pip install "xpander-sdk" "langchain[openai]" langgraph python-dotenv

# Required - OpenAI API
OPENAI_API_KEY="your_openai_api_key_here"

# Required - xpander.ai Platform Configuration  
XPANDER_API_KEY="your_xpander_api_key"
XPANDER_ORGANIZATION_ID="your_organization_id"
XPANDER_AGENT_ID="your_agent_id"

import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
from langchain_core.messages import SystemMessage
from xpander_sdk import Agents

# Load environment variables
load_dotenv()

def validate_environment():
    """Validate that all required environment variables are set"""
    required_vars = [
        "OPENAI_API_KEY",
        "XPANDER_API_KEY", 
        "XPANDER_ORGANIZATION_ID",
        "XPANDER_AGENT_ID"
    ]
    
    for var in required_vars:
        if not os.getenv(var):
            raise KeyError(f"Missing required environment variable: {var}")
    
    print("✅ All required environment variables are set!")

def create_system_prompt(instructions):
    """Convert xpander agent instructions to system prompt"""
    parts = []
    
    if hasattr(instructions, 'general') and instructions.general:
        parts.append(f"System: {instructions.general}")
    
    if hasattr(instructions, 'goal_str') and instructions.goal_str:
        parts.append(f"Goals:\n{instructions.goal_str}")
    
    if hasattr(instructions, 'instructions') and instructions.instructions:
        instr_list = "\n".join([f"- {instr}" for instr in instructions.instructions])
        parts.append(f"Instructions:\n{instr_list}")
    
    return "\n\n".join(parts)

def main():
    # Validate environment
    validate_environment()
    
    # Initialize xpander agent
    print("🔧 Loading xpander agent configuration...")
    xpander_agent = Agents().get(agent_id=os.getenv("XPANDER_AGENT_ID"))
    
    print(f"📦 Loaded agent with {len(xpander_agent.tools.functions)} tools")
    print(f"🤖 Using model: {xpander_agent.model_name}")
    
    # Initialize the language model
    llm = ChatOpenAI(model=xpander_agent.model_name, temperature=0)
    
    # Create system prompt from xpander agent instructions
    system_prompt = create_system_prompt(xpander_agent.instructions)
    print(f"📋 System Prompt:\n{system_prompt}\n")
    
    # Create a ReAct agent with xpander tools
    agent = create_react_agent(
        llm, 
        xpander_agent.tools.functions
    )
    
    print(f"🚀 LangChain ReAct agent created successfully!")
    
    # Test the agent
    test_query = "search for information about xpander.ai and summarize what you find"
    print(f"\n📋 Test Query: {test_query}")
    print("🤔 Agent thinking...\n")
    
    # Stream the agent's response
    for chunk in agent.stream({
        "messages": [
            ("system", system_prompt),
            ("user", test_query)
        ]
    }):
        # Extract and display the agent's response
        if "agent" in chunk and chunk["agent"].get("messages"):
            messages = chunk["agent"]["messages"]
            for message in messages:
                if hasattr(message, 'content') and message.content:
                    print(f"🤖 Agent: {message.content}")

if __name__ == "__main__":
    main()

# The agent will automatically use instructions configured in xpander
# Examples of what you can configure:
# - Role: "You are a helpful research assistant"
# - Goals: "Help users find accurate information and provide summaries"
# - Instructions: ["Always verify information", "Provide sources", "Be concise"]

# Example query that uses multiple tools
complex_query = """
Search for the latest AI developments, 
then send an email summary to team@company.com
"""

response = agent.invoke({
    "messages": [
        ("system", system_prompt),
        ("user", complex_query)
    ]
})

python langchain_xpander_agent.py

✅ All required environment variables are set!
🔧 Loading xpander agent configuration...
📦 Loaded agent with 2 tools
🤖 Using model: gpt-4
📋 System Prompt:
System: You are a helpful AI assistant...

🚀 LangChain ReAct agent created successfully!
📋 Test Query: search for information about xpander.ai and summarize what you find
🤔 Agent thinking...

🤖 Agent: I'll search for information about xpander.ai for you...

# Use different OpenAI models
llm = ChatOpenAI(model="gpt-4o", temperature=0.7)

# Or use other providers supported by LangChain
from langchain_anthropic import ChatAnthropic
llm = ChatAnthropic(model="claude-3-sonnet-20240229")

def process_agent_response(response):
    """Custom processing of agent responses"""
    for message in response["messages"]:
        if message.type == "ai":
            print(f"🤖 AI: {message.content}")
        elif message.type == "tool":
            print(f"🔧 Tool Result: {message.content}")

KeyError: Missing required environment variable: XPANDER_API_KEY

Error: 2 validation errors for TavilySearchService...

401 Unauthorized