LangChain + LangGraph Integration with xpander.ai
This guide demonstrates how to integrate LangChain and LangGraph with xpander.ai to create powerful ReAct agents with access to over 2000 pre-built tools and services.
📓 Prefer Jupyter Notebook? You can also follow along with our interactive notebook: LangChain + xpander.ai Example Notebook - it contains all the code and explanations in a single executable file. What You’ll Build
By the end of this tutorial, you’ll have a LangChain ReAct agent that can:
- 🔍 Search the web using Tavily Search with advanced filtering
- 📧 Send emails with rich formatting and attachments
- 🧠 Use intelligent reasoning through LangGraph’s ReAct pattern
- 🎯 Follow custom instructions from your xpander agent configuration
- ⚡ Optimize token usage with xpander’s output schema filtering
Prerequisites
Before starting, make sure you have:
- xpander.ai account: Sign up here
- Python 3.8 or higher
- OpenAI API key
- Basic knowledge of Python and LangChain
Step 1: Installation
First, install the required dependencies:
pip install "xpander-sdk" "langchain[openai]" langgraph python-dotenv
Step 2: Environment Setup
Create a .env file in your project directory:
# 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"
Where to find your xpander credentials:
- API Key: Go to xpander Settings → API Keys
- Organization ID: Found in your organization settings or browser URL
- Agent ID: Create or select an agent in the xpander platform
Make sure to add .env to your .gitignore file to avoid committing sensitive credentials.
Step 3: Set Up Your xpander Agent
In the xpander.ai platform:
- Create a new agent or use an existing one
- Add tools to your agent:
- Tavily Search - for web search capabilities
- Send Email - for email functionality
- Any other tools you need from the 2000+ available
- Configure tool output filtering to optimize performance
- Set up agent instructions and goals
Step 4: Create the Integration
Create a new Python file langchain_xpander_agent.py:
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()
Step 5: Advanced Features
Custom Agent Instructions
You can enhance your agent by adding custom instructions in the xpander platform:
# 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)
]
})
Output Schema Filtering
xpander’s advanced output schema filtering helps you:
- Reduce token usage by removing unnecessary data
- Remove PII and sensitive information
- Focus AI attention on relevant response parts
- Prevent data exposure of irrelevant fields
Configure this in your xpander agent’s tool settings.
Step 6: Running Your Agent
Execute your agent:
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...
| Tool | Description | Key Features |
|---|
| 🔍 Tavily Search | Advanced web search | Configurable search depth, Result filtering, Answer generation, Topic-specific search |
| 📧 Send Email | Email capabilities | Rich text support, Attachment handling, Template support |
Customization Options
- Go to your xpander agent configuration
- Browse the tool library (2000+ available tools)
- Add tools to your agent
- They’ll automatically be available in your LangChain agent
Using Different Models
# 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")
Custom Message Handling
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}")
Troubleshooting
Common Issues
Environment Variable Errors
KeyError: Missing required environment variable: XPANDER_API_KEY
.env file
Tool Configuration Errors
Error: 2 validation errors for TavilySearchService...
- Use Output Filtering: Configure tool output schemas to reduce token usage
- Set Tool Dependencies: Ensure proper execution order in xpander platform
- Optimize System Prompts: Keep instructions clear and concise
- Monitor Usage: Track API usage in both OpenAI and xpander dashboards
What’s Next?
Now that you have a working LangChain + LangGraph integration:
- 🔧 Add More Tools: Explore xpander’s 2000+ tool library
- ⚙️ Configure Advanced Features: Set up output filtering and dependencies
- 🏗️ Build Complex Workflows: Create multi-step agent processes
- 📊 Monitor Performance: Use xpander’s analytics dashboard
- 🚀 Deploy to Production: Use xpander’s cloud infrastructure
Additional Resources
- 📚 Documentation: docs.xpander.ai
- 💬 Discord Community: Join our Discord
- 🐛 GitHub Issues: Report bugs and request features
- ✉️ Support: Contact support through the xpander platform
Ready to build production-grade AI agents with LangChain and xpander? Start building today 🚀
