What output formats are supported for automation?

In addition to a human-readable text format with status markers, it supports JSONL (JSON Lines) for easy programmatic parsing and integration with other tools.

Does Memex CLI support parallel execution?

By default, tasks without specified dependencies run in parallel to save time, while tasks with dependencies follow a sequential DAG order.

What is Memex CLI's primary function?

Memex CLI is a wrapper for AI backends that uses a stdin protocol to define and execute tasks with built-in memory and resume support.

Can I use multiple AI models in a single workflow?

Yes, Memex CLI allows you to specify different backends like Claude for design and Codex for implementation within the same task file.

How does the 'resume' feature benefit developers?

It allows you to continue a previous AI session using a Run ID, ensuring that all previous task outputs, conversation history, and file changes are preserved for the next step.

Memex CLI AI Orchestrator

Name: Memex CLI AI Orchestrator
Author: chaorenex1

bychaorenex1

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Productivity & Workflow

Executes multi-backend AI tasks with persistent memory and resume capabilities for seamless coding workflows.

About

Memex CLI is a powerful orchestration tool that allows developers to run complex AI tasks across multiple backends, including Codex, Claude, and Gemini, using a standardized stdin protocol. It solves the problem of context loss in long-running AI workflows by providing a 'resume' functionality that preserves conversation history and file states. With support for parallel task execution and Directed Acyclic Graph (DAG) dependencies, Memex CLI transforms Claude Code into a robust automation hub for code generation, system design, and multimodal analysis.

Key Features

Stateful task resumption with full context and history preservation
Structured output options including human-readable text and machine-ready JSONL
Parallel and dependency-based (DAG) task orchestration
1 GitHub stars
Multi-backend support for Anthropic Claude, OpenAI Codex, and Google Gemini
Flexible file loading with glob patterns and intelligent embedding modes

Use Cases

Orchestrating complex system design tasks that involve different AI models for architecture and implementation
Running batch UI/UX reviews using Gemini's multimodal capabilities within a CLI workflow
Automating multi-step code refactoring projects that require persistent state across sessions

About

Key Features

Stateful task resumption with full context and history preservation
Structured output options including human-readable text and machine-ready JSONL
Parallel and dependency-based (DAG) task orchestration
1 GitHub stars
Multi-backend support for Anthropic Claude, OpenAI Codex, and Google Gemini
Flexible file loading with glob patterns and intelligent embedding modes

Use Cases

Orchestrating complex system design tasks that involve different AI models for architecture and implementation
Running batch UI/UX reviews using Gemini's multimodal capabilities within a CLI workflow
Automating multi-step code refactoring projects that require persistent state across sessions