feat: add section in user guide on new functionality for Agent.as_tool()

.github/dependabot.yml

@@ -0,0 +1,21 @@
+# To get started with Dependabot version updates, you'll need to specify which
+# package ecosystems to update and where the package manifests are located.
+# Please see the documentation for all configuration options:
+# https://docs.github.com/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
+
+version: 2
+updates:
+  - package-ecosystem: "npm"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    open-pull-requests-limit: 100
+    commit-message:
+      prefix: ci
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      interval: "daily"
+    open-pull-requests-limit: 100
+    commit-message:
+      prefix: ci

.github/workflows/ci.yml

@@ -21,7 +21,7 @@ jobs:
       - name: Setup Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: '20'
+          node-version: '22'
 
       - name: Install dependencies
         run: npm install

.github/workflows/deploy-github-pages.yml

@@ -19,7 +19,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: '20'
+          node-version: '22'
 
       - name: Install dependencies
         run: npm install

.github/workflows/docs-preview.yml

@@ -61,7 +61,7 @@ jobs:
       - name: Set up Node.js
         uses: actions/setup-node@v4
         with:
-          node-version: '20'
+          node-version: '22'
 
       - name: Install dependencies
         run: npm install

.node-version

@@ -1 +1 @@
-v20.19.5
+v22.12.0

docs/examples/python/structured_output.py

@@ -5,13 +5,12 @@
 This example demonstrates how to use structured output with Strands Agents to
 get type-safe, validated responses using Pydantic models.
 """
-import asyncio
-import tempfile
-
 from typing import List, Optional
+
 from pydantic import BaseModel, Field
 from strands import Agent
 
+
 def basic_example():
     """Basic example extracting structured information from text."""
     print("\n--- Basic Example ---")
@@ -22,80 +21,14 @@ class PersonInfo(BaseModel):
         occupation: str
 
     agent = Agent()
-    result = agent.structured_output(
-        PersonInfo,
-        "John Smith is a 30-year-old software engineer"
+    result = agent(
+        "John Smith is a 30-year-old software engineer",
+        structured_output_model=PersonInfo,
     )
 
-    print(f"Name: {result.name}")      # "John Smith"
-    print(f"Age: {result.age}")        # 30
-    print(f"Job: {result.occupation}") # "software engineer"
-
-
-def multimodal_example():
-    """Basic example extracting structured information from a document."""
-    print("\n--- Multi-Modal Example ---")
-
-    class PersonInfo(BaseModel):
-        name: str
-        age: int
-        occupation: str
-
-    with tempfile.NamedTemporaryFile(delete=False) as person_file:
-        person_file.write(b"John Smith is a 30-year old software engineer")
-        person_file.flush()
-
-        with open(person_file.name, "rb") as fp:
-            document_bytes = fp.read()
-
-    agent = Agent()
-    result = agent.structured_output(
-        PersonInfo,
-        [
-            {"text": "Please process this application."},
-            {
-                "document": {
-                    "format": "txt",
-                    "name": "application",
-                    "source": {
-                        "bytes": document_bytes,
-                    },
-                },
-            },
-        ]
-    )
-
-    print(f"Name: {result.name}")      # "John Smith"
-    print(f"Age: {result.age}")        # 30
-    print(f"Job: {result.occupation}") # "software engineer"
-
-
-def conversation_history_example():
-    """Example using conversation history with structured output."""
-    print("\n--- Conversation History Example ---")
-
-    agent = Agent()
-
-    # Build up conversation context
-    print("Building conversation context...")
-    agent("What do you know about Paris, France?")
-    agent("Tell me about the weather there in spring.")
-
-    # Extract structured information without additional prompt
-    class CityInfo(BaseModel):
-        city: str
-        country: str
-        population: Optional[int] = None
-        climate: str
-
-    # Uses existing conversation context with a prompt
-    print("Extracting structured information from conversation context...")
-    result = agent.structured_output(CityInfo, "Extract structured information about Paris")
-
-    print(f"City: {result.city}")
-    print(f"Country: {result.country}")
-    print(f"Population: {result.population}")
-    print(f"Climate: {result.climate}")
+    print(f"Name: {result.structured_output.name}")      # "John Smith"
+    print(f"Age: {result.structured_output.age}")        # 30
+    print(f"Job: {result.structured_output.occupation}") # "software engineer"
 
 
 def complex_nested_model_example():
@@ -121,47 +54,24 @@ class Person(BaseModel):
         skills: List[str] = Field(default_factory=list, description="Professional skills")
 
     agent = Agent()
-    result = agent.structured_output(
-        Person,
-        "Extract info: Jane Doe, a systems admin, 28, lives at 123 Main St, New York, USA. Email: jane@example.com"
-    )
-
-    print(f"Name: {result.name}")                    # "Jane Doe"
-    print(f"Age: {result.age}")                      # 28
-    print(f"Street: {result.address.street}")        # "123 Main St" 
-    print(f"City: {result.address.city}")            # "New York"
-    print(f"Country: {result.address.country}")      # "USA"
-    print(f"Email: {result.contacts[0].email}")      # "jane@example.com"
-    print(f"Skills: {result.skills}")                # ["systems admin"]
-
-
-async def async_example():
-    """Basic example extracting structured information from text asynchronously."""
-    print("\n--- Async Example ---")
-
-    class PersonInfo(BaseModel):
-        name: str
-        age: int
-        occupation: str
-
-    agent = Agent()
-    result = await agent.structured_output_async(
-        PersonInfo,
-        "John Smith is a 30-year-old software engineer"
+    result = agent(
+        "Extract info: Jane Doe, a systems admin, 28, lives at 123 Main St, New York, USA. Email: jane@example.com",
+        structured_output_model=Person,
     )
 
-    print(f"Name: {result.name}")      # "John Smith"
-    print(f"Age: {result.age}")        # 30
-    print(f"Job: {result.occupation}") # "software engineer"
+    print(f"Name: {result.structured_output.name}")                    # "Jane Doe"
+    print(f"Age: {result.structured_output.age}")                      # 28
+    print(f"Street: {result.structured_output.address.street}")        # "123 Main St"
+    print(f"City: {result.structured_output.address.city}")            # "New York"
+    print(f"Country: {result.structured_output.address.country}")      # "USA"
+    print(f"Email: {result.structured_output.contacts[0].email}")      # "jane@example.com"
+    print(f"Skills: {result.structured_output.skills}")                # ["systems admin"]
 
 
 if __name__ == "__main__":
     print("Structured Output Examples\n")
 
     basic_example()
-    multimodal_example()
-    conversation_history_example()
     complex_nested_model_example()
-    asyncio.run(async_example())
 
     print("\nExamples completed.")

docs/examples/typescript/structured_output/README.md

@@ -0,0 +1,27 @@
+# Structured Output Example
+
+Demonstrates how to use structured output with Strands Agents to get type-safe, validated responses using Zod schemas.
+
+## Prerequisites
+
+- Node.js 20+
+- AWS credentials configured for Amazon Bedrock
+
+## Setup
+
+```bash
+npm install
+```
+
+## Run
+
+```bash
+npm start
+```
+
+## What It Covers
+
+- Basic structured output with Zod schemas
+- Complex nested schemas
+
+See the [Structured Output documentation](https://strandsagents.com/docs/examples/structured-output/) for more details.

docs/examples/typescript/structured_output/package.json

@@ -0,0 +1,17 @@
+{
+  "name": "structured-output-example",
+  "version": "1.0.0",
+  "description": "Structured output example using Strands Agents TypeScript SDK with Zod schemas",
+  "type": "module",
+  "scripts": {
+    "start": "npx tsx structured_output.ts"
+  },
+  "dependencies": {
+    "@strands-agents/sdk": "latest",
+    "zod": "^4.1.12"
+  },
+  "devDependencies": {
+    "tsx": "^4.21.0",
+    "typescript": "^5.3.3"
+  }
+}

docs/examples/typescript/structured_output/structured_output.ts

@@ -0,0 +1,76 @@
+/**
+ * Structured Output Example
+ *
+ * This example demonstrates how to use structured output with Strands Agents to
+ * get type-safe, validated responses using Zod schemas.
+ */
+import { Agent } from '@strands-agents/sdk'
+import { z } from 'zod'
+
+async function basicExample(): Promise<void> {
+  console.log('\n--- Basic Example ---')
+
+  const PersonInfo = z.object({
+    name: z.string(),
+    age: z.number(),
+    occupation: z.string(),
+  })
+
+  const agent = new Agent()
+  const result = await agent.invoke('John Smith is a 30-year-old software engineer', {
+    structuredOutputSchema: PersonInfo,
+  })
+
+  console.log(`Name: ${result.structuredOutput.name}`) // "John Smith"
+  console.log(`Age: ${result.structuredOutput.age}`) // 30
+  console.log(`Job: ${result.structuredOutput.occupation}`) // "software engineer"
+}
+
+async function complexNestedSchemaExample(): Promise<void> {
+  console.log('\n--- Complex Nested Schema Example ---')
+
+  const Address = z.object({
+    street: z.string(),
+    city: z.string(),
+    country: z.string(),
+    postalCode: z.string().optional(),
+  })
+
+  const Contact = z.object({
+    email: z.string().optional(),
+    phone: z.string().optional(),
+  })
+
+  const Person = z.object({
+    name: z.string().describe('Full name of the person'),
+    age: z.number().describe('Age in years'),
+    address: Address.describe('Home address'),
+    contacts: z.array(Contact).describe('Contact methods'),
+    skills: z.array(z.string()).describe('Professional skills'),
+  })
+
+  const agent = new Agent()
+  const result = await agent.invoke(
+    'Extract info: Jane Doe, a systems admin, 28, lives at 123 Main St, New York, USA. Email: jane@example.com',
+    { structuredOutputSchema: Person },
+  )
+
+  console.log(`Name: ${result.structuredOutput.name}`) // "Jane Doe"
+  console.log(`Age: ${result.structuredOutput.age}`) // 28
+  console.log(`Street: ${result.structuredOutput.address.street}`) // "123 Main St"
+  console.log(`City: ${result.structuredOutput.address.city}`) // "New York"
+  console.log(`Country: ${result.structuredOutput.address.country}`) // "USA"
+  console.log(`Email: ${result.structuredOutput.contacts[0].email}`) // "jane@example.com"
+  console.log(`Skills: ${result.structuredOutput.skills}`) // ["systems admin"]
+}
+
+async function main(): Promise<void> {
+  console.log('Structured Output Examples\n')
+
+  await basicExample()
+  await complexNestedSchemaExample()
+
+  console.log('\nExamples completed.')
+}
+
+main()

mkdocs.yml

@@ -114,7 +114,7 @@ nav:
         - Amazon Bedrock: user-guide/concepts/model-providers/amazon-bedrock.md
         - Amazon Nova: user-guide/concepts/model-providers/amazon-nova.md
         - Anthropic: user-guide/concepts/model-providers/anthropic.md
-        - Gemini: user-guide/concepts/model-providers/gemini.md
+        - Google: user-guide/concepts/model-providers/google.md
         - LiteLLM: user-guide/concepts/model-providers/litellm.md
         - llama.cpp: user-guide/concepts/model-providers/llamacpp.md
         - LlamaAPI: user-guide/concepts/model-providers/llamaapi.md

package.json

@@ -48,15 +48,16 @@
     "turndown": "^7.2.2",
     "turndown-plugin-gfm": "^1.0.2",
     "typedoc": "^0.28.14",
-    "typescript": "^5.9.3"
+    "typescript": "^5.9.3",
+    "vite": "^8.0.0"
   },
   "devDependencies": {
-    "@astrojs/starlight": "^0.37.3",
+    "@astrojs/starlight": "^0.38.1",
     "@types/js-yaml": "^4.0.9",
     "@types/mdast": "^4.0.4",
     "@types/node": "^24.10.1",
     "acorn": "^8.14.0",
-    "astro": "^5.6.1",
+    "astro": "^6.0.5",
     "js-yaml": "^4.1.1",
     "pino": "^9.7.0",
     "pino-pretty": "^13.0.0",

src/config/navigation.yml

@@ -66,6 +66,7 @@ sidebar:
               - docs/user-guide/concepts/tools/mcp-tools
               - docs/user-guide/concepts/tools/executors
               - docs/user-guide/concepts/tools/community-tools-package
+              - docs/user-guide/concepts/tools/vended-tools
           - label: Plugins
             items:
               - docs/user-guide/concepts/plugins
@@ -77,7 +78,7 @@ sidebar:
               - docs/user-guide/concepts/model-providers/amazon-bedrock
               - docs/user-guide/concepts/model-providers/amazon-nova
               - docs/user-guide/concepts/model-providers/anthropic
-              - docs/user-guide/concepts/model-providers/gemini
+              - docs/user-guide/concepts/model-providers/google
               - docs/user-guide/concepts/model-providers/litellm
               - docs/user-guide/concepts/model-providers/llamacpp
               - docs/user-guide/concepts/model-providers/llamaapi
@@ -189,7 +190,7 @@ sidebar:
       - docs/examples/python/file_operations
       - docs/examples/python/agents_workflows
       - docs/examples/python/knowledge_base_agent
-      - docs/examples/python/structured_output
+      - docs/examples/structured_output
       - docs/examples/python/multi_agent_example/multi_agent_example
       - docs/examples/python/graph_loops_example
       - docs/examples/python/meta_tooling
@@ -217,6 +218,7 @@ sidebar:
           - docs/community/model-providers/sglang
           - docs/community/model-providers/vllm
           - docs/community/model-providers/mlx
+          - docs/community/model-providers/ovhcloud-ai-endpoints
           - docs/community/model-providers/xai
       - label: Session Managers
         items: