uAgents Adapters: Connecting AI Framework Ecosystems

uAgents Adapters provide a bridge between the uAgents ecosystem and various agentic frameworks, enabling seamless communication between different AI agent architectures.

Why Use Adapters?

AI development landscapes often involve multiple frameworks and technologies, each with their own strengths:

• LangChain: Powerful for composing LLMs with tools and chains
• LangGraph: Excellent for complex orchestration and stateful workflows
• CrewAI: Specialized for multi-agent collaborative systems

The uAgents Adapter package allows you to leverage these specialized frameworks while still benefiting from the uAgents ecosystem for communication, discovery, and deployment.

Available Adapters

The uAgents Adapter package currently supports several major AI frameworks:

1. LangChain Adapter

Connect LangChain agents, chains, and tools to the uAgents ecosystem.

from uagents_adapter.langchain import UAgentRegisterTool

# Register a LangChain agent as a uAgent
tool = UAgentRegisterTool()
agent_info = tool.invoke({
    "agent_obj": langchain_agent,
    "name": "my_langchain_agent",
    "port": 8000,
    "description": "A LangChain agent powered by GPT-4",
    "api_token": AGENTVERSE_API_KEY
})

2. LangGraph Adapter

Integrate LangGraph's powerful orchestration with uAgents.

from uagents_adapter.langgraph import UAgentRegisterTool

# Wrap LangGraph agent function for uAgent integration
def langgraph_agent_func(query):
    # Process with LangGraph
    result = langgraph_app.invoke(query)
    return result

# Register the LangGraph function as a uAgent
tool = UAgentRegisterTool()
agent_info = tool.invoke({
    "agent_obj": langgraph_agent_func,
    "name": "my_langgraph_agent",
    "port": 8080,
    "description": "A LangGraph orchestration agent",
    "api_token": AGENTVERSE_API_KEY
})

3. CrewAI Adapter

Expose CrewAI's collaborative agent teams as uAgents.

from uagents_adapter.crewai import UAgentRegisterTool

# Create a function to handle CrewAI operations
def crew_handler(query):
    # Process with CrewAI
    result = my_crew.kickoff(inputs={"query": query})
    return result

# Register the CrewAI function as a uAgent
tool = UAgentRegisterTool()
agent_info = tool.invoke({
    "agent_obj": crew_handler,
    "name": "my_crew_agent",
    "port": 8081,
    "description": "A CrewAI team of specialized agents",
    "api_token": AGENTVERSE_API_KEY
})

Common Parameters

All adapters accept the following parameters:

Parameter	Type	Description
agent_obj	object	The framework-specific agent or function to wrap
name	string	Name for your agent in the uAgents ecosystem
port	int	Port for the agent's HTTP server
description	string	Human-readable description of agent capabilities
api_token	string	Your Agentverse API key for registration
mailbox	bool	Whether to use Agentverse mailbox for persistence (optional)
ai_agent_address	string	AI Agent address to conver Natural language into structured query prompt (optional)

Communication Protocol

Once registered, adapter agents communicate using the uAgents chat protocol:

from uagents_core.contrib.protocols.chat import (
    ChatMessage, TextContent
)

# Send a message to an adapter-wrapped agent
message = ChatMessage(
    timestamp=datetime.utcnow(),
    msg_id=uuid4(),
    content=[TextContent(type="text", text="Your query here")]
)
await ctx.send(adapter_agent_address, message)

Cleanup and Management

Always clean up your agents when shutting down to ensure proper deregistration:

from uagents_adapter.langchain import cleanup_uagent

try:
    # Your agent code here
    while True:
        time.sleep(1)
except KeyboardInterrupt:
    # Clean up the agent
    cleanup_uagent("your_agent_name")
    print("Agent stopped.")

Next Steps

To explore concrete examples of adapter usage, refer to the uAgents Adapter Examples section.