OpenAI Codex CLI for Teams Training Course

Introduction to OpenAI Codex CLI

• Overview of what Codex CLI is and its 2025 open-source Rust architecture.
• Core features: handling prompts, file operations, bash execution, and multi-step tasks.
• Comparison with Claude Code and other terminal agents.
• Understanding approval modes and security boundaries.

Installation and Setup

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

Core Workflow Commands

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

Approval Modes and Safety

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

Git and CI Integration

• Utilizing Codex CLI to generate commits and diffs.
• Implementing pre-commit hooks with agent-based code review.
• 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 using 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.
• Strategies for selecting models based on latency versus quality requirements.

Team Deployment and Governance

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

Custom Prompts and Workflows

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

Performance Tuning

• Understanding the performance characteristics of Rust.
• Optimizing token usage for large-scale projects.
• Managing caching and session state.

Troubleshooting Common Issues

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

Security Best Practices

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

Summary and Next Steps

• Recapping core capabilities and workflows.
• Exploring community resources and opportunities for open-source contributions.
• Transitioning to advanced topics in multi-agent orchestration.