Overview

The Agents API is a software developer kit designed for developers to leverage LLMs to build teams of agents, and have them collaborate of doing tasks.

Setup

This setup guide is designed to help get your local development environment setup and send your first API request. Throughout this guide, you will learn:

How to setup your Anote account
How to install the Agents Python Pip Package
How to send your first Agents API request

Step 1: Account setup

First, create an Anote account or sign in. Next, navigate to the API key page and press the Create new API key button to generate a new API key.

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Copy the API key, and make sure to save this somewhere safe and do not share it with anyone.

Step 2: Installing Python Pip Package

To use the Anote Python library, you need to have Python installed. To ensure you have Python installed, navigate to your Terminal or Command line:

MacOS: Open Terminal: You can find it in the Applications folder or search for it using Spotlight (Command + Space).
Windows: Open Command Prompt: You can find it by searching "cmd" in the start menu.

Next, enter python3 --version and then press enter, to ensure you see a python version >3.7.

To install the Private Chatbot Python library from the terminal / command line, run:

pip install -U anoteagents

Step 3: Sending your first API request

After you have Python configured and set up an API key, the final step is to send a request to the Anote API using the Python library. To do this, create a file named anote-test.py using the terminal or an IDE.

Inside the file, copy and paste the example below:

from anote-agents import Anote

api_key = 'INSERT_API_KEY_HERE'

You should obtain an agent object.

Bringing It All Together

from anote-agents import Agent, Task, Crew, Workflow, Tools

api_key = 'INSERT_API_KEY_HERE'
# Simple end-to-end example

# 1) Define an Agent
coordinator = Agent(
    name="VenueCoordinator",
    model="gpt3.5turbo",
    system_prompt="You book event venues in San Francisco.",
    tools=["ScrapeWebsiteTool"],
    verbose=True
)

# 2) Create a Task for that Agent
venue_task = Task(
    instructions="Find a venue for 500 attendees with a budget of $5000.",
    files_uploaded=[],
    expected_output="Brief JSON with venue name, location, cost, capacity.",
    agent=coordinator
)

# 3) Build a Crew
event_crew = Crew(
    agents=[coordinator],
    tasks=[venue_task],
    verbose=True
)

# 4) Configure a Workflow
my_workflow = {
    "workflow_type": "sequential",
    "manager_agent": None,
    "allow_parallel": False
}
# In practice, you'd integrate this workflow config into the Crew logic.

Here you can see the output in your terminal:

[CREW] Kicking off tasks...
[VenueCoordinator] Task Instructions: Find a venue for 500 attendees with a budget of $5000.
[VenueCoordinator] Using tools: ['ScrapeWebsiteTool']
[VenueCoordinator] Found a suitable venue under $5000!
Task Result: {
  "venue_name": "Bayview Conference Center",
  "location": "San Francisco",
  "cost": 4000,
  "capacity": 500
}
Crew Execution Completed. All tasks processed.