Local Ollama Chatbot in Python

In this video, we take the next step with Ollama and Python. Last time, we built a simple script that sent one prompt to a local LLM and returned a response. But that script was stateless—every new question started from scratch.

Now we’ll upgrade it by adding memory. You’ll learn how to build a chatbot in Python that remembers past prompts and responses, carries context through the conversation, and even trims older turns to keep performance fast. This is how you turn Ollama + Python into a real conversational AI.

We’ll cover:
How to add conversation history in Python
Using a system message to control style (e.g. pirate voice)
Adding simple commands like /reset and /exit
Trimming long chats with a MAX_TURNS limit
Live demo: comparing the old script vs the new memory-enabled chatbot

By the end, you’ll know how to:
Run Ollama locally in Python
Build a chatbot with memory using the Ollama client
Control context and behavior with system messages
Start extending this foundation into GUIs, APIs, or LangChain integrations

This tutorial is perfect if you want to:
Explore local AI models and LLMs running on your machine
Learn chat history implementation in Python
Understand Ollama basics before moving into LangChain or RAG
Turn simple prompt scripts into full interactive assistants

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👉 Subscribe to the channel for more hands-on guides. What’s coming next will blow your mind—we’ll go beyond the terminal into full interactive experiences with Ollama and Python.