Building Intelligent Agents from Scratch: A Journey into LLM-Powered Autonomy
Source: tai_blog

In recent years  the advent of large language models (LLMs) has revolutionized the field of artificial intelligence  making it possible for machines to understand and generate human-like text with unprecedented accuracy. These advancements have paved the way for the creation of autonomous agents powered by LLMs  capable of performing complex tasks through natural language understanding and interaction. This article delves into the process of building such intelligent agents from scratch  without relying on high-level frameworks  to unlock their full potential.   The demand for LLM agents arises from the limitations of traditional rule-based systems and the increasing complexity of tasks in modern applications. While traditional systems can handle specific and well-defined tasks  they often fall short in dealing with the nuances and variability of natural language. LLM agents  leveraging the vast knowledge and contextual understanding embedded within large language models  offer more flexible and intelligent solutions.   The Need for LLM AgentsEnhanced User Interaction: LLM agents can engage in natural  conversational interactions  making them ideal for customer service  virtual assistants  and educational tools.Complex Problem Solving: These agents can handle diverse queries and tasks by drawing on their extensive training data  suitable for research  data analysis  and decision support systems.Automation and Efficiency: LLM agents can automate routine tasks such as scheduling  email management  and information retrieval  significantly enhancing productivity.Scalability: LLM agents can be deployed across various platforms and industries without extensive reprogramming  offering scalable solutions for businesses.Continuous Learning and Adaptation: By fine-tuning with domain-specific data  LLM agents can adapt to new information and changing requirements  ensuring their continued relevance and effectiveness.Setting Up the EnvironmentTo embark on the journey of building an LLM agent  start by setting up your environment. Ensure you have Python installed on your system and install the necessary libraries:   pip install python-dotenv groq requestsCreate a .env file in your project directory to securely store your API key:   GROQ_API_KEY=your_api_key_hereThe Agent Class with Tool Calling CapabilitiesWe will define an Agent class to interact with the language model and integrate tool-calling capabilities.   Import Libraries and Load Environment Variables:from dotenv import load_dotenv import os from groq import Groq import requests # Load environment variables from .env file load_dotenv()Define the Tool Class:class Tool: def __init__(self  name  function): self.name = name self.function = function def execute(self  *args  **kwargs): return self.function(*args  **kwargs)Define the Agent Class:class Agent: def __init__(self  client: Groq  system: str = ) -> None: self.client = client self.system = system self.messages: list = [] self.tools = {} if self.system: self.messages.append({role: system  content: system}) def add_tool(self  tool: Tool): self.tools[tool.name] = tool def __call__(self  message=): if message: self.messages.append({role: user  content: message}) response = self.execute() if response.startswith(CALL_TOOL): parts = response.split() tool_name = parts[1] params = parts[2:] result = self.tools[tool_name].execute(*params) self.messages.append({role: tool  content: result}) return result else: self.messages.append({role: assistant  content: response}) return response def execute(self): completion = self.client.chat.completions.create( model=llama3-70b-8192  messages=self.messages ) return completion.choices[0].message.contentToolsCalculator Tool :   def calculator(a  b  operation): a = float(a) b = float(b) if operation == add: return str(a + b) elif operation == subtract: return str(a - b) elif operation == multiply: return str(a * b) elif operation == divide: return str(a / b) else: return Invalid operation calc_tool = Tool(calculator  calculator)Web Search Tool:   def web_search(query): response = requests.get(fhttps://api.duckduckgo.com/?q={query}&format=json&pretty=1) if response.status_code == 200: return response.json()[results] else: return Failed to fetch results search_tool = Tool(web_search  web_search)Using the Agent with Tools:os.environ[GROQ_API_KEY] = os.getenv(GROQ_API_KEY) client = Groq() agent = Agent(client  system=You are a helpful assistant.) # Add tools to the agent agent.add_tool(calc_tool) agent.add_tool(search_tool) # Call the web search tool response = agent(CALL_TOOL web_search what is weather today in new york) print(response)Output:ConclusionBuilding an AI agent from scratch without frameworks offers a deeper understanding of the underlying processes and greater control over the implementation. This guide demonstrated how to create a simple conversational agent  integrate tool-calling capabilities  and interact with various tools using basic libraries and a hypothetical language model API. By expanding on this foundation  you can develop more sophisticated agents tailored to specific tasks and domains  unleashing the transformative potential of LLM-powered autonomy.   Additional Resource:Code :https://github.com/imanoop7/Agents-from-Scratch   Feel free to explore these resources  and happy learning! If you have any more questions  feel free to ask. U+1F60A   If you liked this article and you want to support me:Clap my article 10 times; that will really help me out.U+1F44FFollow me on Medium and subscribe for Free to get my latest articleU+1FAF6