OpenAI Codex CLI for Teams Training Course

Introduction to OpenAI Codex CLI

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

Installation and Setup

• Installing Codex CLI on macOS and Linux.
• 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 multi-turn sessions.
• File read, write, and edit operations via prompts.
• Executing shell commands and managing piped outputs.
• Managing working directories and project context.

Approval Modes and Safety

• Configuring automatic, ask-before-execute, and fully manual modes.
• Sandboxing: read-only versus 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 review.
• Running Codex CLI in headless CI environments.
• Integration with GitHub Actions and GitLab CI.

MCP Server Integration

• Connecting to Model Context Protocol servers.
• Extending tool capabilities with 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.
• Model selection strategies balancing latency versus quality.

Team Deployment and Governance

• Shared configuration and secrets management.
• Usage policies and audit logging for enterprise environments.
• Establishing 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 Rust performance characteristics.
• Optimizing token usage for large projects.
• Caching and session state management.

Troubleshooting Common Issues

• Resolving connection failures to backends.
• Debugging prompt ambiguity and misinterpretations.
• Handling rate limiting and retry strategies.

Security Best Practices

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

Summary and Next Steps

• Recap of core capabilities and workflows.
• Community resources and open-source contributions.
• Transitioning to advanced multi-agent orchestration topics.