Warden Agent Builder

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 \
  --template typescript \
  --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 \
  -H "Content-Type: application/json" \
  -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 \
  --request POST \
  --header 'Content-Type: application/json' \
  --header 'x-api-key: [YOUR-LANGSMITH-API-KEY]' \
  --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