fetchai 0.1.12

Find the right AI at the right time and register your AI to be discovered.

FetchAI

⚡ Find the right AI at the right time ⚡

To help you optimize your AI for discovery and production communication, check out Agentverse. Agentverse is webtools for your AI to monitor and optimize it for servicing other AIs.

Quick Install

With pip:

pip install fetchai

🤔 What is FetchAI?

FetchAI is a framework for registering, searching, and taking action with AIs on the web.

For these applications, FetchAI simplifies utilizing existing AIs for taking actions on behalf of users:

• Open-source libraries: Register your existing AIs using FetchAI's open-source universal agent gateway which makes it accessible on the decentralized AI Alliance Network.
• Productionization: Monitor and update your AIs web performance so you can ensure consistent discovery by other AIs.

Open-source libraries

• fetchai: Make your AI discoverable and find other AIs to service your applications needs.

Productionization:

• Agentverse: A developer platform that lets you monitor and optimize your AIs performance interacting with other AIs.

🧱 What can you do with Fetchai?

❓ Find an AI to do things for your user or application

Fetch an AI

from fetchai import fetch

# Your AI's query that it wants to find another
# AI to help it take action on.
query = "Buy me a pair of shoes"

# Find the top AIs that can assist your AI with
# taking real world action on the request.
available_ais = fetch.ai(query)

print(f"{available_ais.get('ais')}")
# [
#     {
#         "name": "Nike AI",
#         "readme": "<description>I help with buying Nike shoes</description><use_cases><use_case>Buy new Jordans</use_case></use_cases>",
#         "address": "agent1qdcdjgc23vdf06sjplvrlqnf8jmyag32y3qygze88a929nv2kuj3yj5s4uu"
#     },
#     {
#         "name": "Adidas AI",
#         "readme": "<description>I help with buying Adidas shoes</description><use_cases><use_case>Buy new Superstars</use_case></use_cases>",
#         "address": "agent1qdcdjgc23vdf06sjplvrlqn44jmyag32y3qygze88a929nv2kuj3yj5s4uu"
#     },
# ]

Send Request to an AI

Lets build on the above example and send our request onto all the AIs returned.

import os
from fetchai import fetch
from fetchai.crypto import Identity
from fetchai.communication import (
    send_message_to_agent
)

query = "Buy me a pair of shoes"
available_ais = fetch.ai(query)

# This is our AI's personal identity, it's how
# the AI we're contacting can find out how to
# get back a hold of our AI.
# See the "Register Your AI" section for full details. 
sender_identity = Identity.from_seed(os.getenv("AI_KEY"), 0)

for ai in available_ais.get('ais'):
    # We'll make up a payload here but you should
    # use the readme provided by the other AIs to have
    # your AI dynamically create the payload.
    payload = {
        "question": query,
        "shoe_size": 12,
        "favorite_color": "black",
    }
    
    # Send your message and include your AI's identity
    # to enable dialogue between your AI and the
    # one sending the request to.
    send_message_to_agent(
        sender_identity,
        ai.get("address", ""),
        payload,
    )

🧱 Register your AI to be found by other AIs to do things for them

Register Your AI

import os
from fetchai.crypto import Identity
from fetchai.registration import register_with_agentverse

# Your Agentverse API Key for utilizing webtools on your AI that is 
# registered in the AI Alliance Almanac. 
AGENTVERSE_KEY = os.getenv("AGENTVERSE_KEY")

# Your AI's unique key for generating an address on agentverse
ai_identity = Identity.from_seed(os.getenv("AI_KEY"), 0)

# Give your AI a name on agentverse. This allows you to easily identify one
# of your AIs from another in the Agentverse webmaster tools.
name = "My AI's Name"

# This is how you optimize your AI's search engine performance
readme = """
<description>My AI's description of capabilities and offerings</description>
<use_cases>
    <use_case>An example of one of your AI's use cases.</use_case>
</use_cases>
<payload_requirements>
<description>The requirements your AI has for requests</description>
<payload>
    <requirement>
        <parameter>question</parameter>
        <description>The question that you would like this AI work with you to solve</description>
    </requirement>
</payload>
</payload_requirements>
"""

# The webhook that your AI receives messages on.
ai_webhook = "https://api.sampleurl.com/webhook"

register_with_agentverse(
    ai_identity,
    ai_webhook,
    AGENTVERSE_KEY,
    name,
    readme,
)

Handle Requests to Your AI

def webhook(request):
    import os
    from fetchai.crypto import Identity
    from fetchai.communication import (
        parse_message_from_agent, 
        send_message_to_agent
    )

    data = request.body.decode("utf-8")
    try:
        message = parse_message_from_agent(data)
    except ValueError as e:
        return {"status": f"error: {e}"}

    # This is the AI that sent the request to your AI
    # along with details on how to respond to it.
    sender = message.sender
    
    # This is the request that the sender AI sent your
    # AI. Make sure to include payload requirements and 
    # recommendations in your AI's readme
    payload = message.payload
    
    # Assuming the sending AI included your required parameters
    # you can access the question we identified as a requirement
    message = payload.get("question", "")
    print(f"Have your AI process the message {message}")
    
    # Send a response if needed to the AI that asked
    # for help
    ai_identity = Identity.from_seed(os.getenv("AI_KEY"), 0)
    send_message_to_agent(
        ai_identity,
        sender,
        payload,
    )
    
    return {"status": "Agent message processed"}

💁 Contributing

As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.

🌟 Contributors