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Provides ultra-fast, typo-tolerant file search and grep tuned for Neovim and AI agents, with built-in memory (frecency, git status, size, definition matches). It reduces agent token use and speeds developer file discovery in large repos.
Indexes any repo into a knowledge graph of dependencies, call chains, and execution flows, then feeds it to AI coding agents via MCP so they stop missing context. Ships as a CLI plus a zero-install browser graph explorer with chat.
An MCP (Model Context Protocol) server that lets AI assistants interact with Xiaohongshu (RedNote): check login, publish image/text or video posts, search and fetch feed/details, and manage comments — exposes HTTP+MCP endpoints and integrates with MCP clients via local Docker or browser automation.
Provides an MCP server exposing 30+ trading tools — real-time prices, technical indicators, Bollinger Band scores, Reddit/news sentiment, and backtesting — designed to integrate with Claude/OpenClaw agents for automated market analysis.
Framework for building multi-modal AI agents that watch, listen, and reason over live video, pairing vision models (YOLO, Roboflow, Moondream) with LLMs like Gemini and OpenAI. Agents join calls in ~500ms and keep audio/video latency under 30ms.
Write repository automation as natural-language markdown that compiles into deterministic GitHub Actions workflows running AI agents. Agents run read-only by default and write only via sanitized safe-outputs. Works with Copilot, Claude, Codex, or Gemini.
Gives coding agents controlled access to a live Chrome browser through MCP, exposing DevTools-backed automation, debugging, screenshots, console inspection, network analysis, and tracing.
Declares and installs agent dependencies from an apm.yml manifest—skills, prompts, agents, plugins and MCP servers—with transitive resolution, security auditing, plugin packaging, and cross-host registries so agents are reproducible across repos.
Embeds a GUI agent in your web page as client-side JavaScript, letting users drive the interface with natural language — it reads the DOM as text (no screenshots) and performs clicks and form fills. Bring your own LLM; no extension or backend required.
Provides Gymnasium-style APIs and tooling to run isolated, networked execution environments for agentic reinforcement learning. Offers async/sync EnvClients, Docker/Kubernetes container providers, a web UI and CLI for scaffolding and deploying environments (Hugging Face Spaces); experimental and evolving.
Provides a Gymnasium-style API and tooling to create, deploy, and interact with isolated execution environments for agentic RL training. Includes async/sync clients, a web interface, CLI, Docker-based deployment, and Hugging Face Spaces integration.
Enforces a brainstorm → plan → test-driven → review workflow on AI coding agents instead of letting them jump straight to code. Ships as composable skills that auto-trigger by context and run across Claude Code, Cursor, Copilot CLI, Gemini and more.