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
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Resources and Community
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