OpenAI Codex CLI for Teams Training Course

Introduction to OpenAI Codex CLI

• Understanding Codex CLI and its 2025 open-source Rust architecture
• Key features: prompt handling, file operations, bash execution, and multi-step task management
• Comparative analysis with Claude Code and other terminal agents
• Overview of approval modes and security boundaries

Installation and Setup

• Installing Codex CLI on macOS and Linux systems
• Configuring API keys for OpenAI and compatible service providers
• Connecting to local backends via Ollama and Atomic Chat
• Setting up SSH and remote development environments

Core Workflow Commands

• Executing single prompts and managing multi-turn sessions
• Performing file read, write, and edit operations through prompts
• Running shell commands and handling piped outputs
• Managing working directories and project context

Approval Modes and Safety

• Configuring automatic, ask-before-execute, and fully manual modes
• Implementing sandboxing and distinguishing between read-only and write-enabled sessions
• Safely handling destructive commands and file deletions

Git and CI Integration

• Using Codex CLI to generate commits and diffs
• Implementing pre-commit hooks with agent-based reviews
• Running Codex CLI in headless CI environments
• Integrating with GitHub Actions and GitLab CI

MCP Server Integration

• Connecting to Model Context Protocol servers
• Extending tool capabilities via custom MCP endpoints
• Developing internal MCP tools for proprietary systems

Multi-Backend Support

• Switching between OpenAI, Gemini, and GitHub Models APIs
• Performing local inference with Ollama and self-hosted endpoints
• Adopting model selection strategies to balance latency versus quality

Team Deployment and Governance

• Managing shared configurations and secrets
• Establishing usage policies and audit logging for enterprise compliance
• Setting up standardized team prompts and security guardrails

Custom Prompts and Workflows

• Creating reusable prompt templates
• Chaining tasks for complex refactoring projects
• Batch processing multiple files and repositories

Performance Tuning

• Understanding Rust performance characteristics
• Optimizing token usage for large-scale projects
• Implementing caching and session state management strategies

Troubleshooting Common Issues

• Resolving connection failures to backend services
• Debugging prompt ambiguity and misinterpretations
• Handling rate limiting and implementing retry strategies

Security Best Practices

• Protecting API keys in shared development environments
• Preventing prompt injection and command hijacking
• Addressing data residency and compliance requirements

Summary and Next Steps

• Recap of core capabilities and established workflows
• Exploring community resources and open-source contribution opportunities
• Bridging to advanced multi-agent orchestration topics