智能体概述

npm install @mastra/core@beta

SILICONFLOW_API_KEY=<your-api-key>

import { Agent } from "@mastra/core/agent";

export const testAgent = new Agent({
  id: "test-agent",
  name: "Test Agent",
  instructions: "You are a helpful assistant.",
  model: "siliconflow-cn/Qwen/Qwen3-Coder-30B-A3B-Instruct",
});

// String (most common)
instructions: "You are a helpful assistant.";

// Array of strings
instructions: [
  "You are a helpful assistant.",
  "Always be polite.",
  "Provide detailed answers.",
];

// Array of system messages
// 这里和 LLM 的格式是一致的s
instructions: [
  { role: "system", content: "You are a helpful assistant." },
  { role: "system", content: "You have expertise in TypeScript." },
];

// With provider-specific options (e.g., caching, reasoning)
instructions: {
  role: "system",
  content:
    "You are an expert code reviewer. Analyze code for bugs, performance issues, and best practices.",
  providerOptions: {
    openai: { reasoningEffort: "high" },        // OpenAI's reasoning models
    anthropic: { cacheControl: { type: "ephemeral" } }  // Anthropic's prompt caching
  }
}

import { Mastra } from "@mastra/core";
import { testAgent } from "./agents/test-agent";

export const mastra = new Mastra({
  agents: { testAgent },
});

const testAgent = mastra.getAgent("testAgent");

The image is a black-and-white photograph depicting a bear standing in water. The bear is shown in profile, facing to the right, with its head slightly raised and its gaze directed forward. Its fur appears thick and textured, with varying shades of gray that suggest a natural, rugged coat. The bear’s body is partially submerged, with water reaching up to its midsection, and ripples are visible around it, indicating gentle movement in the water. The background consists of a calm, rippling water surface that extends to the horizon, with no visible land or other objects. The lighting is soft and even, enhancing the textures of the bear’s fur and the water’s surface. The overall mood of the image is serene and naturalistic.

const response = await testAgent.generate("Help me organize my day", {
  maxSteps: 10,
});

console.log(response.text);

const response = await testAgent.generate("Help me organize my day", {
  onStepFinish: ({ text, toolCalls, toolResults, finishReason, usage }) => {
    console.log({ text, toolCalls, toolResults, finishReason, usage });
  },
});

export const testAgent = new Agent({
  id: "test-agent",
  instructions: "You are a helpful assistant.",
  name: "Test Agent",
  tools: { testTool },
});

export type UserTier = {
  "user-tier": "enterprise" | "pro";
};

export const testAgent = new Agent({
  id: "test-agent",
  name: "Test Agent",
  model: ({ requestContext }) => {
    const userTier = requestContext.get("user-tier") as UserTier["user-tier"];

    return userTier === "enterprise"
      ? "openai/gpt-5"
      : "openai/gpt-4.1-nano";
  },
});

import { RequestContext } from "@mastra/core/request-context";

export type UserTier = {
  "user-tier": "enterprise" | "pro";
};

const requestContext = new RequestContext<UserTier>();
requestContext.set("user-tier", "enterprise");
const agent = mastra.getAgent("weatherAgent");
await agent.generate("What's the weather in London?", {
  requestContext,
});