入门

本指南将帮助你安装 Koog 并创建你的第一个 AI 代理。

前提条件

在开始之前，请确保你已具备以下条件：

• 一个使用 Gradle 或 Maven 的 Kotlin/JVM 项目。
• 已安装 Java 17+。
• 你首选的 LLM 提供商的有效 API 密钥（Ollama 无需此密钥，因为它在本地运行）。

安装 Koog

要使用 Koog，你需要在构建配置中包含所有必要的依赖项。

NOTE

将 LATEST_VERSION 替换为 Maven Central 上发布的最新 Koog 版本。

=== "Gradle (Kotlin DSL)"

1. 将依赖项添加到 `build.gradle.kts` 文件。

    ```kotlin
    dependencies {
        implementation("ai.koog:koog-agents:LATEST_VERSION")
    }
    ```
2. 确保 `mavenCentral()` 在版本库列表中。

    ```kotlin
    repositories {
        mavenCentral()
    }
    ```

=== "Gradle (Groovy)"

1. 将依赖项添加到 `build.gradle` 文件。

    ```groovy
    dependencies {
        implementation 'ai.koog:koog-agents:LATEST_VERSION'
    }
    ```
2. 确保 `mavenCentral()` 在版本库列表中。
    ```groovy
    repositories {
        mavenCentral()
    }
    ```

=== "Maven"

1. 将依赖项添加到 `pom.xml` 文件。

    ```xml
    <dependency>
        <groupId>ai.koog</groupId>
        <artifactId>koog-agents-jvm</artifactId>
        <version>LATEST_VERSION</version>
    </dependency>
    ```
2. 确保 `mavenCentral()` 在版本库列表中。

    ```xml
     <repositories>
        <repository>
            <id>mavenCentral</id>
            <url>https://repo1.maven.org/maven2/</url>
        </repository>
    </repositories>
    ```

NOTE

将 Koog 与 Ktor 服务器、Spring 应用程序或 MCP 工具集成时， 你需要在构建配置中包含额外的依赖项。 关于确切的依赖项，请参考 Koog 文档中的相关页面。

设置 API 密钥

TIP

使用环境变量或安全的配置管理系统来存储你的 API 密钥。 避免将 API 密钥直接硬编码到你的源代码中。

=== "OpenAI"

获取你的 [API 密钥](https://platform.openai.com/api-keys)并将其作为环境变量赋值。

=== "Linux/macOS"

    ```bash
    export OPENAI_API_KEY=your-api-key
    ```

=== "Windows"

    ```shell
    setx OPENAI_API_KEY "your-api-key"
    ```

重启你的终端以应用更改。你现在可以检索并使用 API 密钥来创建代理。

=== "Anthropic"

获取你的 [API 密钥](https://console.anthropic.com/settings/keys)并将其作为环境变量赋值。

=== "Linux/macOS"

    ```bash
    export ANTHROPIC_API_KEY=your-api-key
    ```

=== "Windows"

    ```shell
    setx ANTHROPIC_API_KEY "your-api-key"
    ```

重启你的终端以应用更改。你现在可以检索并使用 API 密钥来创建代理。

=== "Google"

获取你的 [API 密钥](https://aistudio.google.com/app/api-keys)并将其作为环境变量赋值。

=== "Linux/macOS"

    ```bash
    export GOOGLE_API_KEY=your-api-key
    ```

=== "Windows"

    ```shell
    setx GOOGLE_API_KEY "your-api-key"
    ```

重启你的终端以应用更改。你现在可以检索并使用 API 密钥来创建代理。

=== "DeepSeek"

获取你的 [API 密钥](https://platform.deepseek.com/api_keys)并将其作为环境变量赋值。

=== "Linux/macOS"

    ```bash
    export DEEPSEEK_API_KEY=your-api-key
    ```

=== "Windows"

    ```shell
    setx DEEPSEEK_API_KEY "your-api-key"
    ```

重启你的终端以应用更改。你现在可以检索并使用 API 密钥来创建代理。

=== "OpenRouter"

获取你的 [API 密钥](https://openrouter.ai/keys)并将其作为环境变量赋值。

=== "Linux/macOS"

    ```bash
    export OPENROUTER_API_KEY=your-api-key
    ```

=== "Windows"

    ```shell
    setx OPENROUTER_API_KEY "your-api-key"
    ```

重启你的终端以应用更改。你现在可以检索并使用 API 密钥来创建代理。

=== "Bedrock"

获取有效的 [AWS 凭据](https://docs.aws.amazon.com/IAM/latest/UserGuide/id_credentials_bedrock.html)（访问密钥和秘密密钥）并将其作为环境变量赋值。

=== "Linux/macOS"

    ```bash
    export AWS_BEDROCK_ACCESS_KEY=your-access-key
    export AWS_BEDROCK_SECRET_ACCESS_KEY=your-secret-access-key
    ``` 

=== "Windows"

    ```shell
    setx AWS_BEDROCK_ACCESS_KEY "your-access-key"
    setx AWS_BEDROCK_SECRET_ACCESS_KEY "your-secret-access-key"
    ```

重启你的终端以应用更改。你现在可以检索并使用 API 密钥来创建代理。

=== "Ollama"

安装 Ollama 并在本地运行模型，无需 API 密钥。

关于更多信息，请参见 [Ollama 文档](https://docs.ollama.com/quickstart)。

创建并运行代理

=== "OpenAI"

下面的示例使用 [`GPT-4o`](https://platform.openai.com/docs/models/gpt-4o) 模型创建并运行一个简单的 AI 代理。

<!--- INCLUDE
import ai.koog.agents.core.agent.AIAgent
import ai.koog.prompt.executor.llms.all.simpleOpenAIExecutor
import ai.koog.prompt.executor.clients.openai.OpenAIModels
import kotlinx.coroutines.runBlocking
-->
```kotlin
fun main() = runBlocking {
    // 从 OPENAI_API_KEY 环境变量获取 API 密钥
    val apiKey = System.getenv("OPENAI_API_KEY")
        ?: error("The API key is not set.")
    
    // 创建代理
    val agent = AIAgent(
        promptExecutor = simpleOpenAIExecutor(apiKey),
        llmModel = OpenAIModels.Chat.GPT4o
    )

    // 运行代理
    val result = agent.run("Hello! How can you help me?")
    println(result)
}
```
<!--- KNIT example-getting-started-01.kt -->

该示例可以产生以下输出：

```
Hello! I'm here to help you with whatever you need. Here are just a few things I can do:

- Answer questions.
- Explain concepts or topics you're curious about.
- Provide step-by-step instructions for tasks.
- Offer advice, notes, or ideas.
- Help with research or summarize complex material.
- Write or edit text, emails, or other documents.
- Brainstorm creative projects or solutions.
- Solve problems or calculations.

Let me know what you need help with—I’m here for you!
```

=== "Anthropic"

下面的示例使用 [`Claude Opus 4.1`](https://www.anthropic.com/news/claude-opus-4-1) 模型创建并运行一个简单的 AI 代理。

<!--- INCLUDE
import ai.koog.agents.core.agent.AIAgent
import ai.koog.prompt.executor.llms.all.simpleAnthropicExecutor
import ai.koog.prompt.executor.clients.anthropic.AnthropicModels
import kotlinx.coroutines.runBlocking
-->
```kotlin
fun main() = runBlocking {
    // 从 ANTHROPIC_API_KEY 环境变量获取 API 密钥
    val apiKey = System.getenv("ANTHROPIC_API_KEY")
        ?: error("The API key is not set.")
    
    // 创建代理
    val agent = AIAgent(
        promptExecutor = simpleAnthropicExecutor(apiKey),
        llmModel = AnthropicModels.Opus_4_1
    )

    // 运行代理
    val result = agent.run("Hello! How can you help me?")
    println(result)
}
```
<!--- KNIT example-getting-started-02.kt -->

该示例可以产生以下输出：

```
Hello! I can help you with:

- **Answering questions** and explaining topics
- **Writing** - drafting, editing, proofreading
- **Learning** - homework, math, study help
- **Problem-solving** and brainstorming
- **Research** and information finding
- **General tasks** - instructions, planning, recommendations

What do you need help with today?
```

=== "Google"

下面的示例使用 [`Gemini 2.5 Pro`](https://cloud.google.com/vertex-ai/generative-ai/docs/models/gemini/2-5-pro) 模型创建并运行一个简单的 AI 代理。

<!--- INCLUDE
import ai.koog.agents.core.agent.AIAgent
import ai.koog.prompt.executor.llms.all.simpleGoogleAIExecutor
import ai.koog.prompt.executor.clients.google.GoogleModels
import kotlinx.coroutines.runBlocking
-->
```kotlin
fun main() = runBlocking {
    // 从 GOOGLE_API_KEY 环境变量获取 API 密钥
    val apiKey = System.getenv("GOOGLE_API_KEY")
        ?: error("The API key is not set.")
    
    // 创建代理
    val agent = AIAgent(
        promptExecutor = simpleGoogleAIExecutor(apiKey),
        llmModel = GoogleModels.Gemini2_5Pro
    )

    // 运行代理
    val result = agent.run("Hello! How can you help me?")
    println(result)
}
```
<!--- KNIT example-getting-started-03.kt -->

该示例可以产生以下输出：

```
I'm an AI that can help you with tasks involving language and information. You can ask me to:

*   **Answer questions**
*   **Write or edit text** (emails, stories, code, etc.)
*   **Brainstorm ideas**
*   **Summarize long documents**
*   **Plan things** (like trips or projects)
*   **Be a creative partner**

Just tell me what you need
```

=== "DeepSeek"

下面的示例使用 `deepseek-chat` 模型创建并运行一个简单的 AI 代理。

<!--- INCLUDE
import ai.koog.agents.core.agent.AIAgent
import ai.koog.prompt.executor.clients.deepseek.DeepSeekLLMClient
import ai.koog.prompt.executor.llms.SingleLLMPromptExecutor
import ai.koog.prompt.executor.clients.deepseek.DeepSeekModels
import kotlinx.coroutines.runBlocking
-->
```kotlin
fun main() = runBlocking {
    // 从 DEEPSEEK_API_KEY 环境变量获取 API 密钥
    val apiKey = System.getenv("DEEPSEEK_API_KEY")
        ?: error("The API key is not set.")
    
    // 创建 LLM 客户端
    val deepSeekClient = DeepSeekLLMClient(apiKey)

    // 创建代理
    val agent = AIAgent(
        // 使用 LLM 客户端创建提示执行器
        promptExecutor = SingleLLMPromptExecutor(deepSeekClient),
        // 提供模型
        llmModel = DeepSeekModels.DeepSeekChat
    )

    // 运行代理
    val result = agent.run("Hello! How can you help me?")
    println(result)
}
```
<!--- KNIT example-getting-started-04.kt -->

该示例可以产生以下输出：

```
Hello! I'm here to assist you with a wide range of tasks, including answering questions, providing information, helping with problem-solving, offering creative ideas, and even just chatting. Whether you need help with research, writing, learning something new, or simply want to discuss a topic, feel free to ask—I’m happy to help! 😊
```

=== "OpenRouter"

下面的示例使用 [`GPT-4o`](https://openrouter.ai/openai/gpt-4o) 模型创建并运行一个简单的 AI 代理。

<!--- INCLUDE
import ai.koog.agents.core.agent.AIAgent
import ai.koog.prompt.executor.llms.all.simpleOpenRouterExecutor
import ai.koog.prompt.executor.clients.openrouter.OpenRouterModels
import kotlinx.coroutines.runBlocking
-->
```kotlin
fun main() = runBlocking {
    // 从 OPENROUTER_API_KEY 环境变量获取 API 密钥
    val apiKey = System.getenv("OPENROUTER_API_KEY")
        ?: error("The API key is not set.")
    
    // 创建代理
    val agent = AIAgent(
        promptExecutor = simpleOpenRouterExecutor(apiKey),
        llmModel = OpenRouterModels.GPT4o
    )

    // 运行代理
    val result = agent.run("Hello! How can you help me?")
    println(result)
}
```
<!--- KNIT example-getting-started-05.kt -->

该示例可以产生以下输出：

```
I can answer questions, help with writing, solve problems, organize tasks, and more—just let me know what you need!
```

=== "Bedrock"

下面的示例使用 [`Claude Sonnet 4.5`](https://www.anthropic.com/news/claude-sonnet-4-5) 模型创建并运行一个简单的 AI 代理。

<!--- INCLUDE
import ai.koog.agents.core.agent.AIAgent
import ai.koog.prompt.executor.llms.all.simpleBedrockExecutor
import ai.koog.prompt.executor.clients.bedrock.BedrockModels
import kotlinx.coroutines.runBlocking
-->
```kotlin
fun main() = runBlocking {
    // 从 AWS_BEDROCK_ACCESS_KEY 和 AWS_BEDROCK_SECRET_ACCESS_KEY 环境变量获取访问密钥
    val awsAccessKeyId = System.getenv("AWS_BEDROCK_ACCESS_KEY")
        ?: error("The access key is not set.")

    val awsSecretAccessKey = System.getenv("AWS_BEDROCK_SECRET_ACCESS_KEY")
        ?: error("The secret access key is not set.")
    
    // 创建代理
    val agent = AIAgent(
        promptExecutor = simpleBedrockExecutor(awsAccessKeyId, awsSecretAccessKey),
        llmModel = BedrockModels.AnthropicClaude4_5Sonnet
    )

    // 运行代理
    val result = agent.run("Hello! How can you help me?")
    println(result)
}
```
<!--- KNIT example-getting-started-06.kt -->

该示例可以产生以下输出：

```
Hello! I'm a helpful assistant and I can assist you in many ways, including:

- **Answering questions** on a wide range of topics (science, history, technology, etc.)
- **Writing help** - drafting emails, essays, creative content, or editing text
- **Problem-solving** - working through math problems, logic puzzles, or troubleshooting issues
- **Learning support** - explaining concepts, providing study notes, or tutoring
- **Planning & organizing** - helping with projects, schedules, or breaking down tasks
- **Coding assistance** - explaining programming concepts or helping debug code
- **Creative brainstorming** - generating ideas for projects, stories, or solutions
- **General conversation** - discussing topics or just chatting

 What would you like help with today?
```

=== "Ollama"

下面的示例使用 [`llama3.2`](https://ollama.com/library/llama3.2) 模型创建并运行一个简单的 AI 代理。

<!--- INCLUDE
import ai.koog.agents.core.agent.AIAgent
import ai.koog.prompt.executor.llms.all.simpleOllamaAIExecutor
import ai.koog.prompt.llm.OllamaModels
import kotlinx.coroutines.runBlocking
-->
```kotlin
fun main() = runBlocking {
    // 创建代理
    val agent = AIAgent(
        promptExecutor = simpleOllamaAIExecutor(),
        llmModel = OllamaModels.Meta.LLAMA_3_2
    )

    // 运行代理
    val result = agent.run("Hello! How can you help me?")
    println(result)
}
```
<!--- KNIT example-getting-started-07.kt -->

该示例可以产生以下输出：

```
I can assist with various tasks such as answering questions, providing information, and even helping with language-related tasks like proofreading or writing suggestions. What's on your mind today?
```

接下来

• 探索 Koog 的 主要特性。
• 了解更多可用的 代理类型。