69 lines
2.5 KiB
Markdown
69 lines
2.5 KiB
Markdown
# LangGraph CLI Agent
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## Overview
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This repository contains a simple command‑line agent built with **LangGraph** and **LangChain OpenAI**. The agent:
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1. **Plans** a user‑supplied task into 3‑6 discrete steps.
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2. **Executes** each step sequentially, collecting results.
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3. **Summarises** the outcome after all steps are finished.
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The agent runs locally and can use either an OpenAI API key or a local Ollama model.
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## Installation
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```bash
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# Create a virtual environment (recommended)
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python -m venv venv
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source venv/bin/activate # On Windows use `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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The `requirements.txt` file contains:
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```text
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langgraph>=1.0.0
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langchain-openai>=0.2.0
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langchain-ollama>=0.1.0
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```
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## Configuration
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The agent automatically chooses an LLM provider:
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| Environment Variable | Meaning | Default |
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|----------------------|---------|---------|
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| `OPENAI_API_KEY` | OpenAI API key | *None* |
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| `CHAT_MODEL` | Model name for local Ollama | `llama3` |
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| `OLLAMA_BASE_URL` | Base URL for local Ollama | `http://localhost:11434/v1` |
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If `OPENAI_API_KEY` is set, the agent uses the OpenAI endpoint. Otherwise it falls back to the local Ollama model.
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## Usage
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```bash
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python agent.py "Compare Python and JavaScript"
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```
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Typical output:
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```
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===== PLAN =====
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1. Compare the syntax of Python and JavaScript.
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2. Discuss performance considerations.
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3. Evaluate ecosystem support.
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4. Summarise key differences.
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===== RESULTS =====
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[Step 1] Python uses indentation for blocks, whereas JavaScript uses braces.
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[Step 2] JavaScript is generally faster in the browser, but Python excels in data science.
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[Step 3] Python has a richer scientific stack; JavaScript dominates web development.
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[Step 4] Python is best for backend and data work; JavaScript is essential for frontend.
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===== SUMMARY =====
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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.
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```
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## Development
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- **Graph**: The graph is defined in `agent.py` using `StateGraph`. The state is a `TypedDict` named `PlanningState`.
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- **Error handling**: All LLM calls are wrapped in try/except blocks. Parsing errors raise descriptive exceptions.
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- **Testing**: The project includes minimal unit tests for plan parsing and integration tests that run the graph on a sample task.
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Feel free to extend the graph with more sophisticated nodes or to plug in different LLMs.
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