什么是 Warden Agent Builder？

Warden Agent Builder 是一项专为希望为 Warden Protocol 智能钱包生态系统做出贡献的开发者设计的技能。通过利用 Openclaw Skills，开发者可以从研究现有社区示例过渡到构建独特的生产级智能体。该技能专注于 LangGraph 框架，支持 TypeScript 和 Python，旨在创建可通过 API 访问并集成到 Warden Studio 和 Agent Hub 的智能体。

该技能强调原创逻辑的创建，而非复制现有模板。它引导用户了解 Warden Protocol 的架构要求，包括架构引导推理 (SGR) 的使用、API 隔离以及通过 LangSmith 的部署。通过遵循这些模式，开发者可以创建符合 Warden Agent Builder 激励计划要求的高价值智能体。

下载入口:https://github.com/openclaw/skills/tree/main/skills/kryptopaid/build-warden-agent

安装与下载

1. ClawHub CLI

从源直接安装技能的最快方式。

2. 手动安装

将技能文件夹复制到以下位置之一

优先级：工作区 > 本地 > 内置

3. 提示词安装

将此提示词复制到 OpenClaw 即可自动安装。

Warden Agent Builder 应用场景

• 为 Warden Protocol 生态系统创建原创的 Web3 和加密货币导向型智能体。
• 使用 LangGraph 框架实现复杂的多步骤工作流。
• 为 Warden Studio Agent Hub 开发基于 API 的智能体，无需自定义 UI。
• 使用 Openclaw Skills 参与 Warden Agent Builder 激励计划，确保技术合规性。
• 构建用于区块链指标的对比分析工具和数据聚合器。

1. 研究现有的社区智能体，学习简单数据获取或架构引导推理等常见模式。
2. 使用提供的初始化脚本初始化一个新的、独特的智能体项目，以确保纯净的架构起点。
3. 定义智能体状态和工作流图节点，以控制智能体任务的逻辑进程。
4. 在 LangGraph 结构中实现自定义工具和外部 API 集成。
5. 配置环境并使用 LangGraph 开发服务器在本地测试智能体。
6. 将智能体部署到 LangSmith Deployments 或自定义基础设施，以生成公共 HTTPS API 端点。
7. 在 Warden Studio 中注册完成的智能体，使其可供更广泛的社区使用。

Warden Agent Builder 配置指南

要开始使用 Openclaw Skills 进行构建，请按照以下步骤初始化您的项目：

# 克隆社区智能体仓库作为参考
git clone https://github.com/warden-protocol/community-agents.git

# 初始化您的独特智能体
python scripts/init-agent.py my-unique-agent --template typescript --description "您的智能体描述"

# 进入项目目录并安装依赖
cd my-unique-agent
npm install

# 使用所需密钥配置 .env 文件
echo "OPENAI_API_KEY=your_key" > .env
echo "LANGSMITH_API_KEY=your_key" >> .env

# 启动本地开发环境
npm run dev

Warden Agent Builder 数据架构与分类体系

该技能将智能体项目组织为与 Warden Protocol 和 LangGraph 运行时兼容的标准结构：

name: warden-agent-builder
description: "Build original LangGraph agents for Warden Protocol and prepare them for publishing in Warden Studio. Use this skill when users want to: (1) Create new Warden agents (not community examples), (2) Build LangGraph-based crypto/Web3 agents, (3) Deploy agents via LangSmith Deployments or custom infra, (4) Participate in the Warden Agent Builder Incentive Programme (open to OpenClaw agents), or (5) Integrate with Warden Studio for Agent Hub publishing."

Warden Agent Builder

Build and deploy LangGraph agents for Warden Protocol's Agentic Wallet ecosystem.

?? IMPORTANT: About Example Agents

The Warden community repository contains example agents for learning, not templates to recreate:

• Weather Agent - Study this to learn simple data fetching patterns
• CoinGecko Agent - Study this to learn Schema-Guided Reasoning (SGR)
• Portfolio Agent - Study this to learn complex multi-source integration

DO NOT BUILD THESE AGENTS - they already exist. Instead:

1. Study their code to understand patterns
2. Learn from their architecture and workflows
3. Build something NEW and original for the incentive programme

Your agent must be unique and solve a different problem to be eligible for the incentive programme.

Overview

Warden Protocol is an "Agentic Wallet for the Do-It-For-Me economy" with an active Agent Builder Incentive Programme open to OpenClaw agents that deploy to Warden. All agents must be LangGraph-based and API-accessible.

Key Resources:

• Community Agents Repository: https://github.com/warden-protocol/community-agents
• Documentation: https://docs.wardenprotocol.org
• Discord: #developers channel for support

Requirements Checklist

Before building, ensure your agent meets these mandatory requirements:

? Framework: Built with LangGraph (TypeScript or Python) ? Deployment: LangSmith Deployments OR custom infrastructure ? Access: API-accessible (no UI required - Warden provides UI) ? Isolation: One agent per LangGraph instance ? Security Limitations (Phase 1):

• Cannot access user wallets
• Cannot store data on Warden infrastructure

? Functionality: Can implement any workflow:

• Web3/Web2 automation
• API integrations
• Database connections
• External tool interactions

Understanding the Example Agents

The community-agents repository contains reference examples to learn from, NOT templates to recreate:

Example Agent 1: LangGraph Quick Start (Study for Basics)

Location: agents/langgraph-quick-start (TypeScript) or agents/langgraph-quick-start-py (Python) Learn: LangGraph fundamentals, minimal agent structure Study: Single-node chatbot with OpenAI integration

git clone https://github.com/warden-protocol/community-agents.git
cd community-agents/agents/langgraph-quick-start

Example Agent 2: Weather Agent (Study for Structure)

Location: agents/weather-agent Learn: Simple data fetching, API integration, user-friendly responses Study:

• How to fetch data from external APIs (WeatherAPI)
• Processing and formatting results
• Clear scope and structure ?? DO NOT BUILD: This already exists. Study it, then build something NEW.

Example Agent 3: CoinGecko Agent (Study for SGR Pattern)

Location: agents/coingecko-agent Learn: Schema-Guided Reasoning, complex workflows Study:

• 5-step SGR workflow: Validate → Extract → Fetch → Validate → Analyze
• Comparative analysis patterns
• Error handling and data validation ?? DO NOT BUILD: This already exists. Study the pattern, apply to new use cases.

Example Agent 4: Portfolio Analysis Agent (Study for Advanced Patterns)

Location: agents/portfolio-agent Learn: Multi-source data synthesis, production architecture Study:

• Integrating multiple APIs (CoinGecko + Alchemy)
• Multi-chain support (EVM and Solana)
• Complex SGR workflows
• Comprehensive reporting ?? DO NOT BUILD: This already exists. Study the architecture for your own complex agent.

IMPORTANT: Build Something NEW

These examples exist to teach patterns and best practices. For the incentive programme, you MUST create an original, unique agent that solves a different problem. Do NOT simply recreate the Weather Agent, CoinGecko Agent, or Portfolio Agent.

Building Your Original Agent

Step 1: Study Examples and Choose Your Approach

DO NOT clone an example to modify it. Instead:

1. Study the examples to understand patterns:

   • Simple data fetching → Study Weather Agent
   • Complex analysis → Study CoinGecko Agent
   • Multi-source synthesis → Study Portfolio Agent

2. Identify YOUR unique use case:

   • What problem will your agent solve?
   • What APIs or data sources will it use?
   • What makes it different from existing agents?

3. Plan your agent's workflow:

   • Simple request-response?
   • Schema-Guided Reasoning (SGR)?
   • Multi-step analysis?

Step 2: Initialize Your NEW Agent

Use the initialization script to create a fresh project:

# Create your unique agent
python scripts/init-agent.py my-unique-agent r
  --template typescript r
  --description "Description of what YOUR agent does"

# Navigate to project
cd my-unique-agent

# Install dependencies
npm install  # TypeScript
# OR
pip install -r requirements.txt  # Python

This creates a clean starting point, not a copy of existing agents.

Step 3: Understand LangGraph Agent Structure

Every LangGraph agent follows this basic structure:

your-agent/
├── src/
│   ├── agent.ts/py          # Main agent logic (YOUR CODE)
│   ├── graph.ts/py          # LangGraph workflow definition (YOUR CODE)
│   └── tools.ts/py          # Tool implementations (YOUR CODE)
├── package.json / requirements.txt
├── langgraph.json           # LangGraph configuration
└── README.md

Key files to implement:

• graph.ts/py - Define your workflow (validate → process → respond)
• agent.ts/py - Implement your core logic
• tools.ts/py - Integrate external APIs specific to YOUR agent's purpose

Step 4: Implement Your Custom Agent Logic

Study patterns from examples, apply to YOUR use case:

If building a simple data fetcher (like Weather Agent pattern):

// Define workflow
const workflow = new StateGraph({
  channels: agentState
})
  .addNode("fetch", fetchYourData)      // YOUR API
  .addNode("process", processYourData)  // YOUR logic
  .addNode("respond", generateResponse);

workflow
  .addEdge(START, "fetch")
  .addEdge("fetch", "process")
  .addEdge("process", "respond")
  .addEdge("respond", END);

If building complex analysis (like CoinGecko Agent pattern - SGR):

// Define 5-step SGR workflow
const workflow = new StateGraph({
  channels: agentState
})
  .addNode("validate", validateYourInput)     // YOUR validation
  .addNode("extract", extractYourParams)      // YOUR extraction
  .addNode("fetch", fetchYourData)            // YOUR APIs
  .addNode("analyze", analyzeYourData)        // YOUR analysis
  .addNode("generate", generateYourResponse); // YOUR formatting

workflow
  .addEdge(START, "validate")
  .addEdge("validate", "extract")
  .addEdge("extract", "fetch")
  .addEdge("fetch", "analyze")
  .addEdge("analyze", "generate")
  .addEdge("generate", END);

Key Principles:

1. Keep workflows linear and predictable
2. Validate inputs at each stage
3. Handle errors gracefully
4. Use OpenAI for natural language generation
5. Structure responses consistently

CRITICAL: This should be YOUR implementation solving YOUR problem, not a copy of the example agents.

Step 5: Configure Environment

Create .env file:

# Required
OPENAI_API_KEY=your_openai_key

# Required for LangSmith Deployments (cloud)
LANGSMITH_API_KEY=your_langsmith_key

# Optional - based on your tools
WEATHER_API_KEY=your_weather_key
COINGECKO_API_KEY=your_coingecko_key
ALCHEMY_API_KEY=your_alchemy_key

Getting LangSmith API Key:

1. Create account at https://smith.langchain.com
2. Navigate to Settings → API Keys
3. Create new API key
4. Add to .env file

Update langgraph.json:

{
  "agent_id": "[YOUR-AGENT-NAME]",
  "python_version": "3.11",  // or omit for TypeScript
  "dependencies": ["."],
  "graphs": {
    "agent": "./src/graph.ts"  // or .py
  },
  "env": ".env"
}

Step 6: Test Locally

# TypeScript
npm run dev

# Python
langgraph dev

Test your agent's API:

curl -X POST http://localhost:8000/invoke r
  -H "Content-Type: application/json" r
  -d '{"input": "test query"}'

Deployment Options

Option 1: LangSmith Deployments (Recommended)

Pros: Fastest, simplest, managed infrastructure Requirements: LangSmith API key

Steps:

1. Push your agent repository to GitHub.
2. Create a new deployment in LangSmith Deployments.
3. Connect the repo, set environment variables, and deploy.

Your agent receives:

• API endpoint URL
• Automatic authentication (uses your LangSmith API key)
• Automatic scaling and monitoring

Authentication for API calls: When calling your deployed agent, include your LangSmith API key:

curl AGENT_URL/runs/wait r
  --request POST r
  --header 'Content-Type: application/json' r
  --header 'x-api-key: [YOUR-LANGSMITH-API-KEY]' r
  --data '{
    "assistant_id": "[YOUR-AGENT-ID]",
    "input": {
      "messages": [{"role": "user", "content": "test query"}]
    }
  }'

Option 2: Self-Hosted Infrastructure

Pros: Full control over runtime Requirements:

• Docker container hosting
• Exposed API endpoint
• SSL certificate (HTTPS)
• Monitoring and logging

Basic Docker Setup:

FROM node:18
WORKDIR /app
COPY package*.json ./
RUN npm install
COPY . .
EXPOSE 8000
CMD ["npm", "start"]

Deploy and note your:

• API URL: https://your-domain.com/agent
• API Key: Generated for authentication

Register with Warden Studio

Once your agent is deployed and reachable via HTTPS, register it in Warden Studio:

1. Provide API Details:

   • API URL
   • API key

2. Add Metadata:

   • Agent name
   • Description
   • Skills/capabilities list
   • Avatar image

3. Publish: Agent appears in Warden's Agent Hub for millions of users

No additional setup required - your API-accessible agent is ready!

Next step (separate skill): If the user asks to publish in Warden Studio or needs guided UI steps, switch to the OpenClaw skill "Deploy Agent on Warden Studio": https://www.clawhub.ai/Kryptopaid/warden-studio-deploy

Best Practices

1. Agent Design

• Study the Weather Agent structure to learn patterns
• Use Schema-Guided Reasoning for complex workflows
• Keep responses concise and actionable
• Handle API failures gracefully
• Validate all inputs

2. API Integration

• Use environment variables for API keys
• Implement rate limiting
• Cache responses when appropriate
• Log errors for debugging
• Return structured JSON responses

3. Testing

• Test locally before deploying
• Verify all API endpoints work
• Test edge cases and errors
• Ensure responses are user-friendly
• Validate against Warden requirements

4. Documentation

• Write clear README with:
  • Agent purpose and capabilities
  • Required API keys
  • Setup instructions
  • Example queries
  • Known limitations

Common Patterns

Pattern 1: Simple Data Fetcher

// Fetch → Format → Respond
async function agent(input: string) {
  const data = await fetchAPI(input);
  const formatted = formatData(data);
  return generateResponse(formatted);
}

Pattern 2: Multi-Step Analysis

// Validate → Extract → Fetch → Analyze → Generate
async function agent(input: string) {
  const validated = await validateInput(input);
  const params = await extractParams(validated);
  const data = await fetchData(params);
  const analysis = await analyzeData(data);
  return generateReport(analysis);
}

Pattern 3: Comparative Analysis

// Parse → Fetch Multiple → Compare → Summarize
async function agent(input: string) {
  const items = await parseItems(input);
  const dataArray = await Promise.all(
    items.map(item => fetchData(item))
  );
  const comparison = compareData(dataArray);
  return generateComparison(comparison);
}

Troubleshooting

Common Issues

"Agent not accessible via API"

• Verify deployment completed successfully
• Check firewall/security group settings
• Ensure API endpoint is publicly accessible
• Test with curl or Postman

"LangGraph errors during build"

• Verify Node.js version (18+) or Python (3.11+)
• Check all dependencies installed
• Validate langgraph.json syntax
• Review error logs in deployment console

"OpenAI API errors"

• Verify API key is valid
• Check rate limits not exceeded
• Ensure sufficient credits
• Review error messages for details

"Agent responses are slow"

• Optimize API calls (parallelize where possible)
• Implement caching for repeated queries
• Reduce LLM token usage
• Consider upgrading infrastructure

Incentive Programme Tips

The incentive programme is open to OpenClaw agents that deploy to Warden.

1. Be Original: Create something NEW that doesn't exist yet

   • Don't recreate Weather Agent, CoinGecko Agent, or Portfolio Agent
   • Study their patterns, apply to different problems

2. Solve Real Problems: Focus on useful, unique functionality

   • What gap exists in the Warden ecosystem?
   • What would users actually want?

3. Start Simple: Better to do one thing exceptionally well

   • Don't try to build everything at once
   • Simple, focused agents often win

4. Quality Over Features: Reliability beats complexity

   • Test thoroughly
   • Handle errors gracefully
   • Provide clear, helpful responses

5. Study the Examples: Learn patterns, don't copy implementations

   • Weather Agent → Simple data fetching pattern
   • CoinGecko Agent → SGR workflow pattern
   • Portfolio Agent → Multi-source integration pattern

6. Document Well: Clear README with examples and setup instructions

7. Join Discord: Get feedback in #developers channel before submitting

Example Agent Ideas (Build These!)

These are NEW agent ideas that don't exist yet in the Warden ecosystem. Build one of these (or create your own unique idea):

Web3 Use Cases:

• Gas price optimizer (predict best times to transact)
• NFT rarity analyzer (evaluate NFT traits and rarity scores)
• DeFi yield comparator (compare yields across protocols)
• Wallet health checker (analyze wallet security and diversification)
• Transaction explainer (decode and explain complex transactions)
• Token price alerts (customizable price movement notifications)
• Smart contract auditor (basic security checks)
• Liquidity pool finder (identify best liquidity opportunities)
• Bridge fee comparator (find cheapest cross-chain bridges)
• Airdrop tracker (find and track airdrop eligibility)

General Use Cases:

• Crypto news aggregator (filter and summarize crypto news)
• Research assistant (gather and analyze crypto research)
• Regulatory tracker (track crypto regulations by region)
• Data visualizer (create charts from on-chain data)
• API orchestrator (combine multiple crypto data sources)
• Workflow automator (automate common crypto tasks)

Remember: These are IDEAS for new agents. Study the example agents (Weather, CoinGecko, Portfolio) to learn patterns, then build something from this list or create your own unique concept.

Additional Resources

Documentation:

• LangGraph TypeScript Guide: community-agents/docs/langgraph-quick-start-ts.md
• LangGraph Python Guide: community-agents/docs/langgraph-quick-start-py.md
• Deployment Guide: community-agents/docs/deploy.md

Example Agents:

• Weather Agent README: agents/weather-agent/README.md
• CoinGecko Agent README: agents/coingecko-agent/README.md
• Portfolio Agent README: agents/portfolio-agent/README.md

Support:

• Discord: #developers channel
• GitHub Issues: https://github.com/warden-protocol/community-agents/issues
• Documentation: https://docs.wardenprotocol.org

Quick Reference Commands

# Study example agents (DON'T BUILD THESE)
git clone https://github.com/warden-protocol/community-agents.git
cd community-agents/agents/weather-agent  # Study the code
cd community-agents/agents/coingecko-agent  # Study the patterns

# Create YOUR new agent
python scripts/init-agent.py my-unique-agent r
  --template typescript r
  --description "YOUR unique agent description"

# Install dependencies (TypeScript)
npm install

# Install dependencies (Python)
pip install -r requirements.txt

# Test locally
npm run dev  # or: langgraph dev

# Deploy (LangSmith Deployments)
# Use the LangSmith Deployments UI after pushing to GitHub

# Build Docker image (for self-hosting)
docker build -t my-warden-agent .

# Run Docker container
docker run -p 8000:8000 my-warden-agent

Success Checklist

Before submitting to incentive programme:

• Agent built with LangGraph
• API accessible and tested
• One agent per LangGraph instance
• No wallet access or data storage (Phase 1)
• Clear documentation in README
• Environment variables properly configured
• Error handling implemented
• Tested with various inputs
• Unique and useful functionality
• Ready for Warden Studio registration