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.
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.
Trains multi-step LLM agents with reinforcement learning (GRPO) on your own tasks, wrapping existing agent code behind an OpenAI-compatible client. Its RULER mode scores trajectories with an LLM judge, so there's no reward function to hand-write.
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).
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.
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.