Agents - uAgents Framework

Introduction

The uAgents Framework is a lightweight library designed to facilitate the development of decentralized Agents. Agents in a multi-agent system can communicate with any, and all agents in the system to solve problems, execute tasks and transact.

Getting into Agents

Agents are programs designed to operate freely and communicate with whomever they're programmed to. Agents can connect, search, and transact in order to create dynamic markets, and they can be programmed to interact both within their environment and with other agents in the network. Because they're siloed, and decentralized they can safely accomplish particular activities and objectives without requiring human participation. We have a very simple guide in our documentation that gets you started on building an agent to be part of the network .

At the simplest level, and agent would work as follows:

Of course, many agents in the above workflow can come together to become multi-agents workflows, where single Agents call one another to complete a task. Agents that you design could be programmed to contact known agents, whereas in a dynamic marketplace you may need an agent that you haven't created, searching and interacting with that agent may be the more optimal strategy.

A simple Agent using the uAgents library could be:

from uagents import Agent, Context, Model
 
agent = Agent(name="concept", seed="secret_seed_phrase_concept", endpoint="127.0.0.1", port="8001")

class Message(Model):
    message : str

 
@agent.on_message(model=Message)
async def print_message(ctx: Context, msg : Message):
    ctx.logger.info(f"Message received: {msg.message}")
 
if __name__ == "__main__":
    agent.run()

This Agent defines its communication method as receiving any Object of Class Message, with a value for message of type string. You can see how this agent behaves, and how to extend this in our guides

Agents are lightweight programs that can be inserted to existing systems with the ability to simplify the way we see complicated systems. As an example, supply chain management could deploy Agents using the uAgents Framework to improve operations at various stages. Demand forecasting, inventory control, logistics optimization, supplier relationships monitoring, quality control and risk mitigation in all areas can be done with their help. Agents could transform supply chain operations by increasing efficiency, reducing costs, improving accuracy and providing real-time visibility.

You can view the source code for an Agent that monitors inventory levels in our examples (opens in a new tab)

Agents thrive on IoT devices such as Raspberry Pi, and there are some great examples of multi-agent simulations using Agents on Raspberry Pi available on Github (opens in a new tab).

LLMs

Agents can wrap and orchestrate LLMs to create personalized Agents for any task. With the rise of Large Language Models (LLMs) and AI-related products, autonomous intelligent Agents have become the link between these models and tools. They are revolutionizing the way we solve problems, make decisions and collaborate with each other.

Integrating LLMs into an Agent is relatively trivial, we have a guide for that too

Getting started with Agent development!

Visit the GitHub repository (opens in a new tab) to see the source code for uAgents, and to keep up-to-date with any update made to the uAgents Framework.

From there, view the examples on uAgents repository, or start reading our guides, we'd recommend starting with agent to agent communication .