Reflection Agent

Introduction

Simple Reflection Agent where make twitter post better by few iterations.

General information

• This project uses conda as an environment manager. The user must have Anaconda or Miniconda installed.
• This project uses git-hooks to check code quality when creating commits.

Repository

• https://github.com/bchabros/reflection_agent.git

Local env setup

1. Conda

• Install conda (miniconda version): https://docs.conda.io/projects/conda/en/latest/user-guide/install/index.html
• Install conda (anaconda navigator version): https://anaconda.org/anaconda/conda
• Make sure conda directory (C:\Users\<USER>\AppData\Local\miniconda3) or (C:\Users\<USER>\AppData\Local\conda) is added to PATH environment variable in Windows
• Create conda environment from env.yaml: conda env create -f env.yaml
• Activate environment: conda activate reflection_agent

2. .env file

• Create .env file in the project's root directory (based on .env-sample file). The content of .env is not stored in Git repository, because it contains secrets.

3. PyCharm settings

• Edit Run/Debug configurations in PyCharm and make sure to select the correct .env file and conda environment
• In PyCharm choose File -> Settings -> Python interpreter and select reflection_agent environment

4. Main File:

main.py - It's based on basic streamlit library so to run app you have to run command streamlit run main.py

5. Reflection Agent Algorithm

Pipeline is simple Reflection Agent where in first step we send Twitter post and then we boost this twitter in few iterations.

6. LangGraph Studio

In repo is langgraph.json which is compatible with LangGraph Studio: https://blog.langchain.dev/langgraph-studio-the-first-agent-ide/