Build your own AI assistant (a "Jarvis")
A personal AI assistant — your own Jarvis — isn't magic or out of reach. It's a few parts working together: a brain, some tools, a memory, and (optionally) a voice. Here's the whole picture in plain English, and how to start.
Inspired by open research like Stanford SAIL's OpenJarvis — a local-first, open-source framework. This guide is our own; the links point you to the real projects.
The 4 parts of any AI assistant
Every assistant — from a weekend project to a research lab's — is some combination of: a brain (a model that thinks), tools (so it can actually do things), a memory (so it remembers you), and an interface (how you talk to it). Build those four and you have a Jarvis.
Step 1. Pick its brain (the model)
Choose what does the thinking. Two paths: run an
open model locally with
Ollama (private, free, offline — the local-first approach OpenJarvis champions), or call a hosted model via an
API (e.g., Claude) for more power. Many assistants use a small local model for everyday tasks and a stronger API model for hard ones.
Step 2. Give it tools (so it can do things)
A brain that can only chat is a toy. Give it tools — read files, search the web, check your calendar, send an email — using
function calling or the open
Model Context Protocol (MCP), which is a standard way to safely connect an assistant to your tools and data.
Step 3. Give it a memory
Store context the assistant can pull from: your notes, preferences, past chats, and project files. Simple version: a folder of text/markdown files it reads. Fancier: a vector database for semantic search. The point is continuity — it should know you, not start from zero every time.
Step 4. Give it a voice & a face (optional)
Add an interface. For voice, turn speech into text with
Whisper and add text-to-speech for replies. For a screen, a simple web UI or a menu-bar app works. Start with text — add voice once the brain and tools work.
Step 5. Keep it private & local-first
The best personal assistants keep your data on your device. Running the model locally (Ollama) means your notes and conversations never leave your machine — the core idea behind local-first frameworks like OpenJarvis. Only reach out to the cloud when you choose to.
Step 6. Automate it
Now make it proactive: scheduled tasks (a morning digest), triggers (when an email arrives, draft a reply), and routines. This is where an assistant stops being a chatbot and starts being yours.
Don't reinvent the wheel
You don't have to build all of this from scratch. Stand on open-source shoulders: study and build on frameworks like Stanford's OpenJarvis, the Vercel AI SDK for the app layer, and the tools in our curated toolkit. Respect their licenses, keep your build private and honest, and use it responsibly (see Our Stance).
Prompts to power your assistant
The right prompts and context turn any model into a sharp assistant.
Try free prompts →