Build an AI Agent from Scratch in 2026: The 4-Component Architecture with FastAPI (101k Stars) and LangChain - No Black Boxes

Learn the real architecture: an AI agent is just LLM + tools + memory + loop. Build one from scratch with FastAPI (101,321 stars) and LangChain (143,470) - you will understand every moving part.

💡 What You Will Learn

Learn the real architecture: an AI agent is just LLM + tools + memory + loop. Build one from scratch with FastAPI (101,321 stars) and LangChain (143,470) - you will understand every moving part.

## The short answer Every AI agent - no matter how fancy - is four components: an **LLM** that decides, **tools** it can call, **memory** of past steps, and a **loop** that runs until the task is done. Build these four and you have an agent you fully understand. ## The 4 components ```python # 1. LLM - the brain from langchain_openai import ChatOpenAI llm = ChatOpenAI(model="gpt-4o-mini") # 2. Tools - the hands from langchain.tools import tool @tool def get_weather(city: str) -> str: """Get current weather for a city.""" return f"Sunny, 24C in {city}" # 3. Memory - the notebook from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() # 4. Loop - the motor from langchain.agents import create_tool_calling_agent, AgentExecutor agent = create_tool_calling_agent(llm, [get_weather], prompt) executor = AgentExecutor(agent=agent, tools=[get_weather], memory=memory) print(executor.invoke({"input": "What is the weather in Shanghai?"})) ``` ## Serving it with FastAPI ```python from fastapi import FastAPI from pydantic import BaseModel app = FastAPI() class Query(BaseModel): text: str @app.post("/agent") def run_agent(q: Query): return executor.invoke({"input": q.text}) ``` ## The loop explained 1. LLM reads the task + available tools. 2. LLM decides: answer directly, or call a tool (returns structured tool call). 3. Tool executes, result goes back to LLM. 4. Repeat until the LLM decides it is done - that is the ReAct pattern. ## Real numbers - FastAPI (101,321 stars, MIT) is the fastest-growing Python web framework - the standard for serving agents. - A tool-calling agent with 3 tools handles ~90% of everyday automation tasks. - This stack is fully local-capable: swap ChatOpenAI for Ollama (177,825 stars). ## FAQ **Q: Why build from scratch instead of using a framework?** A: Understanding the four components lets you debug, extend and trust your agent - frameworks are great once you know the fundamentals. **Q: What is ReAct?** A: Reasoning + Acting: the pattern where the model alternates between thinking and calling tools (from the 2022 ReAct paper). **Q: How do I add more tools?** A: Just write more @tool functions - the LLM discovers them from the docstrings.