Codex Machine Learning Setup

agents.md

Codex-one: OpenAI Codex Machine Learning Setup

Introduction

The OpenAI Codex Machine Learning Setup is a comprehensive environment designed for building advanced AI-powered applications with a focus on agentic capabilities. This project provides a robust foundation for creating AI systems that can reason about complex tasks, interact with external tools, and execute actions on behalf of users.

Built around a core set of modern AI libraries and tools, this setup enables the development of sophisticated machine learning pipelines, particularly those leveraging Large Language Models (LLMs) for reasoning and decision-making. The project structure integrates seamlessly with FastMCP for standardized API interfaces and provides connectivity with various external services through tool integrations.

Core Libraries

AI & Orchestration

Library	Version	Purpose
DSPy	2.6.24	Declarative framework for building LLM-powered reasoning pipelines. DSPy enables structured programming of language model behavior, replacing brittle prompts with modular, maintainable code patterns.
FastMCP	0.3.1	Framework for creating standardized tool APIs via the Model Context Protocol (MCP). Serves as the interface layer between our AI systems and external clients or services.
ArcadePy	1.4.0	Python SDK for Arcade.dev, providing authenticated integrations to external services like GitHub, Slack, etc. Handles secure tool execution with proper credentialing.
OpenAI	1.61.0	Client library for interfacing with OpenAI's models. Used by DSPy for LLM-powered reasoning capabilities.
LiteLLM	1.60.4	Proxy routing service for LLMs, allowing DSPy to route calls to various model providers (OpenAI, Arcade, etc.).

Web Server & Routing

Library	Version	Purpose
FastAPI	0.111.0	Modern, high-performance web framework for building APIs. Used when exposing our agent as an HTTP service.
Uvicorn	0.29.0	ASGI server implementation, used to serve FastAPI applications with high performance.

HTTP & Utilities

Library	Version	Purpose
Requests	2.32.3	Standard HTTP client library for making direct API calls to external services.
HTTPX	0.27.0	Async-capable HTTP client that works well with FastAPI for non-blocking operations.

Auth & Security

Library	Version	Purpose
python-dotenv	1.0.1	Library for loading environment variables from a .env file, critical for securely managing API keys and credentials.

Testing & Quality Assurance

Library	Version	Purpose
pytest	8.2.0	Testing framework for writing and executing unit and integration tests.
pytest-mock	3.14.0	Extension for monkeypatching in tests, allowing controlled mocking of dependencies.
pytest-cov	5.0.0	Plugin for generating code coverage reports during test execution.
Black	24.4.2	Code formatter ensuring consistent styling across the codebase.
Flake8	7.0.0	Linter for enforcing coding standards and catching potential issues.
MyPy	1.10.0	Static type checker for Python, enhancing code quality and catching type-related errors.

Observability

Library	Version	Purpose
Loguru	0.7.2	Advanced logging library providing flexible and visually appealing logs.
Rich	13.7.1	Library for rich terminal output, enhancing the display of logs and debug information.

Installation

Prerequisites

• Python 3.9 or higher
• pip (Python package installer)
• Virtual environment tool (recommended: venv or conda)

Setup Steps

1. Clone the repository:

   git clone https://github.com/your-organization/codex-one.git
   cd codex-one

2. Create and activate a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

   pip install -r requirements.txt

4. Install the local MCP package for development:

   pip install -e ./mcp

5. Set up environment variables: Create a .env file in the project root with the following variables:

   OPENAI_API_KEY=your_openai_api_key
   ARCADE_API_KEY=your_arcade_api_key
   # Add other required API keys/credentials
   

Basic Usage

Creating a Simple DSPy Pipeline

import dspy
from dspy.retrieve import ColBERTv2

# Set up DSPy with OpenAI
llm = dspy.OpenAI(model="gpt-4o")
dspy.settings.configure(lm=llm)

# Define a simple QA module
class SimpleQA(dspy.Module):
    def __init__(self):
        super().__init__()
        self.generate_answer = dspy.ChainOfThought("question -> answer")

    def forward(self, question):
        return self.generate_answer(question=question)

# Use the module
qa = SimpleQA()
response = qa(question="How do neural networks learn?")
print(response.answer)

Setting Up a FastMCP Server

from fastapi import FastAPI
from dspy_mcp.server.app import MCPServer
from dspy_mcp.tools.echo import EchoTool

# Create a FastAPI app
app = FastAPI(title="Codex AI Service")

# Initialize the MCP Server
mcp_server = MCPServer()

# Register a simple tool
mcp_server.register_tool(EchoTool())

# Mount the MCP server on the FastAPI app
app.mount("/mcp", mcp_server.app)

# Run with: uvicorn server:app --reload

Using Arcade.dev Integration

import arcadepy as arcade
from dotenv import load_dotenv

# Load environment variables
load_dotenv()

# Initialize Arcade client
client = arcade.Client()

# Authenticate with Arcade
auth_info = client.auth.start(
    redirect_uri="http://localhost:3000/callback",
    scopes=["github", "slack"]
)

# After user authentication, use tools
github_tool = client.tools.github
issue = github_tool.create_issue(
    owner="your-org",
    repo="your-repo",
    title="New feature request",
    body="Implement AI-powered code review"
)

# Send notification via Slack
slack_tool = client.tools.slack
slack_tool.post_message(
    channel="#dev-team",
    text=f"Created GitHub issue: {issue.html_url}"
)

Combining DSPy with Tools

import dspy
from dspy_mcp.client.app import MCPClient

# Setup MCP client to connect to tools
mcp_client = MCPClient(base_url="http://localhost:8000/mcp")

# Register MCP tools with DSPy
tools = mcp_client.get_tools()
dspy.settings.configure(tools=tools)

# Create a ReAct agent that can use tools
agent = dspy.ReAct(tools=tools)

# Run the agent with a task
result = agent.forward(task="Create a GitHub issue about the login bug and notify the team on Slack")
print(result.answer)

Next Steps

• Explore the examples/ directory for more detailed usage scenarios
• Check the API documentation for comprehensive information about available modules and functions
• Join our community Discord for support and discussions
• Consider contributing to the project by submitting pull requests or raising issues

For more information on individual components:

• DSPy documentation: https://dspy.ai
• Arcade.dev documentation: https://docs.arcade.dev
• FastMCP documentation: See mcp/README.md

requirements.txt

# Core AI & Orchestration
dspy==2.6.24               # DSPy for declarative LLM workflows
fastmcp==0.3.1            # FastMCP for standardized tool API via MCP
arcadepy==1.4.0           # Arcade.dev Python SDK (if available; otherwise use HTTP)
openai==1.61.0            # For OpenAI-based LLMs (used by DSPy)

# Web Server & Routing (if using FastAPI for HTTP interface)
fastapi==0.111.0
uvicorn[standard]==0.29.0

# HTTP Requests & Utilities
requests==2.32.3          # For direct API calls (Slack, GitHub, etc.)
httpx==0.27.0             # Optional: async HTTP client, works well with FastAPI

# Auth & Security
python-dotenv==1.0.1      # Manage environment variables from .env file

# Testing & Mocking
pytest==8.2.0
pytest-mock==3.14.0       # For monkeypatching in tests
pytest-cov==5.0.0         # For code coverage reports

# Linting & Formatting
black==24.4.2
flake8==7.0.0
mypy==1.10.0              # Static typing (optional)

# Observability (Optional)
loguru==0.7.2             # Flexible logging
rich==13.7.1              # Pretty terminal output/logging

# Optional: LLM Proxy Routing (used internally by DSPy via litellm)
litellm==1.60.4           # For routing DSPy calls to Arcade, OpenAI, etc.

# Local package installation
-e ./mcp                  # Local dspy_mcp package

setup.sh

#!/usr/bin/env bash

# Exit immediately if a command exits with a non-zero status
set -e

# Define the name of the virtual environment directory
VENV_DIR=".venv"

# Define the path to the requirements file
REQUIREMENTS_FILE="requirements.txt"

# Function to display messages
function echo_msg() {
    echo -e "\n\033[1;32m$1\033[0m\n"
}

# Check if Python 3.11 or higher is installed
if ! python3 --version | grep -qE "Python 3\.(1[1-9]|[2-9][0-9])"; then
    echo_msg "Error: Python 3.11 or higher is required."
    exit 1
fi

# Create virtual environment if it doesn't exist
if [ ! -d "$VENV_DIR" ]; then
    echo_msg "Creating virtual environment in $VENV_DIR..."
    python3 -m venv "$VENV_DIR"
else
    echo_msg "Virtual environment already exists in $VENV_DIR."
fi

# Activate the virtual environment
source "$VENV_DIR/bin/activate"

# Upgrade pip to the latest version
echo_msg "Upgrading pip..."
pip install --upgrade pip

# Install required packages from requirements.txt
if [ -f "$REQUIREMENTS_FILE" ]; then
    echo_msg "Installing packages from $REQUIREMENTS_FILE..."
    pip install -r "$REQUIREMENTS_FILE"
else
    echo_msg "Error: $REQUIREMENTS_FILE not found."
    deactivate
    exit 1
fi

# Display success message
echo_msg "Setup complete. Virtual environment is ready."

# Deactivate the virtual environment
deactivate