Building AI Agents with AgentLite: A Comprehensive Guide

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Introduction

AI agents are revolutionizing automation, task execution, and decision-making processes. AgentLite is an open-source framework designed to streamline the development of intelligent, task-oriented AI agents using large language models (LLMs). Whether you want to build a simple single-agent system or a sophisticated multi-agent architecture, AgentLite provides an easy-to-use and highly customizable solution.

In this guide, we will:

• Walk through AgentLite’s key features and setup process.
• Build a Hello World agent to understand the framework.
• Develop an AI-powered chess-playing agent that interacts with a chessboard using AgentLite.
• Explore the practical applications of AI agents in various domains.

By the end of this tutorial, you’ll be equipped to create your own custom AI agents for real-world applications!

🔹 Key Features of AgentLite

• ✅ Lightweight & Easy-to-Use: Simple syntax and intuitive structure make agent creation seamless.
• ✅ Supports Multi-Agent Collaboration: Build networks of AI agents that communicate and solve tasks together.
• ✅ Highly Customizable: Tailor agents for applications such as automation, gaming, and customer service.

🚀 Setup & Installation

Before we start coding, install the required dependencies:

!pip install agentlite-llm chess
!pip install --upgrade langchain langchain-openai

We also need to set up an OpenAI API Key for accessing the language model:

from google.colab import userdata
OPENAI_API_KEY = userdata.get("OPENAI_API_KEY")

Why Use AgentLite? AgentLite simplifies the process of developing AI agents that interact with LLMs. By integrating with frameworks like LangChain, it provides a structured approach to agent design.

Creating a "Hello World" Agent

To understand AgentLite’s core workflow, let’s create a simple agent that responds with “Hello World!” when given any instruction.

Defining the Agent

from agentlite.agents import BaseAgent
from agentlite.llm.LLMConfig import LLMConfig
from agentlite.llm.agent_llms import LangchainChatModel
from agentlite.commons import TaskPackage

class HelloWorldAgent(BaseAgent):
    def __init__(self, llm):
        super().__init__(name="HelloWorld", role="Responds with Hello World!", llm=llm)
    
    def respond(self, task, **kwargs):
        return "Hello World!"

llm_config = LLMConfig({"llm_name": "gpt-3.5-turbo", "temperature": 0.0, "api_key": OPENAI_API_KEY})
llm = LangchainChatModel(llm_config)
hello_agent = HelloWorldAgent(llm=llm)

Executing the Agent

task = TaskPackage(instruction="Any instruction here")
response = hello_agent(task)
print(response)

✅ Expected Output:

Hello World!

📌 Explanation:

• BaseAgent is the parent class that all AI agents inherit from.
• respond() is the agent’s function that generates a reply.
• The TaskPackage object contains the input instruction.
• The agent simply returns "Hello World!" for any given input.

Integrating Chess with AgentLite

Next, let’s take things further by building an AI agent that plays chess!

Implementing a Chess Move Action

We first define an action that allows the agent to make a move on a chessboard.

import chess
import chess.svg
from agentlite.actions import BaseAction
from IPython.display import display, HTML

BOARD = chess.Board()

class BoardMove(BaseAction):
    def __init__(self) -> None:
        super().__init__(
            action_name="BoardMove",
            action_desc="Move a piece on the chessboard",
            params_doc={"query": "Standard Universal Chess Interface Move"},
        )
    
    def __call__(self, query):
        try:
            BOARD.push_uci(query)
        except ValueError as e:
            return f"Error: {e}. Illegal move."
        
        m = chess.Move.from_uci(query)
        svg_code = chess.svg.board(BOARD, arrows=[(m.from_square, m.to_square)], fill={m.from_square: "gray"}, size=200)
        display(HTML(svg_code))
        return f"Moved: {query}."

👀 Viewing Board State

class BoardView(BaseAction):
    def __init__(self) -> None:
        super().__init__(
            action_name="BoardView",
            action_desc="Get the current board status in FEN format",
            params_doc={"key": "True"},
        )
    
    def __call__(self, key, **kwargs):
        return BOARD.fen()

✅ Expected Output:

rnbqkbnr/pppppppp/8/8/8/8/PPPPPPPP/RNBQKBNR w KQkq - 0 1

(This represents the initial chessboard state.)

🎭 Creating a Chess Player Agent

Now, we define an AI-powered chess player that interacts with the board.

from agentlite.agents import BaseAgent
from agentlite.llm.agent_llms import BaseLLM
from agentlite.actions import LegalMoves
from agentlite.logging import AgentLogger

class ChessPlayerAgent(BaseAgent):
    def __init__(self, llm: BaseLLM, color: str):
        if color not in ["white", "black"]:
            raise ValueError("color must be either white or black")
        
        name = f"{color}_player"
        role = f"You are {name}, playing against the opponent."
        
        super().__init__(name=name, role=role, llm=llm, actions=[BoardMove(), BoardView(), LegalMoves()])

🔄 Running the Chess Game

player_white = ChessPlayerAgent(llm=llm, color="white")
player_black = ChessPlayerAgent(llm=llm, color="black")

exp_task = TaskPackage(instruction="It is your turn")
player_white(exp_task)
player_black(exp_task)

Expected Chess Move Output:

Chess Moved. It's black_player's turn.
Chess Moved. It's white_player's turn.

Conclusion

In this guide, we explored AgentLite, built a Hello World agent, and created an AI-powered chess player capable of interacting with a chessboard. AI agents like these can be applied to gaming, automation, and task delegation in various industries.

Further Resources

• AgentLite GitHub Repository
• LangChain Documentation
• OpenAI API Reference
• Build Fast with AI Notebook

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