This project demonstrates the creation of an AI agent using LangGraph and the ReAct Pattern. The goal is to build a robust and interactive AI system capable of reasoning and acting based on user inputs.
Paper Title: REACT: Synergizing Reasoning and Acting in Language Models
Focus: Combines language model reasoning capabilities with real-time action-taking (e.g., tool use, external environments).
Keywords: Language Models, Reasoning, Tool Use, Agents, REACT, LLMs, Decision-Making
A framework that enables language models not just to generate text, but also to interact with tools and environments—merging logical reasoning with real-world execution.
- LangGraph Integration: Utilize LangGraph for managing complex workflows and dependencies.
- ReAct Pattern: Combine reasoning and acting for dynamic decision-making.
- Extensibility: Easily extend the agent with new capabilities.
- Interactive: Engage with the agent through a user-friendly interface.
Contributions are welcome! Please follow these steps:
- Fork the repository.
- Create a new branch:
git checkout -b feature-name. - Commit your changes:
git commit -m 'Add feature'. - Push to the branch:
git push origin feature-name. - Open a pull request.
Description:
Agent 1 is a fully custom-built agent designed from the ground up. It leverages the core principles of the ReAct Pattern and integrates seamlessly with LangGraph to handle complex workflows. This agent serves as a foundational example for creating bespoke AI agents tailored to specific use cases.
Key Features:
- Custom Logic: Implements unique reasoning and acting capabilities to address specialized tasks.
- LangGraph Integration: Utilizes LangGraph nodes and edges to define workflows and dependencies.
- Extensibility: Designed to be modular, allowing for easy addition of new tools or functionalities.
- Interactive Interface: Provides a user-friendly interface for real-time interaction and feedback.
Use Case Example:
Agent 1 can be configured to act as a virtual assistant, capable of scheduling tasks, retrieving information, and interacting with external APIs to perform actions based on user inputs.
Implementation Details:
- Reasoning: Uses LangGraph to model decision-making processes.
- Acting: Executes actions by interfacing with external tools and APIs.
- Feedback Loop: Continuously refines its behavior based on user feedback and task outcomes.
This agent serves as a starting point for building more advanced agents in the project.
This project is licensed under the MIT License.