🦜️🔗 LangChain UAgents

This package contains the LangChain integration with uAgents, allowing you to convert Langchain agents into uAgents and register them on Agentverse.

langchain-uagents

Agentverse website

Features

• Convert Langchain agents to uAgents
• Automatic port allocation with fallback options
• Register agents on Agentverse
• Support for AI agent message forwarding
• ASI1-agentic accessible agent
• Clean shutdown and resource management

Installation

bash pip install -U langchain-uagents

Credentials

We also need to set our Agentverse API key. You can get an API key by visiting this site, or follow the steps in the Agentverse documentation.

```python import os from dotenv import load_dotenv

Load environment variables

load_dotenv()

Get API token for Agentverse

APITOKEN = os.getenv("AVAPIKEY", "yourdefaultkeyhere") OPENAIAPIKEY = os.getenv("OPENAIAPIKEY", "yourdefaultopenaikeyhere") ```

UAgentRegisterTool

Here we show how to use the UAgentRegisterTool to convert a Langchain agent into a uAgent and register it on Agentverse.

Instantiation

```python from langchain_uagents import UAgentRegisterTool

tool = UAgentRegisterTool() ```

Basic Usage

The UAgentRegisterTool accepts the following parameters during invocation:

• agent_obj (required): The Langchain agent object to convert
• name (required): Name for the uAgent
• port (optional, int): Port to run the agent on, defaults to 8000
• description (optional, str): Description of the agent's functionality
• api_token (required): Agentverse API token for registration
• start_range (optional, int): Start of port range for automatic allocation
• end_range (optional, int): End of port range for automatic allocation

```python agentinfo = tool.invoke({ "agentobj": agent, "name": "myagent", "port": 8080, "description": "A useful agent for my tasks", "apitoken": API_TOKEN })

Print agent info

print(f"Created uAgent '{agentinfo['name']}' with address {agentinfo['address']} on port {agent_info['port']}") ```

Complete Example

Here's a complete example showing how to create a calculator agent with Langchain and register it as a uAgent:

```python import os import time from dotenv import loaddotenv from langchainopenai import ChatOpenAI from langchain.agents import initializeagent, AgentType, Tool from langchainuagents import UAgentRegisterTool, cleanup_uagent

Load environment variables

load_dotenv()

Get API token for Agentverse

APITOKEN = os.getenv("AVAPIKEY") OPENAIAPIKEY = os.getenv("OPENAIAPI_KEY")

Define a simple calculator tool

def calculator_tool(expression: str) -> str: """Evaluates a basic math expression (e.g., '2 + 2 * 3').""" try: result = eval(expression) return str(result) except Exception as e: return f"Error: {str(e)}"

Create the langchain agent

tools = [ Tool( name="Calculator", func=calculator_tool, description="Useful for evaluating math expressions" ) ]

llm = ChatOpenAI(temperature=0, apikey=OPENAIAPIKEY) agent = initializeagent( tools=tools, llm=llm, agent=AgentType.ZEROSHOTREACT_DESCRIPTION, verbose=True )

Create and register the uAgent

tool = UAgentRegisterTool() agentinfo = tool.invoke({ "agentobj": agent, "name": "calculatoragent", "port": 8080, "description": "A calculator agent for testing", "apitoken": API_TOKEN })

Print agent info

print(f"Created uAgent '{agentinfo['name']}' with address {agentinfo['address']} on port {agent_info['port']}")

Keep the agent running

try: while True: time.sleep(1) except KeyboardInterrupt: print("\nShutting down calculator agent...") cleanupuagent("calculatoragent") print("Calculator agent stopped.") ```

Port Allocation

The tool automatically handles port allocation:

1. First tries to use the specified port
2. If the port is in use, searches for an available port in the range 8000-9000
3. Raises a RuntimeError if no ports are available

You can customize the port range:

python agent_info = tool.invoke({ "agent_obj": agent, "name": "my_agent", "port": 8080, # Preferred port "start_range": 8000, # Start of port range "end_range": 9000, # End of port range "description": "A useful agent", "api_token": API_TOKEN })

Cleanup

Always clean up your uAgent when done:

```python from langchainuagents import cleanupuagent

Clean up by agent name

cleanupuagent("myagent") ```

Environment Variables

The tool requires the following environment variables:

• AV_API_KEY: Your Agentverse API key for registering agents
• OPENAI_API_KEY: Your OpenAI API key for the Langchain agent

You can set these in a .env file or export them in your environment:

bash export AV_API_KEY="your_agentverse_api_key" export OPENAI_API_KEY="your_openai_api_key"