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.

📜 Table of Contents

• The short answer
• The 4 components
• Serving it with FastAPI
• The loop explained
• Real numbers
• FAQ

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

# 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

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.

❓ FAQ

Why build from scratch instead of using a framework?

Understanding the four components lets you debug, extend and trust your agent - frameworks are great once you know the fundamentals.

What is ReAct?

Reasoning + Acting: the pattern where the model alternates between thinking and calling tools (from the 2022 ReAct paper).

How do I add more tools?

Just write more @tool functions - the LLM discovers them from the docstrings.