memex-cli

Cross-backend AI task orchestration with memory and resume support allows you to execute tasks across Codex, Claude, and Gemini while maintaining persistent context. It uses a deterministic stdin protocol to define tasks and stream results for repeatable runs.

Yes, you can resume interrupted AI workflows without losing previous context. The system provides memory and resume support, maintaining persistent context across executions so you can continue design, coding, testing, and deployment tasks seamlessly.

The best way to execute independent AI tasks in parallel is through DAG orchestration. This allows you to run independent tasks simultaneously or coordinate them via dependencies across Codex, Claude, and Gemini, with streaming outputs and robust error handling.

You load file context for context-aware AI task automation by using the built-in file context loading feature. This integrates file data directly into the task execution stream, providing real-time progress and context-aware processing for your AI workflows.

Yes, multi-backend AI orchestration works with structured task definitions sent via a deterministic stdin protocol. This allows you to define repeatable runs, select specific models, and manage dependencies across Codex, Claude, and Gemini with streaming output.

Limitations of coordinating AI tasks across multiple backends include potential error handling overhead and dependency on external API availability for Codex, Claude, and Gemini. While robust error handling is implemented, complex DAG orchestration may require careful dependency management to avoid execution bottlenecks.