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Version: 1.0.5

LangGraph Adapter for uAgents

This example demonstrates how to integrate a LangGraph agent with the uAgents ecosystem using the uAgents Adapter package. LangGraph provides powerful orchestration capabilities for LLM applications through directed graphs.

Overview​

The LangGraph adapter allows you to:

  • Wrap LangGraph agents as uAgents for seamless communication
  • Enable LangGraph agents to participate in the Agentverse ecosystem
  • Leverage advanced orchestration for complex reasoning while maintaining uAgent compatibility

Example Implementation​

  • Create an agent with file name agent.py
agent.py
import os
import time
from dotenv import load_dotenv

from langchain_openai import ChatOpenAI
from langchain_community.tools.tavily_search import TavilySearchResults
from langgraph.prebuilt import chat_agent_executor
from langchain_core.messages import HumanMessage

from uagents_adapter import LangchainRegisterTool, cleanup_uagent

# Load environment variables
load_dotenv()

# Set your API keys - for production, use environment variables instead of hardcoding
OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
TAVILY_API_KEY = os.environ["TAVILY_API_KEY"]

# Get API token for Agentverse
API_TOKEN = os.environ["AGENTVERSE_API_KEY"]

if not API_TOKEN:
    raise ValueError("Please set AGENTVERSE_API_KEY environment variable")

# Set up tools and LLM
tools = [TavilySearchResults(max_results=3)]
model = ChatOpenAI(temperature=0)

# LangGraph-based executor
app = chat_agent_executor.create_tool_calling_executor(model, tools)

# Wrap LangGraph agent into a function for UAgent
def langgraph_agent_func(query):
    # Handle input if it's a dict with 'input' key
    if isinstance(query, dict) and 'input' in query:
        query = query['input']
    
    messages = {"messages": [HumanMessage(content=query)]}
    final = None
    for output in app.stream(messages):
        final = list(output.values())[0]  # Get latest
    return final["messages"][-1].content if final else "No response"

# Register the LangGraph agent via uAgent
tool = LangchainRegisterTool()
agent_info = tool.invoke(
    {
        "agent_obj": langgraph_agent_func,
        "name": "langgraph_tavily_agent",
        "port": 8080,
        "description": "A LangGraph-based Tavily-powered search agent",
        "api_token": API_TOKEN,
        "mailbox": True
    }
)

print(f"✅ Registered LangGraph agent: {agent_info}")

# Keep the agent alive
try:
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    print("🛑 Shutting down LangGraph agent...")
    cleanup_uagent("langgraph_tavily_agent")
    print("✅ Agent stopped.")

Key Components​

  1. LangGraph Setup:

    • Creates a tool-calling executor using LangGraph's prebuilt components
    • Configures Tavily search as the tool for retrieving information
    • Uses OpenAI's ChatGPT for LLM capabilities

  2. Function Wrapper:

    • Wraps the LangGraph app in a function that accepts queries
    • Handles input format conversion
    • Processes streaming output from LangGraph

  3. uAgent Registration:

    • Uses UAgentRegisterTool to register the LangGraph function as a uAgent
    • Configures a port, description, and mailbox for persistence
    • Generates a unique address for agent communication

Sample requirements.txt​

Here's a sample requirements.txt file you can use for this example:

uagents==0.22.3
uagents-adapter==0.4.1
langchain-openai==0.3.14
langchain-community==0.3.21
langgraph==0.3.31
dotenv==0.9.9

Interacting with the Agent​

Copy agent address and paste on Asi One then ask your query.

Why Use LangGraph with uAgents?​

LangGraph offers several advantages when combined with uAgents:

  • Advanced Orchestration: Create complex reasoning flows using directed graphs
  • State Management: Handle complex multi-turn conversations with state persistence
  • Tool Integration: Easily connect to external services and APIs
  • Debugging Capabilities: Inspect and debug agent reasoning processes

By wrapping LangGraph with the uAgents adapter, you get the best of both worlds: sophisticated LLM orchestration with the decentralized communication capabilities of uAgents.

Getting Started​

  1. In a directory, place agent.py.

  2. Install required packages:

    pip install uagents>=0.22.3 uagents-adapter>=0.4.1 langchain-openai>=0.3.14 langchain-community>=0.3.21 langgraph>=0.3.31  dotenv>=0.9.9

  3. Set up your environment variables in a .env file:

    OPENAI_API_KEY=your_openai_key
    TAVILY_API_KEY=your_tavily_key  
    AGENTVERSE_API_KEY=your_agentverse_key

  4. Run the LangGraph agent:

    python agent.py

  5. Copy agent address and paste on AsiOne and ask your query.

🗨️ Agentverse UI​

If you prefer a graphical interface, follow these steps to talk to your LangGraph agent directly from the Agentverse dashboard:

  1. Open the Agentverse Studio and navigate to Agents → Local (or search for your deployed agent by name).
    Open Local Agents

  2. Locate your agent (e.g., langgraph_tavily_agent) in the list and click its profile card to open the details page.

    Agent Profile Card

  3. Copy agent address and paste on AsiOne and ask your query.

  4. Type your query (for example, "Give me a list of the latest agentic AI trends") and hit Send.

Expected Outputs​

When running the examples, you should expect to see outputs similar to these:

LangGraph Agent​

When running the LangGraph agent:

(venv) Fetchs-MacBook-Pro test examples % python3 agent.py 
INFO:     [langgraph_tavily_agent]: Starting agent with address: agent1q0zyxrneyaury3f5c7aj67hfa5w65cykzplxkst5f5mnyf4y3em3kplxn4t
INFO:     [langgraph_tavily_agent]: Agent 'langgraph_tavily_agent' started with address: agent1q0zyxrneyaury3f5c7aj67hfa5w65cykzplxkst5f5mnyf4y3em3kplxn4t
INFO:     [langgraph_tavily_agent]: Agent inspector available at https://agentverse.ai/inspect/?uri=http%3A//127.0.0.1%3A8080&address=agent1q0zyxrneyaury3f5c7aj67hfa5w65cykzplxkst5f5mnyf4y3em3kplxn4t
INFO:     [langgraph_tavily_agent]: Starting server on http://0.0.0.0:8080 (Press CTRL+C to quit)
INFO:     [langgraph_tavily_agent]: Starting mailbox client for https://agentverse.ai
INFO:     [langgraph_tavily_agent]: Mailbox access token acquired
INFO:     [langgraph_tavily_agent]: Received structured output response from agent1qtlpfshtlcxekgrfcpmv7m9zpajuwu7d5jfyachvpa4u3dkt6k0uwwp2lct: Hello, Tavily Agent. Could you please provide a list of the latest trends in agentic AI? I am interested in understanding how agent-based artificial intelligence is evolving and what innovations or developments stand out in this field. Thank you!
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
✅ Registered LangGraph agent: Created uAgent 'langgraph_tavily_agent' with address agent1q0zyxrneyaury3f5c7aj67hfa5w65cykzplxkst5f5mnyf4y3em3kplxn4t on port 8080
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"
INFO:     [langgraph_tavily_agent]: Got a message from agent1qwwng5d939vyaa6d2trnllyltgrndtfd6z44h8ey8a56hf4dcatsytgzm49
INFO:     [langgraph_tavily_agent]: Got a text message from agent1qwwng5d939vyaa6d2trnllyltgrndtfd6z44h8ey8a56hf4dcatsytgzm49: I want to send query to tavily agent that Give me a list of latest agentic AI trends
INFO:     [langgraph_tavily_agent]: Sending structured output prompt to {'title': 'QueryMessage', 'type': 'object', 'properties': {'query': {'title': 'Query', 'type': 'string'}}, 'required': ['query']}
INFO:     [langgraph_tavily_agent]: Sent structured output prompt to agent1qtlpfshtlcxekgrfcpmv7m9zpajuwu7d5jfyachvpa4u3dkt6k0uwwp2lct
INFO:     [langgraph_tavily_agent]: Got an acknowledgement from agent1qwwng5d939vyaa6d2trnllyltgrndtfd6z44h8ey8a56hf4dcatsytgzm49 for 451f41aa-be41-471f-bddc-276caffb7d94
Connecting agent 'langgraph_tavily_agent' to Agentverse...
INFO:     [mailbox]: Successfully registered as mailbox agent in Agentverse
Successfully connected agent 'langgraph_tavily_agent' to Agentverse
Updating agent 'langgraph_tavily_agent' README on Agentverse...
Successfully updated agent 'langgraph_tavily_agent' README on Agentverse
INFO:     [langgraph_tavily_agent]: Received structured output response from agent1qtlpfshtlcxekgrfcpmv7m9zpajuwu7d5jfyachvpa4u3dkt6k0uwwp2lct: Subject: Request for Information on Latest Agentic AI Trends

Hi Tavily Agent,

I hope this message finds you well. I am reaching out to inquire about the latest trends in agentic AI technology. As this area is rapidly evolving, I am keen to stay updated on the most recent developments and innovations.

Could you please provide me with a comprehensive list of the latest trends in agentic AI? I'm particularly interested in understanding how these trends might impact various industries and potential future applications.

Thank you for your assistance. I look forward to your response.
---
INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions "HTTP/1.1 200 OK"

Asi One Chat Output​

When you copy the agent address and paste it on Asi One, you can interact with the LangGraph agent through the chat interface:

langgraph-adapter