diff --git a/docs/config.json b/docs/config.json
index 363fcc30..53657ab9 100644
--- a/docs/config.json
+++ b/docs/config.json
@@ -50,6 +50,10 @@
           "label": "Agentic Cycle",
           "to": "guides/agentic-cycle"
         },
+        {
+          "label": "Structured Outputs",
+          "to": "guides/structured-outputs"
+        },
         {
           "label": "Streaming",
           "to": "guides/streaming"
diff --git a/docs/guides/structured-outputs.md b/docs/guides/structured-outputs.md
new file mode 100644
index 00000000..789438ed
--- /dev/null
+++ b/docs/guides/structured-outputs.md
@@ -0,0 +1,300 @@
+---
+title: Structured Outputs
+id: structured-outputs
+order: 6
+---
+
+Structured outputs allow you to constrain AI model responses to match a specific JSON schema, ensuring consistent and type-safe data extraction. TanStack AI uses the [Standard JSON Schema](https://standardschema.dev/) specification, allowing you to use any compatible schema library.
+
+## Overview
+
+When you provide an `outputSchema` to the `chat()` function, TanStack AI:
+
+1. Converts your schema to JSON Schema format
+2. Sends it to the provider's native structured output API
+3. Validates the response against your schema
+4. Returns a fully typed result
+
+This is useful for:
+
+- **Extracting structured data** from unstructured text
+- **Building forms or wizards** with AI-generated content
+- **Creating APIs** that return predictable JSON shapes
+- **Ensuring type safety** between AI responses and your application
+
+## Schema Libraries
+
+TanStack AI uses **Standard JSON Schema**, which means you can use any schema library that implements the specification:
+
+- [Zod](https://zod.dev/) (v4.2+)
+- [ArkType](https://arktype.io/)
+- [Valibot](https://valibot.dev/) (via `@valibot/to-json-schema`)
+- Plain JSON Schema objects
+
+> **Note:** Refer to your schema library's documentation for details on defining schemas and using features like `.describe()` for field descriptions. TanStack AI will convert your schema to JSON Schema format automatically.
+
+## Basic Usage
+
+Here's how to use structured outputs with a Zod schema:
+
+```typescript
+import { chat } from "@tanstack/ai";
+import { openaiText } from "@tanstack/ai-openai";
+import { z } from "zod";
+
+// Define your schema
+const PersonSchema = z.object({
+  name: z.string().describe("The person's full name"),
+  age: z.number().describe("The person's age in years"),
+  email: z.string().email().describe("The person's email address"),
+});
+
+// Use it with chat()
+const person = await chat({
+  adapter: openaiText("gpt-4o"),
+  messages: [
+    {
+      role: "user",
+      content: "Extract the person info: John Doe is 30 years old, email john@example.com",
+    },
+  ],
+  outputSchema: PersonSchema,
+});
+
+// person is fully typed as { name: string, age: number, email: string }
+console.log(person.name); // "John Doe"
+console.log(person.age); // 30
+console.log(person.email); // "john@example.com"
+```
+
+## Type Inference
+
+The return type of `chat()` changes based on the `outputSchema` prop:
+
+| Configuration | Return Type |
+|--------------|-------------|
+| No `outputSchema` | `AsyncIterable<StreamChunk>` | 
+| With `outputSchema` | `Promise<InferSchemaType<TSchema>>` |
+
+When you provide an `outputSchema`, TanStack AI automatically infers the TypeScript type from your schema:
+
+```typescript
+import { z } from "zod";
+
+// Define a complex schema
+const RecipeSchema = z.object({
+  name: z.string(),
+  prepTime: z.string(),
+  servings: z.number(),
+  ingredients: z.array(
+    z.object({
+      item: z.string(),
+      amount: z.string(),
+    })
+  ),
+  instructions: z.array(z.string()),
+});
+
+// TypeScript knows the exact return type
+const recipe = await chat({
+  adapter: openaiText("gpt-4o"),
+  messages: [{ role: "user", content: "Give me a recipe for scrambled eggs" }],
+  outputSchema: RecipeSchema,
+});
+
+// Full type safety - TypeScript knows all these properties exist
+recipe.name; // string
+recipe.prepTime; // string
+recipe.servings; // number
+recipe.ingredients[0].item; // string
+recipe.instructions.map((step) => step.toUpperCase()); // string[]
+```
+
+## Using Field Descriptions
+
+Schema field descriptions help the AI understand what data to extract. Most schema libraries support a `.describe()` method:
+
+```typescript
+const ProductSchema = z.object({
+  name: z.string().describe("The product name"),
+  price: z.number().describe("Price in USD"),
+  inStock: z.boolean().describe("Whether the product is currently available"),
+  categories: z
+    .array(z.string())
+    .describe("Product categories like 'electronics', 'clothing', etc."),
+});
+```
+
+These descriptions are included in the JSON Schema sent to the provider, giving the AI context about each field.
+
+## Complex Nested Schemas
+
+You can define deeply nested structures:
+
+```typescript
+const CompanySchema = z.object({
+  name: z.string(),
+  founded: z.number().describe("Year the company was founded"),
+  headquarters: z.object({
+    city: z.string(),
+    country: z.string(),
+    address: z.string().optional(),
+  }),
+  employees: z.array(
+    z.object({
+      name: z.string(),
+      role: z.string(),
+      department: z.string(),
+    })
+  ),
+  financials: z
+    .object({
+      revenue: z.number().describe("Annual revenue in millions USD"),
+      profitable: z.boolean(),
+    })
+    .optional(),
+});
+
+const company = await chat({
+  adapter: anthropicText("claude-sonnet-4-5"),
+  messages: [
+    {
+      role: "user",
+      content: "Extract company info from this article: ...",
+    },
+  ],
+  outputSchema: CompanySchema,
+});
+
+// Access nested properties with full type safety
+console.log(company.headquarters.city);
+console.log(company.employees[0].role);
+```
+
+## Combining with Tools
+
+Structured outputs work seamlessly with the agentic tool loop. When both `outputSchema` and `tools` are provided, TanStack AI will:
+
+1. Execute the full agentic loop (running tools as needed)
+2. Once complete, generate the final structured output
+3. Return the validated, typed result
+
+```typescript
+import { chat, toolDefinition } from "@tanstack/ai";
+import { z } from "zod";
+
+const getProductPrice = toolDefinition({
+  name: "get_product_price",
+  description: "Get the current price of a product",
+  inputSchema: z.object({
+    productId: z.string(),
+  }),
+}).server(async ({ input }) => {
+  // Fetch price from database
+  return { price: 29.99, currency: "USD" };
+});
+
+const RecommendationSchema = z.object({
+  productName: z.string(),
+  currentPrice: z.number(),
+  reason: z.string(),
+});
+
+const recommendation = await chat({
+  adapter: openaiText("gpt-4o"),
+  messages: [
+    {
+      role: "user",
+      content: "Recommend a product for a developer",
+    },
+  ],
+  tools: [getProductPrice],
+  outputSchema: RecommendationSchema,
+});
+
+// The AI will call the tool to get prices, then return structured output
+console.log(recommendation.productName);
+console.log(recommendation.currentPrice);
+console.log(recommendation.reason);
+```
+
+## Using Plain JSON Schema
+
+If you prefer not to use a schema library, you can pass a plain JSON Schema object:
+
+```typescript
+import type { JSONSchema } from "@tanstack/ai";
+
+const schema: JSONSchema = {
+  type: "object",
+  properties: {
+    name: { type: "string", description: "The person's name" },
+    age: { type: "number", description: "The person's age" },
+  },
+  required: ["name", "age"],
+};
+
+const result = await chat({
+  adapter: openaiText("gpt-4o"),
+  messages: [{ role: "user", content: "Extract: John is 25 years old" }],
+  outputSchema: schema,
+});
+
+// Note: With plain JSON Schema, TypeScript infers `unknown` type
+// You'll need to cast or validate the result yourself
+const person = result as { name: string; age: number };
+```
+
+> **Note:** When using plain JSON Schema, TypeScript cannot infer the return type. The result will be typed as `unknown`. For full type safety, use a schema library like Zod.
+
+## Provider Support
+
+Structured outputs are supported by all major providers through their native APIs:
+
+| Provider | Implementation |
+|----------|---------------|
+| OpenAI | Uses `response_format` with `json_schema` |
+| Anthropic | Uses tool-based extraction |
+| Google Gemini | Uses `responseSchema` |
+| Ollama | Uses JSON mode with schema |
+
+TanStack AI handles the provider-specific implementation details automatically, so you can use the same code across different providers.
+
+## Best Practices
+
+1. **Use descriptive field names and descriptions** - This helps the AI understand what data to extract
+
+2. **Keep schemas focused** - Extract only the data you need; simpler schemas produce more reliable results
+
+3. **Use optional fields appropriately** - Mark fields as optional when the data might not be present in the source
+
+4. **Validate edge cases** - Test with various inputs to ensure the schema handles edge cases correctly
+
+5. **Use enums for constrained values** - When a field has a limited set of valid values, use enums:
+
+   ```typescript
+   const schema = z.object({
+     status: z.enum(["pending", "approved", "rejected"]),
+     priority: z.enum(["low", "medium", "high"]),
+   });
+   ```
+
+## Error Handling
+
+If the AI response doesn't match your schema, TanStack AI will throw a validation error:
+
+```typescript
+try {
+  const result = await chat({
+    adapter: openaiText("gpt-4o"),
+    messages: [{ role: "user", content: "..." }],
+    outputSchema: MySchema,
+  });
+} catch (error) {
+  if (error instanceof Error) {
+    console.error("Structured output failed:", error.message);
+  }
+}
+```
+
+The error will include details about which fields failed validation, helping you debug schema mismatches.