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