Ralph Orchestrator implements the simple but effective "Ralph Wiggum" technique for autonomous task completion, continuously running an AI agent against a prompt file until the task is marked as complete or limits are reached. This implementation provides a robust, well-tested, and feature-complete orchestration system for AI-driven development. Also cited in the Anthropic Ralph plugin documentation.
Browserbase Open Operator: an open-source Operator-style web agent built on Stagehand; demonstrates full task decomposition, action planning, and evidence collection using the Browserbase cloud.
A development environment for Claude Code with a spec-driven workflow, TDD enforcement, cross-session memory, semantic search, quality hooks and modular rules. Large, with wide coverage.
Notte open-source web agent environment. It converts browser sessions into a Markov Decision Process with structured observation/action spaces, making browsers first-class RL and LLM agent environments.
Long-term memory for agents: tools that extract what matters from conversations, refine prompts from feedback and keep memory across sessions, with LangGraph's store built in.
E2B Desktop Sandbox: a cloud virtual desktop (Ubuntu + VNC) with Python SDK for screenshot, mouse, keyboard, and process control; designed for AI agents that need a full GUI environment in an isolated VM.
Hand-crafted collection of advanced context engineering techniques and patterns with minimal token footprint focused on improving agent result quality.
Emergence AI agent-E browser agent: hierarchical LLM-based web automation that uses DOM distillation and action abstraction layers to achieve significantly higher benchmark accuracy than prior browser agents.
Enables users to prompt codex from claude code. Unlike the raw codex mcp server, this skill infers parameters such as model, reasoning effort, sandboxing from your prompt or asks you to specify them. It also simplifies continuing prior codex sessions so that codex can continue with the prior context.
Research browser agent from Zhejiang University and HKU. It uses GPT-4V interleaved screenshot + HTML observations to complete open-ended web tasks; established an early web-agent benchmark (WebVoyager).