Self-hostable chat client that unifies many LLM providers (OpenAI, Claude, Gemini, Ollama, DeepSeek) behind one UI. Adds file-upload knowledge-base RAG, vision/TTS, an MCP plugin system, and an agent marketplace, with one-click Vercel or Docker deployment.
Calls 100+ LLM providers — OpenAI, Anthropic, Gemini, Bedrock, Azure — through one OpenAI-compatible API, as a Python SDK or self-hosted proxy. The proxy adds virtual keys, spend tracking, rate limits, and load balancing across models and providers.
Framework for unit-testing, evaluating and benchmarking LLM systems with ready-made metrics (G‑Eval, hallucination, task completion), support for local judge models and synthetic datasets, plus CI-friendly integrations for LangChain/OpenAI/Anthropic.
Coordinates multiple LLM agents that converse to solve a task, splitting work across customizable roles that call tools, run code, and loop in humans. The v0.4 redesign adds async messaging and Python/.NET interoperability across distributed networks.
Agent framework for building tool-using applications on Qwen 3+ LLMs. Provides function calling, MCP, a Dockerized code interpreter, and RAG over documents up to 1M tokens; powers the Qwen Chat backend and a Chrome browser-assistant extension.
AI Engineering Hub is a comprehensive GitHub repository offering in-depth tutorials and 93+ production-ready projects on LLMs, RAGs, AI agents, and real-world AI applications for all skill levels.
Turns any website into structured data or an API without code: record clicks once to capture lists and tables, or describe fields in plain language for AI extraction. Also crawls full sites, scrapes pages to Markdown, and runs filtered searches.
Exposes FreeCAD over the Model Context Protocol so AI assistants (e.g., Claude Desktop) can create/edit CAD documents, run FEM, execute Python inside FreeCAD, and capture screenshots. Provided as a Python MCP server plus a FreeCAD addon with local/remote modes and optional text-only output.
Orchestrates teams of role-based autonomous agents that collaborate on multi-step tasks, plus event-driven Flows for deterministic control. Built from scratch with no LangChain dependency; runs 450M+ agentic workflows monthly.
Python framework for building and serving LLM agents in production: a unified event bus for real-time frontends and human-in-the-loop, fine-grained tool permissions, multi-tenant serving, and tool/code execution sandboxed via Docker or E2B.
Memory engine that lets AI apps remember users across conversations: it extracts facts, tracks updates, resolves contradictions, and auto-forgets stale info, returning context in ~50ms. Tops the LongMemEval, LoCoMo and ConvoMem memory benchmarks.
Automates browser workflows using LLMs and computer vision instead of XPath selectors, so it works on unseen sites and survives layout changes. Drive tasks with natural-language prompts: act, extract, validate. Handles 2FA and multi-step flows.