Computer Science · beginner
Build an AI Agent From Scratch in Python
A from-scratch agent that pairs an LLM with custom tools — no LangChain, no CrewAI, just pure Python so you understand exactly how agents work under the hood.
Project overview
Demonstrates you can build intelligent systems and proves you understand the fundamentals — not just the wrappers — which is what makes a candidate stand out in the modern AI job market.
Real-world use case
Automating customer service responses, data analysis, or content generation for businesses without paying for closed-source agent platforms.
Tools and technologies
- Python
- OpenAI API
- LangChain (optional)
- JSON tool schemas
Skills you can demonstrate
Python Programming, Agent Loops, LLM Tool Use
Build roadmap
- Set up the environment
Create a virtualenv. Install openai (or anthropic). Get an API key. Test a basic chat completion call to confirm everything works. - Write the agent loop
A while loop: send the conversation to the LLM, parse the response, decide if it asked for a tool, run the tool, append the result, repeat until the LLM gives a final answer. - Define your first tool
Write a Python function (e.g., get_weather(city)). Wrap it with a JSON schema describing its name, args, and what it does. Pass that schema to the LLM. - Handle tool calls
When the LLM responds with a tool call, execute the function, capture the result, and feed it back as the next message. This is the heart of an agent. - Add a second tool + memory
Add a calculator tool. Store the conversation history in a list so the agent can reference earlier turns. Cap history length so context doesn't blow up. - Demo and document
Record a session where the agent uses both tools to answer a question that requires reasoning ("What's the temperature in Miami in Celsius?"). Push the code with a clean README.
Ways to extend the project
Add memory (short-term + long-term), tool use (web search, calculators), and multi-agent collaboration where specialized agents hand off subtasks.
How to showcase your work
- GitHub: README that walks the reader through the agent loop step by step. Include a Mermaid diagram of the control flow.
- LinkedIn: A 90-second video where you ask the agent a question that requires 2 tool calls and narrate what's happening internally.
- Resume: Focus on "Python programming", "Agent architectures", and "From-scratch LLM integration".