Feeds simplified Figma layout and style metadata to AI coding agents like Cursor and Claude Code to implement designs in one shot. Sends descriptive JSON (1px border, 16px padding) rather than code, leaving framework choices to the model.
Desktop AI client that unifies cloud and local LLMs, tool calling (MCP), installable Skills, and ACP agent integration into a single multi-window workspace. Supports local Ollama models, multi-provider configuration, remote control, and privacy-focused local storage.
Curates 80+ hands-on LLM-powered examples, tutorials and recipes for building agents, RAG systems, voice assistants, and agentic workflows. Includes starter templates, course playlists, and reference apps for rapid prototyping and learning.
Distributes one post across 14+ platforms (Douyin, Xiaohongshu, TikTok, X), automates likes and replies via a browser plugin, and matches creators to paid brand tasks settled by sales, views, or engagement. Drivable from Claude/Cursor via MCP.
Coordinates role-playing agents to automate real-world tasks — web search and browsing, code execution, document parsing, and multimodal handling. Built on the CAMEL-AI framework; scored 69.09% on the GAIA benchmark, topping open-source frameworks.
Lets AI agents drive GitHub in natural language via MCP: browse repos, triage issues, review pull requests, and trigger Actions runs. Runs as a GitHub-hosted remote OAuth server or a local Go binary, with per-toolset scoping and a read-only mode.
Framework-agnostic library for connecting and optimizing teams of AI agents built in LangChain, LlamaIndex, CrewAI, Semantic Kernel, or Google ADK. Profiles them down to individual tokens, traces execution, and runs built-in evaluation.
Runs an MCP server that lets an LLM like Claude drive Blender directly: create and edit objects, apply materials, inspect scenes, and run Python. Pulls assets from Sketchfab, Poly Haven, and Hyper3D so prompts build editable 3D scenes.
Lets AI agents like Claude Desktop and Cursor explore schemas and run SQL across Postgres, MySQL, MariaDB, SQL Server, and SQLite through one MCP server. A read-only mode stops the agent mutating data; no per-database drivers to wire up.
Exposes xcodebuild, simulator, and device actions as Model Context Protocol tools, so AI agents can build, run, capture logs, and debug iOS and macOS apps without hand-written scripts. Also runs as a standalone CLI and plugs into MCP clients.
Provider-agnostic framework for orchestrating multi-agent LLM workflows in Python: agents that delegate via handoffs, function/MCP/hosted tools, input/output guardrails, automatic session memory, and a visual tracing UI for debugging runs.