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.
Provides 7×24 automated customer service for the Xianyu marketplace with multi-expert routing, context-aware dialogue, and a laddered bargaining system. Built in Python and designed to run against an LLM provider with browser-cookie integration for web interactions.
Connects AI coding agents (Cursor, Claude Code) to Figma through a WebSocket bridge, letting an agent read a design and edit it programmatically. Includes a Figma plugin and 40+ MCP tools for text, styling, components, and bulk edits.
Autonomously executes diverse biomedical research tasks by combining LLM reasoning, retrieval-augmented planning, and code-based execution. Includes a web UI and Gradio demo, a curated Know‑How library, MCP integration, and a biology-tailored reasoning model (Biomni‑R0).
Gives coding agents symbol-level codebase access via language servers (LSP), turning cross-file renames, reference lookups, and edits into precise operations instead of fragile text search. Runs as an MCP server spanning 40+ languages.
Collects 40+ importable n8n workflows from the AI Agents A-Z YouTube channel, each tied to one video episode — spanning content generation, social-media posting, and short-video and narrated-story pipelines, plus companion Docker MCP/REST servers.
Turns a raw idea, novel, or screenplay into a complete multi-shot video through a multi-agent pipeline that scripts, storyboards, and renders shots while a vision model checks character and scene consistency across the whole story.
Twelve engineering principles for building production-grade LLM agents, modeled on the 12-Factor App. Argues the best agents are mostly deterministic software with a few well-placed LLM calls, not a prompt-and-tools loop.
Generates full-stack web apps with the backend included — database, auth, file uploads, real-time UIs, and background workflows — by writing code against Convex's reactive APIs. A fork of bolt.diy; bring your key for Claude, GPT, Gemini, or Grok.
Builds a table-of-contents tree index over long PDFs and uses LLM tree search to fetch relevant sections — no embeddings, chunking, or vector database. Hits 98.7% on FinanceBench, for financial, legal, and technical docs where relevance needs reasoning.
Builds, evaluates, and deploys multi-agent systems in Python, code-first. A graph-based runtime handles routing, fan-out/fan-in, loops, retries, and human-in-the-loop; a Task API covers agent-to-agent delegation, plus a CLI and web UI.
Provides ready-to-use sample agents for Google’s Agent Development Kit across Python, TypeScript, Go, Java, Kotlin, and Android, from simple assistants to multi-agent workflows.