AI Agents
Running Locally
ℹī¸

This is a work in progress article, and will be expanded rapidly.

Running locally

Sometimes you'll want to run an agent on your own hardware or infrastructure; luckily this is very easy to do on any system that support Python 3.10

Introduction

This system is pretty simple, as to get you started as quickly as possible. We're going to run this agent on any device you'd like, in this scenario we're running on a vm bu you could run this on yur laptop, raspberry pi or tweak for agentverse. On startup our script will register our agent to the Almanc, and then our agent will be available to communicate with other agents. To get this agent to be DeltaV accessible, we will also go to agentverse to create a new service for the agent, to then allow this agent to be found in DeltaV.

The agent:

agent.py
from uagents.setup import fund_agent_if_low
from uagents import Agent, Context, Protocol, Model
import random
from pydantic import Field
from ai_engine import UAgentResponse, UAgentResponseType
import sys
 
dungeons = Agent(
    name="dungeonsanddragonsdiceroll",
    port=6145,
    seed="RANDOM STRINGS",
    endpoint=["http://YOUR_IP:6145/submit"],
)
 
fund_agent_if_low(dungeons.wallet.address())
 
 
@dungeons.on_event("startup")
async def hi(ctx: Context):
    ctx.logger.info(dungeons.address)
 
 
class Request(Model):
    dice_sides: int = Field(description="How many sides does your dice need?")
 
 
dice_roll_protocol = Protocol("DungeonsAndDragonsDiceRoll")
 
 
@dice_roll_protocol.on_message(model=Request, replies={UAgentResponse})
async def roll_dice(ctx: Context, sender: str, msg: Request):
    result = str(random.randint(1, msg.dice_sides))
    message = f"Dice roll result: {result}"
    await ctx.send(
        sender, UAgentResponse(message=message, type=UAgentResponseType.FINAL)
    )
 
 
dungeons.include(dice_roll_protocol, publish_manifest=True)
 
dungeons.run()

A few things to note; you'll need to be running this agent on infrastructure that allows you to open a port, in our example we run on port 6145.

The agent is initialised with an endpoint, and a port - this is so that we can receive messages, and other agents know where to send them. We call fund_agent_if_low to get some funds, if we need them. And we define our protocol, which is just an int as seen in the Request object.

Our on_message doesn't do much other than return a number between 1 and the defined dice_sides from the message inclusive. However, the response type is of UAgentResponse which is essential to communicate with DeltaV.

.run() initialises the agent.

Finally, we run our agent as follows: python agent.py

Expected output:

INFO:     [dungeonsanddragonsdiceroll]: Manifest published successfully: DungeonsAndDragonsDiceRoll
INFO:     [dungeonsanddragonsdiceroll]: Registering on almanac contract...
INFO:     [dungeonsanddragonsdiceroll]: Registering on almanac contract...complete
INFO:     [dungeonsanddragonsdiceroll]: agent1qvh76795enwgnzkrjpedlnqxwv83d8wxnkkcszs9z46zc3qpfs3yvzc5kuw
INFO:     [dungeonsanddragonsdiceroll]: Starting server on http://0.0.0.0:6145 (Press CTRL+C to quit)

creating a service group

For this example we set up a really simple service for a pre-existing service group, for further information on services and service groups see Registering Agent Services ↗ī¸

Interacting on deltav

Then we head over to deltav.agentverse.ai and get the ai-engine to interact with our agent on our behalf.

It's recommended you alter the contract slightly, and follow the above steps so that you can run an agent, create a service for the agent and then have that agent accessible by DeltaV.

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