Self‑correcting LangGraph Agent
This repository contains a small demo of a self‑correcting LangGraph agent. The agent receives a task string, executes it via an unreliable tool, then asks an LLM to judge the result. If the judge says the result is failed, the agent retries until it reaches a maximum number of attempts.
Features
- Unreliable tool – 30 % chance of raising an exception.
- LLM judge – forces the model to answer only
successorfailed. - Retry logic – automatically retries until success or a maximum number of attempts.
- LangGraph – low‑level graph with three nodes:
execute_task,verify_result,handle_error.
Setup
# Optional: create a virtual environment
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate
# Install dependencies
pip install -r requirements.txt
Running the Agent
python agent.py
You will be prompted to enter a task. The agent will then perform the task, verify the result, and retry if necessary. Example output:
Self‑correcting LangGraph agent demo
Enter a task: 2+2
--- Result ---
Task: 2+2
Attempts: 2
Status: success
Result: 22
Project Structure
agent.py– main implementation.README.md– this documentation.requirements.txt– Python dependencies.
Notes
- The LLM used is OpenAI's
gpt-4o-mini. If you prefer Ollama, change theChatOpenAIimport toChatOllamaand adjust the model name accordingly. - The unreliable tool is a toy example; replace it with a real tool for production use.