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chore: docs sync from main — needs review (2026-04-24)
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showcase/shell-docs/src/content/docs/integrations/agent-spec/quickstart.mdx

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@@ -108,7 +108,6 @@ hideTOC: true
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Clone the starter template
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```bash
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# If you are in the path integrations/agent-spec/python, then run the following command to go to root
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git clone https://github.com/CopilotKit/with-agent-spec.git
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cd with-agent-spec
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```

showcase/shell-docs/src/content/docs/integrations/built-in-agent/server-tools.mdx

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@@ -21,19 +21,20 @@ import { z } from "zod";
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const getWeather = defineTool({
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name: "getWeather",
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description: "Get the current weather for a city",
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description: "Get the current weather for a location",
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parameters: z.object({
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city: z.string().describe("The city name"),
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location: z.string().describe("The location's name"),
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}),
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execute: async ({ city }) => {
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execute: async ({ location }) => {
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// Your implementation here
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return { temperature: 72, condition: "sunny", city };
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return { temperature: 72, condition: "sunny", location };
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},
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});
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const builtInAgent = new BuiltInAgent({
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model: "openai:gpt-5.4-mini",
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tools: [getWeather],
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maxSteps: 2 //Important for tool calls
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});
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```
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@@ -75,6 +76,7 @@ const createTicket = defineTool({
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const builtInAgent = new BuiltInAgent({
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model: "openai:gpt-5.4-mini",
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tools: [searchDocs, createTicket], // [!code highlight]
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maxSteps: 2
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});
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```
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showcase/shell-docs/src/content/docs/integrations/langgraph/deep-agents.mdx

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@@ -3,7 +3,6 @@ title: Deep Agents
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description: Leverage LangGraph Deep Agents to build sophisticated agentic applications.
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icon: "lucide/BrainCircuit"
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---
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## Prerequisites
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Before you begin, you'll need the following:
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- A LangSmith API key - only required if deploying to LangSmith Platform
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## Getting started
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<Steps>
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<Step>
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### Initialize your agent project
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return f"The weather in {location} is sunny."
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agent = create_deep_agent(
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model="openai:gpt-5.2",
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model="openai:gpt-5.4",
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tools=[get_weather],
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middleware=[CopilotKitMiddleware()], # for frontend tools and context
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system_prompt="You are a helpful research assistant.",
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checkpointer=MemorySaver()
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)
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graph = agent
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```
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Then to test and deploy with LangSmith, we'll also need a `langgraph.json`
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Then create a simple LangGraph agent, add a FastAPI app, and build attach our agent as an AG-UI endpoint.
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```python title="main.py"
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import os
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from fastapi import FastAPI
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import uvicorn
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# [!code highlight:2]
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from ag_ui_langgraph import add_langgraph_fastapi_endpoint
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return f"The weather in {location} is sunny."
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agent = create_deep_agent(
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model="openai:gpt-5.2",
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model="openai:gpt-5.4",
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tools=[get_weather],
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middleware=[CopilotKitMiddleware()], # for frontend tools and context
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system_prompt="You are a helpful research assistant.",
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checkpointer=MemorySaver()
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)
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app = FastAPI()
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# [!code highlight:9]
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add_langgraph_fastapi_endpoint(
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app=app,
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uvicorn.run(
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"main:app",
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host="0.0.0.0",
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port="8123",
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port=8123,
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reload=True,
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)
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<Tabs groupId="deployment_method" items={['LangSmith', 'FastAPI']}>
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<Tab value="LangSmith">
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```tsx title="app/api/copilotkit/route.ts"
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import {
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CopilotRuntime,
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ExperimentalEmptyAdapter,
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copilotRuntimeNextJSAppRouterEndpoint,
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} from "@copilotkit/runtime";
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// [!code highlight]
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import { LangGraphAgent } from "@copilotkit/runtime/langgraph";
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import { NextRequest } from "next/server";
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const serviceAdapter = new ExperimentalEmptyAdapter();
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</Tab>
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<Tab value="FastAPI">
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```tsx title="app/api/copilotkit/route.ts"
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import {
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CopilotRuntime,
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ExperimentalEmptyAdapter,
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copilotRuntimeNextJSAppRouterEndpoint,
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} from "@copilotkit/runtime";
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// [!code highlight]
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import { LangGraphHttpAgent } from "@copilotkit/runtime/langgraph";
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import { NextRequest } from "next/server";
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const serviceAdapter = new ExperimentalEmptyAdapter();
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```tsx title="app/layout.tsx"
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// [!code highlight:2]
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import { CopilotKit } from "@copilotkit/react-core";
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import "@copilotkit/react-ui/v2/styles.css";
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// ...
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```tsx title="app/page.tsx"
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"use client";
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import { CopilotSidebar } from "@copilotkit/react-core/v2";
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import { useDefaultRenderTool } from "@copilotkit/react-core/v2";
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export default function Page() {
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useDefaultRenderTool({
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</Tab>
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</Tabs>
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</Step>
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</Steps>
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</Steps>

showcase/shell-docs/src/content/docs/integrations/microsoft-agent-framework/quickstart.mdx

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@@ -233,6 +233,7 @@ Before you begin, you'll need the following:
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```bash
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uv init my-agent
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cd my-agent
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uv add agent-framework-ag-ui python-dotenv uvicorn agent-framework-openai
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```
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Create a minimal FastAPI server that exposes a Microsoft Agent Framework agent over AG-UI:
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# (optional) AZURE_OPENAI_API_KEY=...
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# Run the agent
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cd agent
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uv run src/byo_agent.py
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uv run main.py
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```
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</Tab>
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</Tabs>

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