openai.api_key = os.getenv("OPENAI_API_KEY")
def generate_code(prompt): try: # Using a model capable of code generation response = openai.Completion.create( engine="gpt-3.5-turbo-instruct", # Or "code-davinci-002" if legacy access is enabled prompt=f"# Python code to prompt\n", max_tokens=100, temperature=0.5 ) return response.choices[0].text.strip() except openai.error.AuthenticationError: return "Error: Invalid API Key." except Exception as e: return f"Error: e"
if name == "main": user_prompt = "sort a list of dictionaries by value" print(f"Prompt: user_prompt") print("-" * 20) print(generate_code(user_prompt))
Run the script:
python test_codex.py
If successful, the model will output Python code that sorts a list of dictionaries.
sudo rm -f /usr/local/bin/codexini
sudo rm -rf /etc/codexini
To install and set up (often referred to as Codexini in early development or specific community contexts), you can choose between the standalone desktop application or the command-line interface (CLI). 1. Download the Codex App
The desktop app provides a visual "command center" for agentic coding. macOS (Apple Silicon): Download directly from the OpenAI Codex App page You can find the official app on the Microsoft Store codexini install
. For the best experience, it is recommended to run the app as an Administrator to grant the agent necessary permissions. OpenAI Developers 2. Install via Terminal (CLI) If you prefer working directly in your terminal, use to install the Codex CLI globally: npm install -g @openai/codex Use code with caution. Copied to clipboard Compatibility:
Supported on macOS and Linux. Windows users should ideally use Windows Subsystem for Linux (WSL) for the most stable environment. OpenAI Developers 3. Initial Configuration Once installed, follow these steps to link your account: Launch & Sign In: Open the app or run in your terminal. Sign in using your ChatGPT account OpenAI API key Select Workspace:
Choose a local folder or a Git repository where you want Codex to operate. Set Permissions:
If prompted, allow Codex access to your directory so it can create worktrees and execute tasks in parallel. 4. Verification
To ensure everything is working, try running your first task: In the CLI, navigate to your project folder and run: codex init Use code with caution. Copied to clipboard
Ask Codex to perform a simple task, like "List the files in this directory," to confirm the agent is responsive. OpenAI Developers like VS Code? Codex | AI Coding Partner from OpenAI openai
To install the Paper MCP Server for use with OpenAI Codex, follow these steps to connect your design system to your AI coding agent: 1. Install the Paper Desktop App
Before connecting to Codex, ensure you have the core application installed from the Paper Downloads page. 2. Add the Paper Plugin Open the Paper Desktop app. Navigate to the Code tab. Click Customize in the sidebar.
Under Personal Plugins, select Add plugin > Create plugin > Add marketplace. Paste paper-design/agent-plugins and submit.
In the directory dialog, go to the Code tab and add the Paper Desktop plugin. 3. Connect to the Codex App
Once Paper is configured, link it as an MCP (Model Context Protocol) server within the Codex application: Open the Codex app. Navigate to Settings > MCP Servers. Select Add custom MCP server. Choose the Streamable HTTP tab. Enter the following details: Name: paper URL: http://127.0.0.1:29979/mcp Click Save. 4. Verify the Connection
After saving, "Paper" should appear in your list of available MCPs within Codex. This allows Codex to access your design tokens and real content from tools like Figma and Notion. Run the script: python test_codex
Pro-tip: If you haven't installed the main coding agent yet, you can find the OpenAI Codex Desktop App on the Microsoft Store for Windows or via official OpenAI developer resources. docs /mcp Paper MCP Server - Paper – design
Here’s a blog-style post you can use or adapt for CodexIni install:
For manual installation on Windows:
A standard install is only the beginning. To leverage Codexini's full power, customize the config file.
Cause:
Fix:
# On Linux/macOS
export PATH="$HOME/.local/bin:$PATH"
For large monorepos (>100k files), the default settings may be slow. Optimize: