Wrangle: A Native macOS Editor for Managing Claude Code Sessions
Wrangle is a native macOS markdown editor designed specifically for managing Claude Code sessions and multi-agent workflows. The tool was created by a developer who found that VS Code couldn't handle their daily workload of running multiple Claude Code sessions.
Key Features
- Native macOS application for managing Claude Code sessions
- Markdown file editing with context awareness
- Embedded terminals
- Smart notifications that pull users back to the right session
- Built specifically for AI power users working with multi-agent workflows
Availability
The tool is available at wrangleapp.dev. A limited number of free keys are available using code U4MTIXMG at checkout.
The developer notes that they built the application with Claude's assistance and has provided a full write-up in the Reddit comments section.
📖 Read the full source: r/ClaudeAI
👀 See Also
Open-source Agent OS: Rust-based OS for AI agents with WASM sandboxing and Hands feature
An open-source operating system for AI agents has been released with 137k lines of Rust code under MIT license. The system runs agents in WASM sandboxes with 16 security layers and introduces 'Hands' for scheduled, autonomous agent operation.
Aired: A Claude Code Skill for Instant HTML Publishing to Live URLs
Aired is an open-source tool that publishes HTML to a live URL in 2 seconds via Claude Code skills or MCP servers. It requires no signup, deployment configuration, or installation for web-based AI tools, and works with Claude Code, Cursor, VS Code, Codex, and Windsurf.
Coding-Flashcards: 800+ Anki cards for Rust, SQLite, Godot, and Wolfram Language
Over 800 markdown flashcards covering Rust, SQLite, Godot, and Wolfram Language from first principles, with scripts to convert to Anki decks or PDFs.
Engram Memory SDK: Graph-Based Memory for AI Agents with Local Models
Engram Memory SDK is an open-source graph memory system for AI agents that works with local models via LiteLLM. It requires only one LLM call for ingestion, then uses vector search and graph traversal for recall with zero ongoing LLM costs.