构建内容创作智能体 - Docs by LangChain

概述

本指南演示如何使用深度智能体从头开始构建一个内容写作智能体。你构建的智能体将：

从 AGENTS.md 和技能文件夹加载语气和工作流规则
使用 web_search 将网络研究委派给专门的子智能体
按照加载的技能撰写博客或社交媒体内容
使用 Gemini 生成封面或社交图片并将文件保存在项目目录下

本教程中的代码接入了图片生成工具和文件系统后端，以便智能体可以在项目目录下读写帖子、研究笔记和图片。有关完整的可运行项目，请参阅 content-builder-agent 示例。

核心概念

本教程涵盖：

长期记忆用于 TODO
技能用于 TODO
子智能体用于 TODO
文件系统后端用于文件读写
自定义工具用于搜索和图片生成

前提条件

API 密钥：

Anthropic (Claude) 或其他提供商 API 密钥
Google (Gemini) 用于 gemini-2.5-flash-image 图片生成
Tavily 用于网络搜索（免费层级）
LangSmith 用于追踪（可选）

Node.js 18 或更高版本。

设置

创建项目目录

mkdir content-builder-agent
cd content-builder-agent

安装依赖

npm install deepagents @langchain/core @langchain/anthropic @google/generative-ai tavily zod tsx

添加 tsx 来运行 content_writer.ts。--input-type=module 标志仅适用于 --eval、--print 或 stdin，不适用于脚本文件路径。安装 @langchain/anthropic 以便 LangChain 可以加载 createDeepAgent 使用的默认 Claude 模型。

设置 API 密钥

export ANTHROPIC_API_KEY="your_anthropic_api_key"
export GOOGLE_API_KEY="your_google_api_key"
export TAVILY_API_KEY="your_tavily_api_key"           # 可选
export LANGSMITH_API_KEY="your_langsmith_api_key"     # 可选

添加配置文件

该示例将行为保存在三种文件中：记忆、技能和子智能体定义。

添加 AGENTS.md

在项目根目录创建 AGENTS.md。当你稍后创建智能体并将此文件指定为记忆参数的一部分时，它会被加载到系统提示词中，以便品牌语气和研究要求适用于每次运行。

# Content Writer Agent

You are a content writer for a technology company. Your job is to create engaging, informative content that educates readers about AI, software development, and emerging technologies.

## Brand Voice

- **Professional but approachable**: Write like a knowledgeable colleague, not a textbook
- **Clear and direct**: Avoid jargon unless necessary; explain technical concepts simply
- **Confident but not arrogant**: Share expertise without being condescending
- **Engaging**: Use concrete examples, analogies, and stories to illustrate points

## Writing Standards

1. **Use active voice**: "The agent processes requests" not "Requests are processed by the agent"
2. **Lead with value**: Start with what matters to the reader, not background
3. **One idea per paragraph**: Keep paragraphs focused and scannable
4. **Concrete over abstract**: Use specific examples, numbers, and case studies
5. **End with action**: Every piece should leave the reader knowing what to do next

## Content Pillars

Our content focuses on:
- AI agents and automation
- Developer tools and productivity
- Software architecture and best practices
- Emerging technologies and trends

## Formatting Guidelines

- Use headers (H2, H3) to break up long content
- Include code examples where relevant (with syntax highlighting)
- Add bullet points for lists of 3+ items
- Keep sentences under 25 words when possible
- Include a clear call-to-action at the end

## Research Requirements

Before writing on any topic:
1. Use the `researcher` subagent for in-depth topic research
2. Gather at least 3 credible sources
3. Identify the key points readers need to understand
4. Find concrete examples or case studies to illustrate concepts

要使此智能体遵循你自己的语气、内容支柱和格式规则，请更新 AGENTS.md 中的文本。

添加技能

创建 skills/ 目录。每个技能是一个包含 SKILL.md 文件的文件夹，文件中有 YAML frontmatter（name、description）和技能说明。创建 skills/blog-post/SKILL.md 并将以下文本复制到其中，其中包含创建长篇帖子、优化 SEO 内容和生成封面图片的信息。

---
name: blog-post
description: Writes and structures long-form blog posts, creates tutorial outlines, and optimizes content for SEO with cover image generation. Use when the user asks to write a blog post, article, how-to guide, tutorial, technical writeup, thought leadership piece, or long-form content.
---

# Blog Post Writing Skill

## Research First (Required)

**Before writing any blog post, you MUST delegate research:**

1. Use the `task` tool with `subagent_type: "researcher"`
2. In the description, specify BOTH the topic AND where to save:

```
task(
    subagent_type="researcher",
    description="Research [TOPIC]. Save findings to research/[slug].md"
)
```

Example:
```
task(
    subagent_type="researcher",
    description="Research the current state of AI agents in 2025. Save findings to research/ai-agents-2025.md"
)
```

3. After research completes, read the findings file before writing

## Output Structure (Required)

**Every blog post MUST have both a post AND a cover image:**

```
blogs/
└── <slug>/
    ├── post.md        # The blog post content
    └── hero.png       # REQUIRED: Generated cover image
```

Example: A post about "AI Agents in 2025" → `blogs/ai-agents-2025/`

**You MUST complete both steps:**
1. Write the post to `blogs/<slug>/post.md`
2. Generate a cover image using `generate_image` and save to `blogs/<slug>/hero.png`

**A blog post is NOT complete without its cover image.**

## Blog Post Structure

Every blog post should follow this structure:

### 1. Hook (Opening)
- Start with a compelling question, statistic, or statement
- Make the reader want to continue
- Keep it to 2-3 sentences

### 2. Context (The Problem)
- Explain why this topic matters
- Describe the problem or opportunity
- Connect to the reader's experience

### 3. Main Content (The Solution)
- Break into 3-5 main sections with H2 headers
- Each section covers one key point
- Include code examples, diagrams, or screenshots where helpful
- Use bullet points for lists

### 4. Practical Application
- Show how to apply the concepts
- Include step-by-step instructions if applicable
- Provide code snippets or templates

### 5. Conclusion & CTA
- Summarize key takeaways (3 bullets max)
- End with a clear call-to-action
- Link to related resources

## Cover Image Generation

After writing the post, generate a cover image using the `generate_cover` tool:

```
generate_cover(prompt="A detailed description of the image...", slug="your-blog-slug")
```

The tool saves the image to `blogs/<slug>/hero.png`.

### Writing Effective Image Prompts

Structure your prompt with these elements:

1. **Subject**: What is the main focus? Be specific and concrete.
2. **Style**: Art direction (minimalist, isometric, flat design, 3D render, watercolor, etc.)
3. **Composition**: How elements are arranged (centered, rule of thirds, symmetrical)
4. **Color palette**: Specific colors or mood (warm earth tones, cool blues and purples, high contrast)
5. **Lighting/Atmosphere**: Soft diffused light, dramatic shadows, golden hour, neon glow
6. **Technical details**: Aspect ratio considerations, negative space for text overlay

### Example Prompts

**For a technical blog post:**
```
Isometric 3D illustration of interconnected glowing cubes representing AI agents, each cube has subtle circuit patterns. Cubes connected by luminous data streams. Deep navy background (#0a192f) with electric blue (#64ffda) and soft purple (#c792ea) accents. Clean minimal style, lots of negative space at top for title. Professional tech aesthetic.
```

**For a tutorial/how-to:**
```
Clean flat illustration of hands typing on a keyboard with abstract code symbols floating upward, transforming into lightbulbs and gears. Warm gradient background from soft coral to light peach. Friendly, approachable style. Centered composition with space for text overlay.
```

**For thought leadership:**
```
Abstract visualization of a human silhouette profile merging with geometric neural network patterns. Split composition - organic watercolor texture on left transitioning to clean vector lines on right. Muted sage green and warm terracotta color scheme. Contemplative, forward-thinking mood.
```

## SEO Considerations

- Include the main keyword in the title and first paragraph
- Use the keyword naturally 3-5 times throughout
- Keep the title under 60 characters
- Write a meta description (150-160 characters)

## Quality Checklist

Before finishing:
- [ ] Post saved to `blogs/<slug>/post.md`
- [ ] Hero image generated at `blogs/<slug>/hero.png`
- [ ] Hook grabs attention in first 2 sentences
- [ ] Each section has a clear purpose
- [ ] Conclusion summarizes key points
- [ ] CTA tells reader what to do next

接下来，创建 skills/social-media/SKILL.md 并将以下文本复制到其中，其中包含撰写社交媒体帖子和生成配图的信息：

---
name: social-media
description: Drafts engaging social media posts, writes hooks, suggests hashtags, creates thread structures, and generates companion images. Use when the user asks to write a LinkedIn post, tweet, Twitter/X thread, social media caption, social post, or repurpose content for social platforms.
---

# Social Media Content Skill

## Research First (Required)

**Before writing any social media content, you MUST delegate research:**

1. Use the `task` tool with `subagent_type: "researcher"`
2. In the description, specify BOTH the topic AND where to save:

```
task(
    subagent_type="researcher",
    description="Research [TOPIC]. Save findings to research/[slug].md"
)
```

Example:
```
task(
    subagent_type="researcher",
    description="Research renewable energy trends in 2025. Save findings to research/renewable-energy.md"
)
```

3. After research completes, read the findings file before writing

## Output Structure (Required)

**Every social media post MUST have both content AND an image:**

**LinkedIn posts:**
```
linkedin/
└── <slug>/
    ├── post.md        # The post content
    └── image.png      # REQUIRED: Generated visual
```

**Twitter/X threads:**
```
tweets/
└── <slug>/
    ├── thread.md      # The thread content
    └── image.png      # REQUIRED: Generated visual
```

Example: A LinkedIn post about "prompt engineering" → `linkedin/prompt-engineering/`

**You MUST complete both steps:**
1. Write the content to the appropriate path
2. Generate an image using `generate_image` and save alongside the post

**A social media post is NOT complete without its image.**

## Platform Guidelines

### LinkedIn

**Format:**
- 1,300 character limit (show more after ~210 chars)
- First line is crucial - make it hook
- Use line breaks for readability
- 3-5 hashtags at the end

**Tone:**
- Professional but personal
- Share insights and learnings
- Ask questions to drive engagement
- Use "I" and share experiences

**Structure:**
```
[Hook - 1 compelling line]

[Empty line]

[Context - why this matters]

[Empty line]

[Main insight - 2-3 short paragraphs]

[Empty line]

[Call to action or question]

#hashtag1 #hashtag2 #hashtag3
```

### Twitter/X

**Format:**
- 280 character limit per tweet
- Threads for longer content (use 1/🧵 format)
- No more than 2 hashtags per tweet

**Thread Structure:**
```
1/🧵 [Hook - the main insight]

2/ [Supporting point 1]

3/ [Supporting point 2]

4/ [Example or evidence]

5/ [Conclusion + CTA]
```

## Image Generation

Every social media post needs an eye-catching image. Use the `generate_social_image` tool:

```
generate_social_image(prompt="A detailed description...", platform="linkedin", slug="your-post-slug")
```

The tool saves the image to `<platform>/<slug>/image.png`.

### Social Image Best Practices

Social images need to work at small sizes in crowded feeds:
- **Bold, simple compositions** - one clear focal point
- **High contrast** - stands out when scrolling
- **No text in image** - too small to read, platforms add their own
- **Square or 4:5 ratio** - works across platforms

### Writing Effective Prompts

Include these elements:

1. **Single focal point**: One clear subject, not a busy scene
2. **Bold style**: Vibrant colors, strong shapes, high contrast
3. **Simple background**: Solid color, gradient, or subtle texture
4. **Mood/energy**: Match the post tone (inspiring, urgent, thoughtful)

### Example Prompts

**For an insight/tip post:**
```
Single glowing lightbulb floating against a deep purple gradient background, lightbulb made of interconnected golden geometric lines, rays of soft light emanating outward. Minimal, striking, high contrast. Square composition.
```

**For announcements/news:**
```
Abstract rocket ship made of colorful geometric shapes launching upward with a trail of particles. Bright coral and teal color scheme against clean white background. Energetic, celebratory mood. Bold flat illustration style.
```

**For thought-provoking content:**
```
Two overlapping translucent circles, one blue one orange, creating a glowing intersection in the center. Represents collaboration or intersection of ideas. Dark charcoal background, soft ethereal glow. Minimalist and contemplative.
```

## Content Types

### Announcement Posts
- Lead with the news
- Explain the impact
- Include link or next step

### Insight Posts
- Share one specific learning
- Explain the context briefly
- Make it actionable

### Question Posts
- Ask a genuine question
- Provide your take first
- Keep it focused on one topic

## Quality Checklist

Before finishing:
- [ ] Post saved to `linkedin/<slug>/post.md` or `tweets/<slug>/thread.md`
- [ ] Image generated alongside the post
- [ ] First line hooks attention
- [ ] Content fits platform limits
- [ ] Tone matches platform norms
- [ ] Has clear CTA or question
- [ ] Hashtags are relevant (not generic)

这些技能指示智能体首先调用 researcher 子智能体，在 blogs/、linkedin/ 或 tweets/ 下写入 markdown，并调用 generate_cover 或 generate_social_image 生成图片。当你稍后创建智能体并指定技能文件夹时，那些技能文件夹中的 SKILLS.md 文件的 frontmatter 会被加载到系统提示词中，以便智能体在任务匹配技能描述时可以使用该技能。

构建脚本

在项目根目录创建 content_writer.ts。以下各部分按顺序属于同一个文件。

添加工具

研究者使用 Tavily 搜索。博客和社交媒体工作流使用 Google Generative AI SDK 进行图片生成。

import { tool } from "@langchain/core/tools";
import * as z from "zod";
import * as fs from "node:fs";
import * as path from "node:path";

const EXAMPLE_DIR = path.dirname(new URL(import.meta.url).pathname);

const webSearch = tool(
  async ({ query, maxResults = 5, topic = "general" }) => {
    const apiKey = process.env.TAVILY_API_KEY;
    if (!apiKey) return { error: "TAVILY_API_KEY not set" };
    try {
      const { TavilyClient } = await import("tavily");
      const client = new TavilyClient({ apiKey });
      return client.search(query, { maxResults, topic });
    } catch (e) {
      return { error: `Search failed: ${e}` };
    }
  },
  {
    name: "web_search",
    description: "Search the web for current information.",
    schema: z.object({
      query: z.string().describe("The search query (be specific and detailed)"),
      maxResults: z
        .number()
        .optional()
        .describe("Number of results to return (default: 5)"),
      topic: z
        .enum(["general", "news"])
        .optional()
        .describe('"general" for most queries, "news" for current events'),
    }),
  },
);

const generateCover = tool(
  async ({ prompt, slug }) => {
    try {
      const { GoogleGenerativeAI } = await import("@google/generative-ai");
      const genai = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY ?? "");
      const model = genai.getGenerativeModel({
        model: "gemini-2.5-flash-image",
      });
      const result = await model.generateContent(prompt);
      const part = result.response.candidates?.[0]?.content?.parts?.find(
        (p) => p.inlineData,
      );
      if (!part?.inlineData) return "No image generated";
      const outputPath = path.join(EXAMPLE_DIR, "blogs", slug, "hero.png");
      fs.mkdirSync(path.dirname(outputPath), { recursive: true });
      fs.writeFileSync(outputPath, Buffer.from(part.inlineData.data, "base64"));
      return `Image saved to ${outputPath}`;
    } catch (e) {
      return `Error: ${e}`;
    }
  },
  {
    name: "generate_cover",
    description: "Generate a cover image for a blog post.",
    schema: z.object({
      prompt: z
        .string()
        .describe("Detailed description of the image to generate."),
      slug: z
        .string()
        .describe("Blog post slug. Image saves to blogs/<slug>/hero.png"),
    }),
  },
);

const generateSocialImage = tool(
  async ({ prompt, platform, slug }) => {
    try {
      const { GoogleGenerativeAI } = await import("@google/generative-ai");
      const genai = new GoogleGenerativeAI(process.env.GOOGLE_API_KEY ?? "");
      const model = genai.getGenerativeModel({
        model: "gemini-2.5-flash-image",
      });
      const result = await model.generateContent(prompt);
      const part = result.response.candidates?.[0]?.content?.parts?.find(
        (p) => p.inlineData,
      );
      if (!part?.inlineData) return "No image generated";
      const outputPath = path.join(EXAMPLE_DIR, platform, slug, "image.png");
      fs.mkdirSync(path.dirname(outputPath), { recursive: true });
      fs.writeFileSync(outputPath, Buffer.from(part.inlineData.data, "base64"));
      return `Image saved to ${outputPath}`;
    } catch (e) {
      return `Error: ${e}`;
    }
  },
  {
    name: "generate_social_image",
    description: "Generate an image for a social media post.",
    schema: z.object({
      prompt: z
        .string()
        .describe("Detailed description of the image to generate."),
      platform: z.string().describe('Either "linkedin" or "tweets"'),
      slug: z
        .string()
        .describe("Post slug. Image saves to <platform>/<slug>/image.png"),
    }),
  },
);

创建智能体

使用 createDeepAgent 创建深度智能体时，传入记忆路径、技能目录、图片工具、内联子智能体定义，以及一个以示例目录为根的 FilesystemBackend，以便 ./AGENTS.md 和 ./skills/ 等路径能正确解析。

import { createDeepAgent, FilesystemBackend } from "deepagents";

function createContentWriter() {
  const researcherSubagent = {
    name: "researcher",
    description:
      "Research subagent with web search capability. Delegate research tasks here.",
    systemPrompt:
      "You are a research assistant. Use the web_search tool to find current, accurate information and return well-organized findings.",
    tools: [webSearch],
  };

  return createDeepAgent({
    model: "google-genai:gemini-3.1-pro-preview",
    memory: ["./AGENTS.md"],
    skills: ["./skills/"],
    tools: [generateCover, generateSocialImage],
    subagents: [researcherSubagent],
    backend: new FilesystemBackend({ rootDir: EXAMPLE_DIR }),
  });
}

添加入口点

const task =
  process.argv.slice(2).join(" ") ||
  "Write a blog post about how AI agents are transforming software development";

const agent = createContentWriter();
const result = await agent.invoke({
  messages: [{ role: "user", content: task }],
  config: { configurable: { threadId: "content-builder-demo" } },
});

const messages = result.messages ?? [];
for (const msg of messages) {
  if (msg.content) console.log(msg.content);
}

运行智能体

文件系统后端可以读取、写入和删除 root_dir 下的文件。请仅在专用目录中运行，并在发布前审查生成的内容。

从项目目录：

npx tsx content_writer.ts

将提示作为额外参数传递：

npx tsx content_writer.ts Write a blog post about prompt engineering

设置了 LANGSMITH_API_KEY 后，你可以在 LangSmith 中检查运行。

输出

成功后，智能体在项目根目录（示例目录）下写入产物，例如：

blogs/
└── prompt-engineering/
    ├── post.md
    └── hero.png
research/
└── prompt-engineering.md

路径遵循 SKILL.md 中的技能说明。

完整代码

在 GitHub 上浏览完整的 content-builder-agent 示例，包括基于 Rich 的流式输出 UI。

后续步骤

编辑 AGENTS.md 以更改品牌语气和研究要求
在 skills/<name>/SKILL.md 下添加技能以支持新的内容类型
在 subagents.yaml 中添加子智能体并在 load_subagents 中注册工具
阅读子智能体、技能和自定义了解更深入的配置

将这些文档连接到 Claude、VSCode 等，通过 MCP 获取实时答案。

在 GitHub 上编辑此页面或提交 issue。

Documentation Index

​概述

​核心概念

​前提条件

​设置

​添加配置文件

​构建脚本

​运行智能体

​输出

​完整代码

​后续步骤

概述

核心概念

前提条件

设置

添加配置文件

构建脚本

运行智能体

输出

完整代码

后续步骤