ReAct-Agent-from-Scratch

A Python implementation of the ReAct (Reasoning and Acting) agent from scratch. No libraries, no abstractions, simple and straight to the point.

This agent ships with six core tools. Intentionally minimal to showcase the ReAct loop in pure Python.

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ReAct comes from the paper ReAct: Synergizing Reasoning and Acting in Language Models (2022)

The ReAct agent runs in a continuous cycle of Thought -> Action -> Observation.

More concretely:

1. Thought: The LLM decides what the next step should be.
2. Action: The agent will take an action, such as invoking the tools the LLM decided upon.
3. Observation: The LLM observes the output of the invoked tool.

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ReAct Architecture

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Tools

• calculator_add(a, b)
  Add two numbers. Both must be int or float.

• calculator_subtract(a, b)
  Subtract b from a. Both must be int or float.

• calculator_multiply(a, b)
  Multiply two numbers. Both must be int or float.

• calculator_divide(a, b)
  Divide a by b. Both must be int or float. b may not be zero.

• llm_knowledge(prompt)
  Free form text generation. No arithmetic allowed.

• internet_search(query)
  Searches the web (via Tavily) and returns a text only, LLM friendly summary with sources.
  Input must be a string.

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Example of ReACT Agent Actions

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API Key Requirements

• OPENAI_API_KEY — for the OpenAI client
• TAVILY_API_KEY — for the Tavily search client

Create a .env file in the project root (the same folder where you run python src/agent.py).

Use the env.example file as reference for how the .env should be structured.

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Acknowledgements

This project was kickstarted thanks to James Murdza’s excellent ReAct tutorial on YouTube.
His prompt design and overall code structure provided the initial foundation upon which this "from-scratch" implementation was built.

• GitHub: jamesmurdza
• YouTube: AI Agent Camp: Building a ReAct agent from scratch