OpenAI Codex CLI for Teams Training Course

Introduction to OpenAI Codex CLI

• Defining Codex CLI and its 2025 open-source Rust architecture.
• Key features: prompt handling, file operations, bash execution, and multi-step tasks.
• Comparison with Claude Code and other terminal-based 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 providers.
• Connecting to local backends using 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 driven by 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.
• Sandboxing and the distinction between read-only and write-enabled sessions.
• Safely handling destructive commands and file deletions.

Git and CI Integration

• Generating commits and diffs using Codex CLI.
• Implementing pre-commit hooks with agent-based 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 via custom MCP endpoints.
• Building internal MCP tools for proprietary systems.

Multi-Backend Support

• Switching between OpenAI, Gemini, and GitHub Models APIs.
• Local inference using Ollama and self-hosted endpoints.
• Strategies for model selection balancing latency and quality.

Team Deployment and Governance

• Managing shared configuration and secrets.
• Implementing usage policies and audit logging for enterprises.
• Establishing standardized team prompts and guardrails.

Custom Prompts and Workflows

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

Performance Tuning

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

Troubleshooting Common Issues

• Resolving connection failures to backends.
• Debugging 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.
• Considering data residency and compliance requirements.

Summary and Next Steps

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