Agents and Functions Creation using APIs

Introduction

This example provides details on how to create Agents and respective Agent Functions in Agentverse using APIs. We provide a Python script that interacts with Agentverse allowing us to create Agents and Agent Functions.

Prerequisites

Before you begin, ensure you have the following:
- Python version greater than 3.9 and less than 3.11.
- The requests library installed. You can install it using pip install requests.
- Agentverse (opens in a new tab) Credentials.

Steps to get API Tokens

Go to Profile section in Agentverse (opens in a new tab).
Click on button + New API Key.
Give name to your API key.
Click on write for Access to all resources in Agentverse and click on Generate API Key

Steps to create agent and respective function

Open terminal and create a directory agents using mkdir agents.
Create a python file agent.py in this directory and include the following sample script in the Python file.

agent.py
from uagents import Agent, Bureau, Context, Model
from uagents.setup import fund_agent_if_low
import requests
from ai_engine import UAgentResponse, UAgentResponseType
 
 
class Coordinates(Model):
    location: str
 
 
# First generate a secure seed phrase (e.g. https://pypi.org/project/mnemonic/)
SEED_PHRASE = "put_your_seed_phrase_here"
 
# Now go to https://agentverse.ai, register your agent in the Mailroom by providing the address you just copied.
# Then, copy the agent's mailbox key and insert it here below inline
AGENT_MAILBOX_KEY = "put_your_AGENT_MAILBOX_KEY_here"
 
# Now your agent is ready to join the agentverse!
location_agent = Agent(
    name="location_agent",
    seed=SEED_PHRASE,
    mailbox=f"{AGENT_MAILBOX_KEY}@https://agentverse.ai",
)
 
fund_agent_if_low(location_agent.wallet.address())
 
# Copy the address shown below
print(f"Your agent's address is: {location_agent.address}")
 
location_protocol = Protocol("Location Coordinates")
 
 
async def location_coordinates(latitude, longitude):
    url = "https://geocoding-by-api-ninjas.p.rapidapi.com/v1/reversegeocoding"
    querystring = {"lat": latitude, "lon": longitude}
 
    headers = {
        "X-RapidAPI-Key": "YOUR_API_KEY",
        "X-RapidAPI-Host": "geocoding-by-api-ninjas.p.rapidapi.com",
    }
 
    response = requests.get(url, headers=headers, params=querystring)
 
    data = response.json()[0]["name"]
 
    return data
 
 
@location_protocol.on_message(model=Coordinates, replies=UAgentResponse)
async def location_coordinates_calculator(ctx: Context, sender: str, msg: Coordinates):
    ctx.logger.info(msg.location)
    latitude, longitude = map(str.strip, msg.location.split(","))
    city = location_coordinates(latitude, longitude)
    ctx.logger.info(city)
    message = city
    await ctx.send(
        sender, UAgentResponse(message=message, type=UAgentResponseType.FINAL)
    )
 
 
location_agent.include(location_protocol)
 
location_agent.run()

Create a python file with name agent_create.py.

Script breakdown

Importing required libraries and setting up authorization token:


# Importing Required libraries
import time
import requests
 
# Define access token
token = 'Bearer <Your_access_token>'

Taking agent Name from user and storing agent address:


# Take name of agent from user
name = input("Please give name of your agent? ")
# Create payload for agent creation request
agent_creation_data = {"name": name}
# Post request to create an agent and store address
response_agent = requests.post(
    "https://agentverse.ai/v1/hosting/agents",
    json=agent_creation_data,
    headers={"Authorization": token},
).json()
 
address = response_agent["address"]
print(f"Agent Address : {address}")

Taking code from agent.py file and storing it as created agent script:


# Reading code to be placed in agent
with open("agent.py", "r") as file:
    code = file.read()
agent_code_data = {"code": code}
 
# Creating agent.py script for created agent
response_code_update = requests.put(
    f"https://agentverse.ai/v1/hosting/agents/{address}/code",
    json=agent_code_data,
    headers={"Authorization": token},
)
 
# Starting the agent
requests.post(
    f"https://agentverse.ai/v1/hosting/agents/{address}/start",
    headers={"Authorization": token},
)
time.sleep(10)  # waiting before getting agent's protocol

Requesting protocol digest for the created Agent:


# Request to get agent protocol digest
response_protcol = requests.get(
    f"https://agentverse.ai/v1/almanac/agents/{address}",
    headers={"Authorization": token},
)
protocol_digest = response_protcol.json()["protocols"][1]
print(f"Protocol Digest : {protocol_digest}")
time.sleep(10)  # Waiting before getting model_digest
 
# Request to get agent's model details
response_model = requests.get(
    f"https://agentverse.ai/v1/almanac/manifests/protocols/{protocol_digest}",
    headers={"Authorization": token},
)
model = response_model.json()["models"]
time.sleep(10)  # Waiting before storing details to create functions
 
function_group_ids = requests.get(
    "https://agentverse.ai/v1beta1/function-groups/", headers={"Authorization": token}
)
function_group_id = function_group_ids.json()[0]["uuid"]
time.sleep(10)

Saving all the details required for creating function and creating function on basis of details received:


# Taking inputs from user for details required to create a function
name_service = input("Please give function name: ")
description = input("Please enter function description: ")
field_name = input("Please enter field name: ")
field_description = input("Please enter field description: ")
tasktype = input("Please tell primary or secondary function: ").upper()
 
# Logging details provided by user
print(
    f"Service name: {name_service} \nFunction Description: {description} \nField Name: {field_name}\nField Description: {field_description}\nTask Type: {tasktype}"
)
 
# Storing model digest and name to be used for function creation
model_digest = response_model.json()["interactions"][0]["request"].replace("model:", "")
print(f"Model Digest : {model_digest}")
model_name = model[0]["schema"]["title"]
print(f"Model Name : {model_name}")
 
# Creating payload for function creation
data = {
    "agent": address,
    "name": name_service,
    "description": description,
    "protocolDigest": protocol_digest,
    "modelDigest": model_digest,
    "modelName": model_name,
    "arguments": [
        {
            "name": field_name,
            "required": True,
            "type": "string",
            "description": field_description,
        }
    ],
    "type": tasktype,
}
 
# Requesting AI Engine function API to create a function with created payload and storing the response.
response_function = requests.post(
    "https://agentverse.ai/v1beta1/functions/",
    json=data,
    headers={"Authorization": token},
)
# Storing name of function and printing it to check if function was created successfully
name = response_function.json()["name"]
print(f"Function Created with name: {name}")

Whole Script


# Importing Required libraries
import time
import requests
 
# Decode the refresh token
token = f'Bearer <Your_access_token>'
 
# Take name of agent from user
name = input("Please give name of your agent? ")
# Create payload for agent creation request
agent_creation_data = {"name": name}
# Post request to create an agent and store address
response_agent = requests.post(
    "https://agentverse.ai/v1/hosting/agents",
    json=agent_creation_data,
    headers={"Authorization": token},
).json()
 
address = response_agent["address"]
print(f"Agent Address : {address}")
 
# Reading code to be placed in agent
with open("agent.py", "r") as file:
    code = file.read()
agent_code_data = {"code": code}
 
# Creating agent.py script for created agent
response_code_update = requests.put(
    f"https://agentverse.ai/v1/hosting/agents/{address}/code",
    json=agent_code_data,
    headers={"Authorization": token},
)
 
# Starting the agent
requests.post(
    f"https://agentverse.ai/v1/hosting/agents/{address}/start",
    headers={"Authorization": token},
)
time.sleep(10)  # waiting before getting agent's protocol
 
# Request to get agent protocol digest
response_protcol = requests.get(
    f"https://agentverse.ai/v1/almanac/agents/{address}",
    headers={"Authorization": token},
)
protocol_digest = response_protcol.json()["protocols"][1]
print(f"Protocol Digest : {protocol_digest}")
time.sleep(10)  # Waiting before getting model_digest
 
# Request to get agent's model details
response_model = requests.get(
    f"https://agentverse.ai/v1/almanac/manifests/protocols/{protocol_digest}",
    headers={"Authorization": token},
)
model = response_model.json()["models"]
time.sleep(10)  # Waiting before storing details to create functions
 
function_group_ids = requests.get(
    "https://agentverse.ai/v1beta1/function-groups/", headers={"Authorization": token}
)
function_group_id = function_group_ids.json()[0]["uuid"]
time.sleep(10)
 
# Taking inputs from user for details required to create a function
name_service = input("Please give function name: ")
description = input("Please enter function description: ")
field_name = input("Please enter field name: ")
field_description = input("Please enter field description: ")
tasktype = input("Please tell primary or secondary function: ").upper()
 
# Logging details provided by user
print(
    f"Service name: {name_service} \nFunction Description: {description} \nField Name: {field_name}\nField Description: {field_description}\nTask Type: {tasktype}"
)
 
# Storing model digest and name to be used for function creation
model_digest = response_model.json()["interactions"][0]["request"].replace("model:", "")
print(f"Model Digest : {model_digest}")
model_name = model[0]["schema"]["title"]
print(f"Model Name : {model_name}")
 
# Creating payload for function creation
data = {
    "agent": address,
    "name": name_service,
    "description": description,
    "protocolDigest": protocol_digest,
    "modelDigest": model_digest,
    "modelName": model_name,
    "arguments": [
        {
            "name": field_name,
            "required": True,
            "type": "string",
            "description": field_description,
        }
    ],
    "type": tasktype,
}
 
# Requesting AI Engine function API to create a function with created payload and storing the response.
response_function = requests.post(
    "https://agentverse.ai/v1beta1/functions/",
    json=data,
    headers={"Authorization": token},
)
# Storing name of function and printing it to check if function was created successfully
name = response_function.json()["name"]
print(f"Function Created with name: {name}")

Steps to run the script

Open terminal and go to directory agents created above.
Make sure agent.py and agent_create.py are in this directory.
Open Agentverse (opens in a new tab) and generate API keys .
Open script in editor and replace token.
Run command python agent_create.py and enter the required details.
Provide Agent and Function Details as asked and check agent and function on Agentverse.

Expected Output

Provide all details asked in the script.


abc@xyz-MacBook-Pro agents % python3 agents_create.py
Please give name of your agent? my first API agent
Agent Address : agent1q06l8hekn859e5rtwufewmyhwghe6j9y00g0wc8u7gcx05cjfk98jyf6lte
Protocol Digest : c7a6f160fd8d8b7cb357dad9b5be420510ce466dbb67051c07caf2b860216b01
Please give function name: location finder
Please enter function description: this function helps to find nearest city using coordinates
Please enter field name: location
Please enter field description: this describes the coordinates of the location in string format longitude latitude
Please tell primary or secondary function: primary
Service name: location finder 
Function Description: this function helps to find nearest city using coordinates 
Field Name: location
Field Description: this describes the coordinates of the location in string format longitude latitude
Task Type: PRIMARY
Model Digest : 10a2f843c4c92955688d5e7f22fabe79623869eabfcd97d97da83527b436d3e2
Model Name : Coordinates
Function Created with name: location finder

Agent created on Agentverse

Function created on Agentverse

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