--- title: Building AI Agents in Pure Python - Beginner Course description: A hands-on beginner tutorial walks through building an AI agent from scratch in pure Python, without frameworks like LangChain or third-party agent tools. It... canonical: https://daily.dev/posts/building-ai-agents-in-pure-python---beginner-course-dxruycp0p twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Building AI Agents in Pure Python - Beginner Course | daily.dev og:description: A hands-on beginner tutorial walks through building an AI agent from scratch in pure Python, without frameworks like LangChain or third-party agent tools. It... og:url: https://daily.dev/posts/building-ai-agents-in-pure-python---beginner-course-dxruycp0p og:image: https://api.daily.dev/og/posts/DXRUycP0P.png og:image:alt: Building AI Agents in Pure Python - Beginner Course og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Building AI Agents in Pure Python - Beginner Course **[Tech With Tim](https://daily.dev/sources/TechWithTim)** · 37 min read · 3 upvotes · 0 comments ## Summary A hands-on beginner tutorial walks through building an AI agent from scratch in pure Python, without frameworks like LangChain or third-party agent tools. It breaks agents down into three core components - a model, tools, and a loop - then codes each step: calling the OpenAI API, tracking conversation history/context, and implementing tool calling with manually written schemas. The video culminates in building a 'mini Claude Code' style coding agent with four tools (list files, read file, write file, run command) that can create directories, write a Python Pygame snake game, and refactor code across multiple files, all driven by a loop that keeps calling tools until the model returns a plain text response. The video includes a HubSpot-sponsored segment promoting a free 'AI Agents Unleashed' playbook. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### What are the three core components needed to build an AI agent from scratch? An AI agent fundamentally consists of a model, tools, and a loop. The model is an LLM (accessed via API, whether local through Ollama or cloud-based like OpenAI) that only generates text; tools are functions (Python functions, MCP servers, or web search) that perform actual actions; and the loop lets the agent repeatedly call tools and feed results back to the model until no more tool calls are needed. Popular tools like Claude Code and Cursor are built on this same pattern. _Developers exploring how coding agents work under the hood can track deeper agent-building breakdowns on daily.dev._ ### Why doesn't my chatbot remember what I said earlier when calling an LLM API repeatedly? LLMs have no built-in memory; each API call is stateless and only knows what's included in the messages list passed to it. To maintain conversation history, you must manually append every user message and assistant response to a growing messages array and resend the entire list with each request, since the model only sees exactly what's contained in that context window. _Developers debugging stateless LLM behavior can find more grounded API patterns on daily.dev._ ### How does tool calling work with the OpenAI chat completions API in Python? The OpenAI API lets you pass a tools parameter containing JSON schemas describing available functions, their parameters, and descriptions; the model can then respond with a request to call a specific function with certain arguments rather than generating text. Your own code must execute that function, capture the result, and append it back into the messages list so the model can decide whether to call more tools or return a final text response. _Developers wiring up custom tool-calling loops can compare implementation patterns on daily.dev._ --- Tags: [#python](https://daily.dev/tags/python), [#llm](https://daily.dev/tags/llm), [#ai-agents](https://daily.dev/tags/ai-agents), [#openai](https://daily.dev/tags/openai) [View this post on daily.dev](https://daily.dev/posts/building-ai-agents-in-pure-python---beginner-course-dxruycp0p) ```json {"@context":"https://schema.org","@graph":[{"@type":"Organization","@id":"https://daily.dev/#organization","name":"daily.dev","url":"https://daily.dev","logo":{"@type":"ImageObject","url":"https://daily.dev/apple-touch-icon.png","width":180,"height":180},"sameAs":["https://twitter.com/dailydotdev","https://github.com/dailydotdev","https://www.linkedin.com/company/daily-dev-ltd"]},{"@type":"WebSite","@id":"https://daily.dev/#website","url":"https://daily.dev","name":"daily.dev","publisher":{"@id":"https://daily.dev/#organization"},"potentialAction":{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https://daily.dev/search?q={search_term_string}"},"query-input":"required name=search_term_string"}}]} {"@context":"https://schema.org","@type":"TechArticle","headline":"Building AI Agents in Pure Python - Beginner Course","url":"https://daily.dev/posts/building-ai-agents-in-pure-python---beginner-course-dxruycp0p","mainEntityOfPage":{"@type":"WebPage","@id":"https://daily.dev/posts/building-ai-agents-in-pure-python---beginner-course-dxruycp0p"},"datePublished":"2026-08-30T13:03:07.752Z","dateModified":"2026-08-30T13:08:47.872Z","description":"A hands-on beginner tutorial walks through building an AI agent from scratch in pure Python, without frameworks like LangChain or third-party agent tools. 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