ReAct Agent from Scratch
Basic Information
This repository provides a pure Python implementation of a ReAct-style AI agent built from scratch without using frameworks like LangChain. It is intended to demonstrate and teach the ReAct reasoning loop of Thought, Action, PAUSE, and Observation by implementing that cycle explicitly. The project integrates several simple tools including a calculator, Wikipedia access, web search, and a weather tool so the agent can perform information retrieval and basic computation. A Streamlit-based web UI is included to interact with the agent. The README and repository topics indicate a focus on a framework-free, educational demonstration and an example of retrieval-augmented or tool-augmented agent behavior.
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App Details
Features
A framework-free ReAct agent implemented in pure Python that follows the Thought, Action, PAUSE, Observation loop. Integrated tool set that includes Calculator, Wikipedia lookup, Web Search, and Weather access to demonstrate tool invocation and observation handling. A user-facing Streamlit web UI for interactive experimentation and testing of the agent. Repository topics and release artifacts highlight intents such as react-loop, rag-agent, and python-no-framework-agent. The codebase purposefully avoids pre-built agent frameworks to expose internal control flow and decision steps, making the implementation compact and focused on core agent mechanics.
Use Cases
This project is useful as an educational and experimental resource for developers and researchers who want to understand how ReAct agents operate without abstraction from higher-level frameworks. It shows how to wire together thought and action cycles, how to call and consume results from simple tools, and how to surface agent behavior through a Streamlit UI for interactive testing. The tool integrations provide concrete examples of combining search, knowledge lookup, computation, and external data like weather. The repository can serve as a starting point for prototyping, learning, or extending to more advanced retrieval, tool sets, or UI features while keeping implementation details visible.
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