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devops-gitops-corpus

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console.log(result.object); // Type-safe JSON object
```
This feature is especially useful for **data extraction** and **API responses**. You're not saying "give it in JSON format" and then trying to parse it anymore.
### Tool Integration: Real World Connection
We added MCP (Model Context Protocol) support in the tool integration part. This really became a game-changing feature:
```typescript
// Define local tool
const weatherTool = createTool({
name: "get_weather",
description: "Get the current weather for a specific location",
parameters: z.object({
location: z.string().describe("City and state"),
}),
execute: async ({ location }) => {
// Real API call would be here
return { temperature: 72, conditions: "sunny" };
},
});
// Connect to external MCP server
const mcpTools = await connectMCPServer("stdio://weather-server");
const agent = new Agent({
name: "Weather Assistant",
instructions: "Can check weather using available tools",
llm: new VercelAIProvider(),
model: openai("gpt-4o"),
tools: [weatherTool, ...mcpTools], // Combine both
});
```
The agent decides which tool to use when by itself. You just say "How's the weather in London?", it calls its own tool and brings you the result.
### Memory: Context Management
We also carefully designed the memory system. It's critical for agents to remember past conversations:
```typescript
import { LibSQLStorage } from "@voltagent/core";
const memoryStorage = new LibSQLStorage({
url: "file:local.db",
});
const agent = new Agent({
name: "Assistant with Memory",
instructions: "Remember our conversation history",
llm: new VercelAIProvider(),
model: openai("gpt-4o"),
memory: memoryStorage, // Automatic context management
});
// First conversation
await agent.generateText("My name is John and I love pizza");
// Next conversation - will remember the previous one
await agent.generateText("What's my favorite food?");
// "Based on our previous conversation, you love pizza!"
```
The framework automatically fetches relevant context and saves new interactions.
### Multi-Agent Systems
One of my favorite features is the sub-agent system. You can break complex tasks into small pieces and distribute them to expert agents:
```typescript
const researchAgent = new Agent({
name: "Researcher",
instructions: "Research topics thoroughly using web search",
tools: [webSearchTool],
});
const writerAgent = new Agent({
name: "Writer",
instructions: "Write engaging content based on research",
tools: [contentGenerator],
});
const coordinator = new Agent({
name: "Coordinator",
instructions: "Coordinate research and writing tasks",
llm: new VercelAIProvider(),
model: openai("gpt-4o"),
subAgents: [researchAgent, writerAgent], // Automatic delegate_task tool
});
// Complex workflow in a single call
await coordinator.generateText("Write a blog post about quantum computing");
// Coordinator will give research to researcher, writing to writer
```
:::important
Memory management and tool integration are the foundation of production-ready agents. Without these, you'll hit scaling issues quickly as your application grows.
:::