uagents-adapter 0.1.0

Adapters for uAgents to integrate with LangChain and CrewAI

uAgents Adapter

This package provides adapters for integrating uAgents with popular AI libraries:

• LangChain Adapter: Convert LangChain agents to uAgents
• CrewAI Adapter: Convert CrewAI crews to uAgents

Installation

# Install the base package
pip install uagents-adapter

# Install with LangChain support
pip install "uagents-adapter[langchain]"

# Install with CrewAI support
pip install "uagents-adapter[crewai]"

# Install with all extras
pip install "uagents-adapter[langchain,crewai]"

LangChain Adapter

The LangChain adapter allows you to convert any LangChain agent into a uAgent that can interact with other agents in the Agentverse ecosystem.

from langchain_core.agents import AgentExecutor, create_react_agent
from langchain_core.tools import BaseTool
from langchain_openai import ChatOpenAI

from uagents_adapter.langchain import UAgentRegisterTool

# Create your LangChain agent
llm = ChatOpenAI(model_name="gpt-4")
tools = [...]  # Your tools here
agent = create_react_agent(llm, tools)
agent_executor = AgentExecutor(agent=agent, tools=tools)

# Create uAgent register tool
register_tool = UAgentRegisterTool()

# Register the agent as a uAgent
result = register_tool.invoke({
    "agent_obj": agent_executor,
    "name": "my_langchain_agent",
    "port": 8000,
    "description": "My LangChain agent as a uAgent",
    "mailbox": True,  # Use Agentverse mailbox service
    "api_token": "YOUR_AGENTVERSE_API_TOKEN",  # Optional: for Agentverse registration
    "return_dict": True  # Return a dictionary instead of a string
})

print(f"Created uAgent '{result['agent_name']}' with address {result['agent_address']} on port {result['agent_port']}")

CrewAI Adapter

The CrewAI adapter allows you to convert any CrewAI crew into a uAgent.

from crewai import Crew, Agent, Task
from uagents_adapter.crewai import CrewAIRegisterTool

# Define your CrewAI crew
agent1 = Agent(
    role="Researcher",
    goal="Research thoroughly",
    backstory="You are a skilled researcher",
    verbose=True,
    allow_delegation=False
)

task1 = Task(
    description="Research about a topic",
    agent=agent1
)

crew = Crew(
    agents=[agent1],
    tasks=[task1],
    verbose=True
)

# Create CrewAI register tool
register_tool = CrewAIRegisterTool()

# Register the crew as a uAgent
result = register_tool.invoke({
    "crew_obj": crew,
    "name": "my_crew_agent",
    "port": 8001,
    "description": "My CrewAI crew as a uAgent",
    "mailbox": True,  # Use Agentverse mailbox service
    "api_token": "YOUR_AGENTVERSE_API_TOKEN",  # Optional: for Agentverse registration
    "query_params": {
        "topic": {
            "type": "string",
            "description": "The topic to research",
            "required": True
        }
    },
    "example_query": "Research about artificial intelligence",
    "return_dict": True  # Return a dictionary instead of a string
})

print(f"Created uAgent '{result['agent_name']}' with address {result['agent_address']} on port {result['agent_port']}")

Agentverse Integration

Mailbox Service

By default, agents are created with mailbox=True, which enables the agent to use the Agentverse mailbox service. This allows agents to communicate with other agents without requiring a publicly accessible endpoint.

When mailbox is enabled:

• Agents can be reached by their agent address (e.g., agent1q...)
• No port forwarding or public IP is required
• Messages are securely handled through the Agentverse infrastructure

Agentverse Registration

You can optionally register your agent with the Agentverse API, which makes it discoverable and usable by other users in the Agentverse ecosystem:

1. Obtain an API token from Agentverse.ai
2. Include the token when registering your agent:

   result = register_tool.invoke({
       # ... other parameters
       "api_token": "YOUR_AGENTVERSE_API_TOKEN"
   })
   

When an agent is registered with Agentverse:

• It connects to the mailbox service automatically
• It appears in the Agentverse directory
• A README with input/output models is automatically generated
• The agent gets an "innovationlab" badge
• Other users can discover and interact with it
• You can monitor its usage and performance through the Agentverse dashboard

Example of auto-generated README for LangChain agents:

# Agent Name
Agent Description
![tag:innovationlab](https://img.shields.io/badge/innovationlab-3D8BD3)

**Input Data Model**

class QueryMessage(Model): query: str

**Output Data Model**

class ResponseMessage(Model): response: str

Example of auto-generated README for CrewAI agents with parameters:

# Agent Name
Agent Description
![tag:innovationlab](https://img.shields.io/badge/innovationlab-3D8BD3)

**Input Data Model**

class ParameterMessage(Model): topic: str max_results: int

**Output Data Model**

class ResponseMessage(Model): response: str

**Example Query**

Research about artificial intelligence

License

Apache 2.0