Providers section.
+
+
+ If you would like us to support a new provider, please reach out to us on [Github](https://github.com/jverre/ai-sdk/issues).
+
diff --git a/docs/content/guides/structured_outputs.mdx b/docs/content/guides/structured_outputs.mdx
new file mode 100644
index 0000000..7a110b6
--- /dev/null
+++ b/docs/content/guides/structured_outputs.mdx
@@ -0,0 +1,220 @@
+import { Steps, Tabs, Callout } from 'nextra/components'
+
+# Structured Outputs
+
+While LLMs are great at generating text, it can be useful to get structured outputs instead. The AI
+SDK provides a `generate_object` function that allows you to do just that, you provide a schema and
+a prompt and the LLM will generate an object that matches the schema.
+
+The `generate_object` function will choose the best way to generate the object based on
+what is supported by the provider you are using. This includes:
+
+1. Explicit JSON support: OpenAI supports passing in a JSON schema that constrains the output of
+the model.
+2. Tool calls: Tool calls rely on specific argument formats, this can be abused to generate structured
+outputs.
+3. Text mode: If none of the above are supported, we will default back to prompting the LLM to
+answer the prompts in a structured way. This is the least reliable method and can often lead to parsing
+errors.
+
+## Basic Usage
+
+To generate structured output, you need to:
+1. Define your output structure using Pydantic models
+2. Use the `generate_object` function with your model
+
+Here's a simple example:
+
+```python
+from pydantic import BaseModel
+from ai_sdk import generate_object
+from ai_sdk.openai import openai
+
+class WeatherResponse(BaseModel):
+ temperature: float
+ condition: str
+ humidity: int
+ wind_speed: float
+
+response = generate_object(
+ model=openai("gpt-4"),
+ schema=WeatherResponse,
+ prompt="What's the weather like in Paris today?"
+)
+
+print(response.object.temperature) # e.g., 22.5
+print(response.object.condition) # e.g., "sunny"
+```
+
+## Understanding the Response
+
+The `generate_object` function returns an `ObjectResult` that contains:
+
+```python
+class ObjectResult:
+ object: BaseModel # Your structured data
+ finish_reason: FinishReason # Why generation stopped
+ usage: Usage # Token usage statistics
+ request: RequestMetadata # Request information
+ response: ResponseMetadata # Response information
+ warnings: List[Warning] # Any warnings generated
+```
+
+
+
+ ```python
+ class MovieReview(BaseModel):
+ title: str
+ rating: int
+ review: str
+
+ response = generate_object(
+ model=openai("gpt-4"),
+ output_model=MovieReview,
+ prompt="Review the movie 'Inception'"
+ )
+ print(f"Rating: {response.object.rating}/10")
+ print(response.usage.total_tokens) # Token usage
+ ```
+
+
+ ```python
+ from pydantic import BaseModel, Field
+
+ class Rating(BaseModel):
+ score: int = Field(ge=1, le=10) # Between 1-10
+ review: str = Field(min_length=10)
+
+ response = generate_object(
+ model=openai("gpt-4"),
+ schema=Rating,
+ prompt="Rate this product"
+ )
+ # Will automatically validate score is 1-10
+ # and review is at least 10 chars
+ ```
+
+
+ ```python
+ from typing import List
+
+ class Character(BaseModel):
+ name: str
+ age: int
+ skills: List[str]
+
+ class Story(BaseModel):
+ title: str
+ genre: str
+ characters: List[Character]
+ plot_summary: str
+
+ response = generate_object(
+ model=openai("gpt-4"),
+ schema=Story,
+ prompt="Create a sci-fi story"
+ )
+ for char in response.object.characters:
+ print(f"{char.name}: {', '.join(char.skills)}")
+ ```
+
+
+
+## Advanced Features
+
+### Using System Messages
+
+You can provide system messages to guide the structured output generation:
+
+```python
+class ProductReview(BaseModel):
+ pros: List[str]
+ cons: List[str]
+ verdict: str
+
+response = generate_object(
+ model=openai("gpt-4"),
+ schema=ProductReview,
+ system="You are a critical product reviewer. Be honest about both positives and negatives.",
+ prompt="Review the latest iPhone"
+)
+```
+
+### Nested Models
+
+The AI SDK supports complex nested models with full validation:
+
+```python
+from typing import Optional, Dict
+
+class Address(BaseModel):
+ street: str
+ city: str
+ country: str
+ postal_code: str
+
+class Contact(BaseModel):
+ email: str
+ phone: Optional[str]
+ address: Address
+ metadata: Dict[str, str] = {}
+
+response = generate_object(
+ model=openai("gpt-4"),
+ schema=Contact,
+ prompt="Generate contact info for John Doe"
+)
+```
+
+
+ The LLM will automatically format its response to match your model's structure, including all required fields and proper types.
+
+
+### Error Handling
+
+When using structured outputs, you might encounter validation errors when the LLM generates data that doesn't match your model's requirements. The SDK provides the `AI_ObjectValidationError` class for handling these cases:
+
+```python
+from ai_sdk.core.errors import AI_ObjectValidationError
+
+try:
+ response = generate_object(
+ model=openai("gpt-4"),
+ output_model=YourModel,
+ prompt="Your prompt"
+ )
+except AI_ObjectValidationError as e:
+ print("Validation error:", e.message)
+except AI_APICallError as e:
+ print(f"API error (status {e.status_code}):", str(e))
+ if e.is_retryable:
+ print("This error can be retried")
+```
+
+
+ The `AI_ObjectValidationError` provides a descriptive message about what fields failed validation and why.
+
+
+
+ Always implement error handling when using structured outputs in production. The LLM might occasionally generate invalid data that needs to be handled gracefully.
+
+
+You might also encounter API-level errors when making requests. The `AI_APICallError` provides detailed information about the failed request:
+- Status code
+- Whether the error is retryable
+- Response body
+- Request details
+
+```python
+try:
+ response = generate_object(
+ model=openai("gpt-4"),
+ output_model=YourModel,
+ prompt="Your prompt"
+ )
+except AI_APICallError as e:
+ print(f"API call failed:")
+ print(f" URL: {e.url}")
+ print(f" Status: {e.status_code}")
+ print(f" Response: {e.response_body}")
+ print(f" Retryable: {e.is_retryable}")
diff --git a/docs/content/guides/text_generation.mdx b/docs/content/guides/text_generation.mdx
new file mode 100644
index 0000000..61e10e6
--- /dev/null
+++ b/docs/content/guides/text_generation.mdx
@@ -0,0 +1,204 @@
+import { Steps, Tabs, Callout } from 'nextra/components'
+
+# Text Generation
+
+When using LLMs, the most common task is to generate text. For this, the Python AI SDK provides a simple `generate_text` function.
+
+## Getting started
+
+### Using prompts
+
+The simplest way to generate text is to pass in a prompt and a model. For example:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.openai import openai
+
+response = generate_text(
+ model=openai("gpt-4"),
+ prompt="Hello, world!"
+)
+
+print(response.text)
+```
+
+### Adding a system prompt
+
+System prompts allow you to guide your LLM to behave in a specific way. You can pass in a system prompt by setting the `system` parameter:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.openai import openai
+
+response = generate_text(
+ model=openai("gpt-4"),
+ system="You are a helpful assistant.",
+ prompt="Hello, world!",
+)
+```
+
+## Understanding the Response
+
+The `generate_text` function returns a `TextResult` object which contains comprehensive information about the generation:
+
+```python
+class TextResult:
+ text: str # The generated text response
+ finish_reason: FinishReason # Why the generation stopped (length, stop token, etc)
+ usage: Usage # Token usage statistics
+ tool_calls: List[ToolCall] # Any tool calls made during generation
+ tool_results: List[Result] # Results from tool calls
+ request: RequestMetadata # Information about the request
+ response: ResponseMetadata # Information about the response
+ warnings: List[Warning] # Any warnings generated
+ provider_metadata: Dict # Provider-specific metadata
+```
+
+
+
+ ```python
+ response = generate_text(
+ model=openai("gpt-4"),
+ prompt="What is the capital of France?"
+ )
+ print(response.text) # "The capital of France is Paris."
+ print(response.finish_reason) # "stop"
+ ```
+
+
+ ```python
+ response = generate_text(
+ model=openai("gpt-4"),
+ prompt="Explain quantum computing"
+ )
+ print(f"Prompt tokens: {response.usage.prompt_tokens}")
+ print(f"Completion tokens: {response.usage.completion_tokens}")
+ print(f"Total tokens: {response.usage.total_tokens}")
+ ```
+
+
+
+
+ The `provider_metadata` field contains additional information specific to each provider. For example, OpenAI might include model-specific parameters while Anthropic might include different metadata.
+
+
+## Going beyond simple prompts with messages
+
+While the `prompt` parameter is useful for simple tasks, it is often better to pass in a list of messages for more complex interactions:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.openai import openai
+
+response = generate_text(
+ model=openai("gpt-4"),
+ messages=[
+ {"role": "user", "content": "Hello, world!"}
+ ]
+)
+```
+
+### Message Types
+
+The Python AI SDK supports four different message types:
+
+- `system`: A system message that guides the behavior of the LLM
+- `user`: A user message that acts as a prompt
+- `assistant`: A message from the assistant
+- `tool`: A message from a tool
+
+#### System messages
+
+System messages (sometimes referred to as `system prompt` or `developer messages`) are used to guide the behavior of the LLM. They
+are typically used to set the tone or provide instructions for the LLM:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.openai import openai
+
+response = generate_text(
+ model=openai("gpt-4"),
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant."},
+ {"role": "user", "content": "Hello, world!"}
+ ]
+)
+```
+
+#### User messages
+
+User messages support both text and image content:
+
+
+
+ ```python
+ response = generate_text(
+ model=openai("gpt-4"),
+ messages=[{"role": "user", "content": "Hello, world!"}]
+ )
+ ```
+
+
+ ```python
+ response = generate_text(
+ model=openai("gpt-4"),
+ messages=[{
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "Hello, world!"},
+ {"type": "text", "text": "This is a test."}
+ ]
+ }]
+ )
+ ```
+
+
+ ```python
+ response = generate_text(
+ model=openai("gpt-4"),
+ messages=[{
+ "role": "user",
+ "content": [{
+ "type": "image",
+ "image": "https://example.com/image.jpg"
+ }]
+ }]
+ )
+ ```
+
+
+
+#### Assistant messages
+
+Assistant messages represent responses from the LLM, useful for maintaining conversation context:
+
+```python
+response = generate_text(
+ model=openai("gpt-4"),
+ messages=[
+ {"role": "user", "content": "Hello, world!"},
+ {"role": "assistant", "content": "Hi, how can I help you today?"},
+ {"role": "user", "content": "What is your name?"}
+ ]
+)
+```
+
+#### Tool messages
+
+Tool messages contain results from tool calls and help provide context about previous tool interactions:
+
+```python
+response = generate_text(
+ model=openai("gpt-4"),
+ messages=[
+ {"role": "user", "content": "What's the weather like?"},
+ {"role": "tool", "content": {"temperature": 22, "condition": "sunny"}},
+ {"role": "assistant", "content": "The weather is sunny with a temperature of 22°C."}
+ ]
+)
+```
+
+
+ Not all providers support all message types. Check the provider's documentation for supported message types.
+
+
diff --git a/docs/content/index.mdx b/docs/content/index.mdx
new file mode 100644
index 0000000..e3d9046
--- /dev/null
+++ b/docs/content/index.mdx
@@ -0,0 +1,56 @@
+import { Callout } from 'nextra/components'
+import { Steps } from 'nextra/components'
+
+# Home
+
+The Python AI SDK is heavily inspired (ie. a port) of the awesome
+[AI SDK](https://sdk.vercel.ai/) built by [Vercel](https://vercel.com).
+
+I have been a big fan of the Vercel AI SDK and have always wanted to use
+it as part of my Python projects and decided to recreate it in Python.
+
+
+ If you have any feedback or feature requests, just reach out on [Github](https://github.com/jverre/ai-sdk).
+
+
+## Getting started
+
+To get started, simply follow the steps:
+
+
+### Install the package
+
+You can install the Python AI SDK using pip:
+
+```bash
+pip install ai-sdk-python
+```
+
+### Making your first LLM call
+
+Once the package is installed, you can make your first LLM call using the `generate_text` function:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.openai import openai
+
+response = generate_text(
+ model=openai("gpt-4o"),
+ prompt="Hello, world!"
+)
+
+print(response.text)
+```
+
+### Adding tracing
+
+The Python AI SDK supports tracing which means you can save all your LLM calls for future analysis.
+
+For this, the AI SDK relies on the [Opik](https://github.com/comet-ml/opik) library. To get started,
+the only thing you need to do is configure the Opik library:
+
+```bash
+opik.configure()
+```
+
+
diff --git a/docs/content/providers/_meta.js b/docs/content/providers/_meta.js
new file mode 100644
index 0000000..e810f8d
--- /dev/null
+++ b/docs/content/providers/_meta.js
@@ -0,0 +1,5 @@
+export default {
+ openai: 'OpenAI',
+ anthropic: 'Anthropic',
+ openrouter: 'OpenRouter',
+ }
\ No newline at end of file
diff --git a/docs/content/providers/anthropic.mdx b/docs/content/providers/anthropic.mdx
new file mode 100644
index 0000000..223ee04
--- /dev/null
+++ b/docs/content/providers/anthropic.mdx
@@ -0,0 +1,160 @@
+import { Callout } from 'nextra/components'
+
+# Anthropic Provider
+
+The Anthropic provider gives you access to Claude models within the AI SDK. It supports Claude 3 models with capabilities for chat, image understanding, and tool use.
+
+## Basic Usage
+
+The simplest way to use Anthropic models is with the `anthropic` function:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.anthropic import anthropic
+
+response = generate_text(
+ model=anthropic("claude-3-opus-20240229"),
+ prompt="Hello, world!"
+)
+```
+
+
+## Supported Models
+
+The following table shows all supported Claude models and their capabilities:
+
+| Model | Chat | Images | Tool Calls |
+|-------|------|--------|------------|
+| claude-3-7-sonnet-20250219 | ✅ | ✅ | ✅ |
+| claude-3-5-sonnet-20241022 | ✅ | ✅ | ✅ |
+| claude-3-5-sonnet-20240620 | ✅ | ✅ | ✅ |
+| claude-3-5-haiku-20241022 | ✅ | ❌ | ✅ |
+| claude-3-opus-20240229 | ✅ | ✅ | ✅ |
+| claude-3-sonnet-20240229 | ✅ | ✅ | ❌ |
+| claude-3-haiku-20240307 | ✅ | ✅ | ✅ |
+
+
+ All models support basic chat functionality. Image and tool call support varies by model.
+
+
+## Configuration
+
+You can configure the Anthropic provider with various settings:
+
+```python
+from ai_sdk.anthropic import create_anthropic_provider, AnthropicProviderSettings
+
+provider = create_anthropic_provider(
+ settings=AnthropicProviderSettings(
+ api_key="your-api-key", # Optional: can also use ANTHROPIC_API_KEY env var
+ base_url="https://api.anthropic.com", # Optional: defaults to Anthropic's API
+ headers={"Custom-Header": "value"} # Optional: additional headers
+ )
+)
+
+model = provider("claude-3-opus-20240229")
+```
+
+### Authentication
+
+The provider will look for your API key in the following order:
+1. The `api_key` parameter in settings
+2. The `ANTHROPIC_API_KEY` environment variable
+
+
+ Never hardcode your API key in your code. Use environment variables instead.
+
+
+## Model Settings
+
+When creating a model, you can customize its behavior:
+
+```python
+from ai_sdk.anthropic import anthropic, AnthropicChatSettings
+
+model = anthropic(
+ "claude-3-opus-20240229",
+ settings=AnthropicChatSettings(
+ sendReasoning=True # Enable reasoning in responses
+ )
+)
+```
+
+## Working with Images
+
+For models that support image input, you can pass images via URL or base64:
+
+```python
+response = generate_text(
+ model=anthropic("claude-3-opus-20240229"),
+ messages=[{
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "What's in this image?"},
+ {"type": "image", "image": "https://example.com/image.jpg"}
+ ]
+ }]
+)
+```
+
+
+ Image support is available on specific Claude 3 models only. Check `SUPPORTED_IMAGE_MODELS` for compatibility.
+
+
+## Using Tool Calls
+
+Tools can be defined using Pydantic models and optional execution functions:
+
+```python
+from pydantic import BaseModel
+from ai_sdk import generate_text
+from typing import Dict
+
+class WeatherTool(BaseModel):
+ location: str
+ units: str = "celsius"
+
+# Define tools with optional execution functions
+tools: Dict[str, dict] = {
+ "weather": {
+ "description": "Get the weather in a location",
+ "parameters": WeatherTool,
+ "execute": lambda args: {
+ "temperature": 22,
+ "condition": "sunny"
+ }
+ }
+}
+
+response = generate_text(
+ model=anthropic("claude-3-opus-20240229"),
+ prompt="What's the weather in Paris?",
+ tools=tools,
+ max_steps=2 # Allow multiple steps for tool execution
+)
+
+# Access results
+print(response.text) # Final response
+print(response.tool_calls) # List of tool calls made
+print(response.tool_results) # List of tool results
+```
+
+The response includes:
+- `text`: The final generated text
+- `tool_calls`: List of `ToolCallPart` objects containing:
+ - `tool_call_id`: Unique identifier for the call
+ - `tool_name`: Name of the tool called
+ - `args`: Dictionary of arguments passed
+- `tool_results`: List of `ToolResultPart` objects containing:
+ - `tool_call_id`: Matching ID from the call
+ - `tool_name`: Name of the tool
+ - `result`: The result returned
+ - `is_error`: Optional error flag
+
+
+ When `max_steps` is greater than 1, the model can make tool calls and use the results in its final response.
+
+
+
+ Not all Claude models support tool calls. Check the compatibility table above for supported models.
+
\ No newline at end of file
diff --git a/docs/content/providers/openai.mdx b/docs/content/providers/openai.mdx
new file mode 100644
index 0000000..471d4a8
--- /dev/null
+++ b/docs/content/providers/openai.mdx
@@ -0,0 +1,178 @@
+import { Callout } from 'nextra/components'
+
+# OpenAI Provider
+
+The OpenAI provider allows you to use OpenAI's models within the AI SDK. It supports chat-based models like GPT-4 and GPT-3.5.
+
+## Basic Usage
+
+The simplest way to use OpenAI models is with the `openai` function:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.openai import openai
+
+response = generate_text(
+ model=openai("gpt-4"),
+ prompt="Hello, world!"
+)
+```
+
+
+## Supported Models
+
+The following table shows all supported OpenAI models and their capabilities:
+
+| Model | Chat | Images | Tool Calls | JSON Mode | System Messages |
+|-------|------|--------|------------|-----------|-----------------|
+| gpt-4o | ✅ | ✅ | ✅ | ✅ | ✅ |
+| gpt-4o-mini | ✅ | ✅ | ✅ | ✅ | ✅ |
+| gpt-4-turbo | ✅ | ✅ | ❌ | ❌ | ✅ |
+| gpt-4 | ✅ | ❌ | ❌ | ❌ | ✅ |
+| gpt-3.5-turbo | ✅ | ❌ | ❌ | ❌ | ✅ |
+| o1 | ✅ | ✅ | ✅ | ✅ | ✅ |
+| o1-mini | ✅ | ✅ | ✅ | ✅ | ❌ |
+| o1-preview | ✅ | ❌ | ❌ | ❌ | ❌ |
+| o3-mini | ✅ | ❌ | ✅ | ✅ | ✅ |
+| chatgpt-4o-latest | ✅ | ✅ | ❌ | ❌ | ✅ |
+
+## Configuration
+
+You can configure the OpenAI provider in several ways:
+
+```python
+from ai_sdk.openai import create_openai_provider, OpenAIProviderSettings
+
+provider = create_openai_provider(
+ settings=OpenAIProviderSettings(
+ api_key="your-api-key", # Optional: can also use OPENAI_API_KEY env var
+ organization="your-org-id", # Optional: for enterprise customers
+ project="your-project", # Optional: for project tracking
+ base_url="https://api.openai.com" # Optional: defaults to OpenAI's API
+ )
+)
+
+model = provider("gpt-4")
+```
+
+### Authentication
+
+The provider will look for your API key in the following order:
+1. The `api_key` parameter in settings
+2. The `OPENAI_API_KEY` environment variable
+
+
+ Never hardcode your API key in your code. Use environment variables instead.
+
+
+### Custom Headers
+
+You can add custom headers to all requests:
+
+```python
+provider = create_openai_provider(
+ settings=OpenAIProviderSettings(
+ headers={
+ "Custom-Header": "value"
+ }
+ )
+)
+```
+
+### Using Alternative Base URLs
+
+If you're using Azure OpenAI or a proxy, you can change the base URL:
+
+```python
+provider = create_openai_provider(
+ settings=OpenAIProviderSettings(
+ base_url="https://your-custom-endpoint.com"
+ )
+)
+```
+
+## Model Settings
+
+When creating a model, you can customize its behavior:
+
+```python
+from ai_sdk.openai import openai, OpenAIChatSettings
+
+model = openai(
+ "gpt-4",
+ settings=OpenAIChatSettings(
+ # Add model-specific settings here
+ )
+)
+```
+
+## Working with Images
+
+For models that support image input:
+
+```python
+response = generate_text(
+ model=openai("gpt-4o"),
+ messages=[{
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "What's in this image?"},
+ {"type": "image", "image": "https://example.com/image.jpg"}
+ ]
+ }]
+)
+```
+
+## Using Tool Calls
+
+Tools can be defined using Pydantic models and optional execution functions:
+
+```python
+from pydantic import BaseModel
+from ai_sdk import generate_text
+from typing import Dict
+
+class WeatherTool(BaseModel):
+ location: str
+ units: str = "celsius"
+
+# Define tools with optional execution functions
+tools: Dict[str, dict] = {
+ "weather": {
+ "description": "Get the weather in a location",
+ "parameters": WeatherTool,
+ "execute": lambda args: {
+ "temperature": 22,
+ "condition": "sunny"
+ }
+ }
+}
+
+response = generate_text(
+ model=openai("gpt-4o"),
+ prompt="What's the weather in Paris?",
+ tools=tools,
+ max_steps=2 # Allow multiple steps for tool execution
+)
+
+# Access results
+print(response.text) # Final response
+print(response.tool_calls) # List of tool calls made
+print(response.tool_results) # List of tool results
+```
+
+The response includes:
+- `text`: The final generated text
+- `tool_calls`: List of `ToolCallPart` objects containing:
+ - `tool_call_id`: Unique identifier for the call
+ - `tool_name`: Name of the tool called
+ - `args`: Dictionary of arguments passed
+- `tool_results`: List of `ToolResultPart` objects containing:
+ - `tool_call_id`: Matching ID from the call
+ - `tool_name`: Name of the tool
+ - `result`: The result returned
+ - `is_error`: Optional error flag
+
+
+ When `max_steps` is greater than 1, the model can make tool calls and use the results in its final response.
+
diff --git a/docs/content/providers/openrouter.mdx b/docs/content/providers/openrouter.mdx
new file mode 100644
index 0000000..a7ca630
--- /dev/null
+++ b/docs/content/providers/openrouter.mdx
@@ -0,0 +1,193 @@
+import { Callout } from 'nextra/components'
+
+# OpenRouter Provider
+
+The OpenRouter provider allows you to access various AI models through OpenRouter's API. It provides a unified interface for multiple model providers with support for advanced features like image understanding and tool calls.
+
+## Basic Usage
+
+The simplest way to use OpenRouter models is with the `openrouter` function:
+
+```python
+from ai_sdk import generate_text
+from ai_sdk.openrouter import openrouter
+
+response = generate_text(
+ model=openrouter("openai/gpt-4o"),
+ prompt="Hello, world!"
+)
+```
+
+## Features
+
+OpenRouter supports all major capabilities across its models:
+
+- ✅ Chat Conversations
+- ✅ Image Understanding
+- ✅ Tool Calls
+- ✅ JSON Mode
+- ✅ System Messages
+
+## Configuration
+
+You can configure the OpenRouter provider with various settings:
+
+```python
+from ai_sdk.openrouter import create_openrouter_provider, OpenRouterProviderSettings
+
+provider = create_openrouter_provider(
+ settings=OpenRouterProviderSettings(
+ api_key="your-api-key", # Optional: can also use OPENROUTER_API_KEY env var
+ base_url="https://openrouter.ai/api", # Optional: defaults to OpenRouter's API
+ headers={"Custom-Header": "value"} # Optional: additional headers
+ )
+)
+
+model = provider("openai/gpt-4o")
+```
+
+### Authentication
+
+The provider will look for your API key in the following order:
+1. The `api_key` parameter in settings
+2. The `OPENROUTER_API_KEY` environment variable
+
+
+ Never hardcode your API key in your code. Use environment variables instead.
+
+
+## Model Settings
+
+When creating a model, you can customize its behavior:
+
+```python
+from ai_sdk.openrouter import openrouter, OpenRouterChatSettings
+
+model = openrouter(
+ "your-chosen-model",
+ settings=OpenRouterChatSettings(
+ logit_bias=None, # Optional: adjust token probabilities
+ parallel_tool_calls=True, # Optional: enable parallel tool execution
+ structured_outputs=True, # Optional: enable structured output format
+ user="user-id" # Optional: user identifier
+ )
+)
+```
+
+## Working with Images
+
+OpenRouter supports image input for compatible models:
+
+```python
+response = generate_text(
+ model=openrouter("openai/gpt-4o"),
+ messages=[{
+ "role": "user",
+ "content": [
+ {"type": "text", "text": "What's in this image?"},
+ {"type": "image", "image": "https://example.com/image.jpg"}
+ ]
+ }]
+)
+```
+
+## Using Tool Calls
+
+Tools can be defined using Pydantic models and optional execution functions:
+
+```python
+from pydantic import BaseModel
+from ai_sdk import generate_text
+from typing import Dict
+
+class WeatherTool(BaseModel):
+ location: str
+ units: str = "celsius"
+
+# Define tools with optional execution functions
+tools: Dict[str, Tool] = {
+ "weather": {
+ "description": "Get the weather in a location",
+ "parameters": WeatherTool,
+ "execute": lambda location: {
+ "location": location,
+ "temperature": 22,
+ "condition": "sunny"
+ }
+ }
+}
+
+response = generate_text(
+ model=openrouter("openai/gpt-4o"), # or openrouter("your-model")
+ prompt="What's the weather in Paris?",
+ tools=tools,
+ max_steps=2 # Allow multiple steps for tool execution
+)
+
+# Access results
+print(response.text) # Final response
+print(response.tool_calls) # List of tool calls made
+print(response.tool_results) # List of tool results
+```
+
+The response includes:
+- `text`: The final generated text
+- `tool_calls`: List of `ToolCallPart` objects containing:
+ - `tool_call_id`: Unique identifier for the call
+ - `tool_name`: Name of the tool called
+ - `args`: Dictionary of arguments passed
+- `tool_results`: List of `ToolResultPart` objects containing:
+ - `tool_call_id`: Matching ID from the call
+ - `tool_name`: Name of the tool
+ - `result`: The result returned
+ - `is_error`: Optional error flag
+
+
+ When `max_steps` is greater than 1, the model can make tool calls and use the results in its final response.
+
+
+## Message Types
+
+OpenRouter supports various message types:
+- `system`: System-level instructions
+- `developer`: Developer-specific instructions
+- `user`: User messages (text or images)
+- `assistant`: AI responses
+- `tool`: Tool call results
+
+```python
+response = generate_text(
+ model=openrouter("your-chosen-model"),
+ messages=[
+ {"role": "system", "content": "You are a helpful assistant"},
+ {"role": "user", "content": "Hello!"},
+ {"role": "assistant", "content": "Hi there!"},
+ {"role": "user", "content": "How are you?"}
+ ]
+)
+```
+
+
+ OpenRouter automatically handles the conversion between different provider message formats, making it easier to work with various models through a single interface.
+
+
+## Error Handling
+
+OpenRouter provides detailed error information when requests fail:
+
+```python
+try:
+ response = generate_text(
+ model=openrouter("your-chosen-model"),
+ prompt="Your prompt"
+ )
+except AI_APICallError as e:
+ print(f"API call failed:")
+ print(f" Status: {e.status_code}")
+ print(f" Response: {e.response_body}")
+ print(f" Retryable: {e.is_retryable}")
+```
+
+
+ Always implement proper error handling in production. OpenRouter will indicate if errors are retryable through the `is_retryable` flag.
+
diff --git a/docs/mdx-components.js b/docs/mdx-components.js
new file mode 100644
index 0000000..9c55d84
--- /dev/null
+++ b/docs/mdx-components.js
@@ -0,0 +1,12 @@
+import { useMDXComponents as getThemeComponents } from 'nextra-theme-docs' // nextra-theme-blog or your custom theme
+
+// Get the default MDX components
+const themeComponents = getThemeComponents()
+
+// Merge components
+export function useMDXComponents(components) {
+ return {
+ ...themeComponents,
+ ...components
+ }
+}
\ No newline at end of file
diff --git a/docs/next.config.mjs b/docs/next.config.mjs
new file mode 100644
index 0000000..1fbe766
--- /dev/null
+++ b/docs/next.config.mjs
@@ -0,0 +1,17 @@
+import nextra from 'nextra'
+
+const withNextra = nextra({
+ theme: 'nextra-theme-docs',
+ themeConfig: './theme.config.jsx'
+})
+
+// You can include other Next.js configuration options here, in addition to Nextra settings:
+export default withNextra({
+ output: 'export',
+ images: {
+ unoptimized: true,
+ },
+ basePath: process.env.NODE_ENV === 'production' ? '/ai-sdk' : '',
+ // This setting is required for static exports
+ distDir: 'dist'
+})
\ No newline at end of file
diff --git a/docs/package-lock.json b/docs/package-lock.json
new file mode 100644
index 0000000..42db4d8
--- /dev/null
+++ b/docs/package-lock.json
@@ -0,0 +1,6893 @@
+{
+ "name": "docs",
+ "lockfileVersion": 3,
+ "requires": true,
+ "packages": {
+ "": {
+ "dependencies": {
+ "next": "^15.2.4",
+ "nextra": "^4.2.17",
+ "nextra-theme-docs": "^4.2.17",
+ "react": "^19.1.0",
+ "react-dom": "^19.1.0",
+ "react-icons": "^5.5.0",
+ "typescript": "^5.8.2"
+ },
+ "devDependencies": {
+ "gh-pages": "^6.3.0"
+ }
+ },
+ "node_modules/@antfu/install-pkg": {
+ "version": "1.0.0",
+ "resolved": "https://registry.npmjs.org/@antfu/install-pkg/-/install-pkg-1.0.0.tgz",
+ "integrity": "sha512-xvX6P/lo1B3ej0OsaErAjqgFYzYVcJpamjLAFLYh9vRJngBrMoUG7aVnrGTeqM7yxbyTD5p3F2+0/QUEh8Vzhw==",
+ "license": "MIT",
+ "dependencies": {
+ "package-manager-detector": "^0.2.8",
+ "tinyexec": "^0.3.2"
+ },
+ "funding": {
+ "url": "https://github.com/sponsors/antfu"
+ }
+ },
+ "node_modules/@antfu/utils": {
+ "version": "8.1.1",
+ "resolved": "https://registry.npmjs.org/@antfu/utils/-/utils-8.1.1.tgz",
+ "integrity": "sha512-Mex9nXf9vR6AhcXmMrlz/HVgYYZpVGJ6YlPgwl7UnaFpnshXs6EK/oa5Gpf3CzENMjkvEx2tQtntGnb7UtSTOQ==",
+ "license": "MIT",
+ "funding": {
+ "url": "https://github.com/sponsors/antfu"
+ }
+ },
+ "node_modules/@braintree/sanitize-url": {
+ "version": "7.1.1",
+ "resolved": "https://registry.npmjs.org/@braintree/sanitize-url/-/sanitize-url-7.1.1.tgz",
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+ "license": "MIT"
+ },
+ "node_modules/@chevrotain/cst-dts-gen": {
+ "version": "11.0.3",
+ "resolved": "https://registry.npmjs.org/@chevrotain/cst-dts-gen/-/cst-dts-gen-11.0.3.tgz",
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+ "license": "Apache-2.0",
+ "dependencies": {
+ "@chevrotain/gast": "11.0.3",
+ "@chevrotain/types": "11.0.3",
+ "lodash-es": "4.17.21"
+ }
+ },
+ "node_modules/@chevrotain/gast": {
+ "version": "11.0.3",
+ "resolved": "https://registry.npmjs.org/@chevrotain/gast/-/gast-11.0.3.tgz",
+ "integrity": "sha512-+qNfcoNk70PyS/uxmj3li5NiECO+2YKZZQMbmjTqRI3Qchu8Hig/Q9vgkHpI3alNjr7M+a2St5pw5w5F6NL5/Q==",
+ "license": "Apache-2.0",
+ "dependencies": {
+ "@chevrotain/types": "11.0.3",
+ "lodash-es": "4.17.21"
+ }
+ },
+ "node_modules/@chevrotain/regexp-to-ast": {
+ "version": "11.0.3",
+ "resolved": "https://registry.npmjs.org/@chevrotain/regexp-to-ast/-/regexp-to-ast-11.0.3.tgz",
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+ "license": "Apache-2.0"
+ },
+ "node_modules/@chevrotain/types": {
+ "version": "11.0.3",
+ "resolved": "https://registry.npmjs.org/@chevrotain/types/-/types-11.0.3.tgz",
+ "integrity": "sha512-gsiM3G8b58kZC2HaWR50gu6Y1440cHiJ+i3JUvcp/35JchYejb2+5MVeJK0iKThYpAa/P2PYFV4hoi44HD+aHQ==",
+ "license": "Apache-2.0"
+ },
+ "node_modules/@chevrotain/utils": {
+ "version": "11.0.3",
+ "resolved": "https://registry.npmjs.org/@chevrotain/utils/-/utils-11.0.3.tgz",
+ "integrity": "sha512-YslZMgtJUyuMbZ+aKvfF3x1f5liK4mWNxghFRv7jqRR9C3R3fAOGTTKvxXDa2Y1s9zSbcpuO0cAxDYsc9SrXoQ==",
+ "license": "Apache-2.0"
+ },
+ "node_modules/@emnapi/runtime": {
+ "version": "1.4.0",
+ "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.4.0.tgz",
+ "integrity": "sha512-64WYIf4UYcdLnbKn/umDlNjQDSS8AgZrI/R9+x5ilkUVFxXcA1Ebl+gQLc/6mERA4407Xof0R7wEyEuj091CVw==",
+ "license": "MIT",
+ "optional": true,
+ "dependencies": {
+ "tslib": "^2.4.0"
+ }
+ },
+ "node_modules/@floating-ui/core": {
+ "version": "1.6.9",
+ "resolved": "https://registry.npmjs.org/@floating-ui/core/-/core-1.6.9.tgz",
+ "integrity": "sha512-uMXCuQ3BItDUbAMhIXw7UPXRfAlOAvZzdK9BWpE60MCn+Svt3aLn9jsPTi/WNGlRUu2uI0v5S7JiIUsbsvh3fw==",
+ "license": "MIT",
+ "dependencies": {
+ "@floating-ui/utils": "^0.2.9"
+ }
+ },
+ "node_modules/@floating-ui/dom": {
+ "version": "1.6.13",
+ "resolved": "https://registry.npmjs.org/@floating-ui/dom/-/dom-1.6.13.tgz",
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+ "license": "MIT",
+ "dependencies": {
+ "@floating-ui/core": "^1.6.0",
+ "@floating-ui/utils": "^0.2.9"
+ }
+ },
+ "node_modules/@floating-ui/react": {
+ "version": "0.26.28",
+ "resolved": "https://registry.npmjs.org/@floating-ui/react/-/react-0.26.28.tgz",
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+ "license": "MIT",
+ "dependencies": {
+ "@floating-ui/react-dom": "^2.1.2",
+ "@floating-ui/utils": "^0.2.8",
+ "tabbable": "^6.0.0"
+ },
+ "peerDependencies": {
+ "react": ">=16.8.0",
+ "react-dom": ">=16.8.0"
+ }
+ },
+ "node_modules/@floating-ui/react-dom": {
+ "version": "2.1.2",
+ "resolved": "https://registry.npmjs.org/@floating-ui/react-dom/-/react-dom-2.1.2.tgz",
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+ "dependencies": {
+ "@floating-ui/dom": "^1.0.0"
+ },
+ "peerDependencies": {
+ "react": ">=16.8.0",
+ "react-dom": ">=16.8.0"
+ }
+ },
+ "node_modules/@floating-ui/utils": {
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diff --git a/docs/package.json b/docs/package.json
new file mode 100644
index 0000000..3ea78d4
--- /dev/null
+++ b/docs/package.json
@@ -0,0 +1,21 @@
+{
+ "scripts": {
+ "dev": "next",
+ "build": "next build",
+ "start": "next start",
+ "export": "next build && next export -o out",
+ "deploy": "touch out/.nojekyll && gh-pages -d out -t true"
+ },
+ "dependencies": {
+ "next": "^15.2.4",
+ "nextra": "^4.2.17",
+ "nextra-theme-docs": "^4.2.17",
+ "react": "^19.1.0",
+ "react-dom": "^19.1.0",
+ "react-icons": "^5.5.0",
+ "typescript": "^5.8.2"
+ },
+ "devDependencies": {
+ "gh-pages": "^6.3.0"
+ }
+}
diff --git a/src/ai_sdk/anthropic/__init__.py b/src/ai_sdk/anthropic/__init__.py
index be6c24a..87b4261 100644
--- a/src/ai_sdk/anthropic/__init__.py
+++ b/src/ai_sdk/anthropic/__init__.py
@@ -1,3 +1,3 @@
-from .provider import anthropic, create_anthropic_provider
+from .provider import anthropic, create_anthropic_provider, AnthropicProviderSettings
-__all__ = ["anthropic", "create_anthropic_provider"]
\ No newline at end of file
+__all__ = ["anthropic", "create_anthropic_provider", "AnthropicProviderSettings"]
\ No newline at end of file
diff --git a/src/ai_sdk/openai/__init__.py b/src/ai_sdk/openai/__init__.py
index e59bc7b..ae27ba8 100644
--- a/src/ai_sdk/openai/__init__.py
+++ b/src/ai_sdk/openai/__init__.py
@@ -1,3 +1,3 @@
-from .provider import openai, create_openai_provider
+from .provider import openai, create_openai_provider, OpenAIProviderSettings
-__all__ = ["openai", "create_openai_provider"]
\ No newline at end of file
+__all__ = ["openai", "create_openai_provider", "OpenAIProviderSettings"]
\ No newline at end of file
diff --git a/src/ai_sdk/openrouter/__init__.py b/src/ai_sdk/openrouter/__init__.py
index 4fbc0e7..5ccfee7 100644
--- a/src/ai_sdk/openrouter/__init__.py
+++ b/src/ai_sdk/openrouter/__init__.py
@@ -1,3 +1,3 @@
-from .provider import openrouter, create_openrouter_provider
+from .provider import openrouter, create_openrouter_provider, OpenRouterProviderSettings
-__all__ = ["openrouter", "create_openrouter_provider"]
\ No newline at end of file
+__all__ = ["openrouter", "create_openrouter_provider", "OpenRouterProviderSettings"]
\ No newline at end of file