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+# 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.