# LangGraph CLI Agent

## Overview
This repository contains a simple command‑line agent built with **LangGraph** and **LangChain OpenAI**. The agent:

1. **Plans** a user‑supplied task into 3‑6 discrete steps.
2. **Executes** each step sequentially, collecting results.
3. **Summarises** the outcome after all steps are finished.

The agent runs locally and can use either an OpenAI API key or a local Ollama model.

## Installation
```bash
# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate    # On Windows use `venv\Scripts\activate`

# Install dependencies
pip install -r requirements.txt
```

The `requirements.txt` file contains:
```text
langgraph>=1.0.0
langchain-openai>=0.2.0
langchain-ollama>=0.1.0
```

## Configuration
The agent automatically chooses an LLM provider:

| Environment Variable | Meaning | Default |
|----------------------|---------|---------|
| `OPENAI_API_KEY` | OpenAI API key | *None* |
| `CHAT_MODEL` | Model name for local Ollama | `llama3` |
| `OLLAMA_BASE_URL` | Base URL for local Ollama | `http://localhost:11434/v1` |

If `OPENAI_API_KEY` is set, the agent uses the OpenAI endpoint. Otherwise it falls back to the local Ollama model.

## Usage
```bash
python agent.py "Compare Python and JavaScript"
```

Typical output:
```
===== PLAN =====
1. Compare the syntax of Python and JavaScript.
2. Discuss performance considerations.
3. Evaluate ecosystem support.
4. Summarise key differences.

===== RESULTS =====
[Step 1] Python uses indentation for blocks, whereas JavaScript uses braces.
[Step 2] JavaScript is generally faster in the browser, but Python excels in data science.
[Step 3] Python has a richer scientific stack; JavaScript dominates web development.
[Step 4] Python is best for backend and data work; JavaScript is essential for frontend.

===== SUMMARY =====
Python offers strong support for scientific computing and backend tasks, while JavaScript remains the de‑facto language for web development. Both languages have robust ecosystems, but their strengths differ.
```

## Development
- **Graph**: The graph is defined in `agent.py` using `StateGraph`. The state is a `TypedDict` named `PlanningState`.
- **Error handling**: All LLM calls are wrapped in try/except blocks. Parsing errors raise descriptive exceptions.
- **Testing**: The project includes minimal unit tests for plan parsing and integration tests that run the graph on a sample task.

Feel free to extend the graph with more sophisticated nodes or to plug in different LLMs.