Meet AgentGPT, your personal AI sidekick that’s more ambitious than your last New Year’s resolution! Ever wanted a digital buddy who doesn’t just sit around binge-watching cat videos? With AgentGPT, you can name your own AI and send it on a quest to conquer the universe—or at least your to-do list! This little genius will brainstorm tasks, execute them with the precision of a caffeinated squirrel, and learn from its triumphs and failures (hopefully fewer of the latter). Imagine your AI trying to figure out how to make the perfect cup of coffee while simultaneously planning your next vacation. It’s like having a personal assistant who never sleeps, never eats, and definitely doesn’t judge you for that third slice of pizza. So, if you’re ready to unleash an autonomous agent that’s smarter than your average bear and more motivated than your last gym membership, hop on the AgentGPT train and watch your goals take flight! 🚀

A self-hosted "second brain": Khoj indexes your own files and answers questions from them, parsing Markdown (whole Obsidian vaults included), org-mode, PDF, Word, plain text, Notion pages, GitHub repositories, and images described by a vision model, then embedding everything with sentence-transformers into a vector index for semantic search and RAG with cited sources. Any LLM backend works: local models like Llama, Qwen, or Mistral via Ollama, or cloud models like GPT, Claude, and Gemini. You can build custom agents, each with its own persona, scoped knowledge base, chat model, and tools such as web search and code execution. Scheduled automations run recurring research and deliver newsletters or notifications to your inbox, and research mode performs multi-hop web searches with inline citations. Access it from a browser, the Obsidian plugin, Emacs, desktop, or WhatsApp - all clients connect to the same self-hosted instance, making Khoj one of the few AI assistants Emacs users can point at decades of org files. Semantic search means recall works without exact keywords: "that paper about forecasting with transformers" surfaces the right PDF even when you cannot remember its title. Switching LLM backends never requires re-indexing your documents, and with a local model via Ollama, even inference stays on hardware you control - journals, research, and private notes are never sent anywhere. Python/FastAPI stack, AGPL-licensed, with PostgreSQL storage.