Turns any website into clean markdown, structured JSON, or screenshots through a single API — handling JavaScript rendering, rotating proxies, rate limits, and full-site crawling so LLM apps get web data without running scraping infrastructure.
React components for building LLM chat and agent interfaces: message bubbles, prompt sets, conversation lists, and sender inputs under a RICH interaction paradigm, plus a streaming Markdown renderer and hooks for wiring UI to model data streams.
Splits autonomous R&D into two cooperating agents: one proposes hypotheses, the other writes and tests code — iterating on quant-finance factors, Kaggle pipelines, and model research. Hits a ~30% medal rate on MLE-Bench, nearly double AIDE's.
Orchestrates and coordinates multiple coding agents in a shared, cross-device workspace — preserving context, files, and durable artifacts so teams can run parallel agent workflows without losing intent or state.
Combines drag-and-drop field binding with natural-language prompts so an AI agent derives the transformations behind charts your raw tables can't produce. Reads from databases, files, images, and websites; 30+ chart types and branchable threads.
Continuously records your screen and audio 24/7 to a local, searchable timeline you can query in natural language. Stores screenshots with accessibility data in SQLite, and a plugin system runs scheduled AI agents on what it captures.
Provides a Python framework for building generative-AI agents and workflows with Pydantic-style type safety and composable capabilities. Model-agnostic provider support, built-in observability, human-in-the-loop tool approval, and durable execution for production use cases.
Crawls 30+ social platforms (Weibo, Xiaohongshu, Douyin), parses their video and image content, then has five specialized agents debate in a moderated forum to synthesize public-opinion reports. Can fuse public sentiment with a private business database.
Multi-tenant agent harness that makes enterprise knowledge retrievable, graph-reasonable, and deliverable by LLM-powered agents. Integrates RAG + a Milvus-based knowledge graph, LangGraph orchestration, and document parsing for citation-backed answers and graph reasoning; deployable via Docker (requires a compatible LLM API).
Edits a codebase from natural-language prompts in the terminal, coordinating specialized sub-agents — file picker, planner, editor, reviewer — instead of one model. Beats Claude Code 61% vs 53% on its own evals; agents scriptable in TypeScript.
Packs a Git repository into a single AI-friendly file for easy ingestion by LLMs. Offers per-file and total token counts, optional Tree-sitter compression, secret scanning, and multiple interfaces (CLI, web, browser extension, Docker, MCP) for AI-driven code review and analysis.
Routes each user query to the most suitable agent via a classifier that weighs agent profiles and conversation history, keeping context shared across handoffs. Python and TypeScript, with a SupervisorAgent that runs sub-agents in parallel.