How to Build a Private, On-Device AI Agent with Stanford's OpenJarvis

Stanford researchers just released OpenJarvis — a local-first framework for building AI agents that run entirely on-device, with no cloud calls required. Tool use, persistent memory, and online learning. All on your hardware, completely private.

For anyone who’s been waiting for a serious open-source alternative to cloud-hosted agent frameworks for privacy-sensitive applications — healthcare, legal work, personal data processing, enterprise environments with air-gap requirements — this is worth a close look.

Here’s how to get started.

What OpenJarvis Actually Does

Before diving into setup, it’s worth understanding what’s distinct here:

• Tool use — agents can call local tools (file operations, web browsing via local browser, code execution, search) without routing through a vendor API
• Persistent memory — the agent maintains a knowledge store that persists between sessions, builds context over time, and can recall prior interactions
• Online learning — the framework supports updating the agent’s behavior based on feedback and experience, without retraining a base model
• Fully local — all components run on consumer hardware; no API keys, no data leaving your machine

The architecture targets modern consumer hardware (Apple Silicon, recent NVIDIA GPUs) and is designed to be deployable without a data science background.

Prerequisites

Before installing OpenJarvis, you’ll need:

• Python 3.11 or higher
• At least 16GB RAM (32GB recommended for larger models)
• 20–40GB free disk space (model weights)
• A supported backend: llama.cpp, ollama, or a local HuggingFace model

For Apple Silicon (M2/M3/M4):

# Install via Homebrew
brew install [email protected] git

For Linux (Ubuntu/Debian):

sudo apt update
sudo apt install python3.11 python3.11-venv git

Installation

# Clone the OpenJarvis repository
git clone https://github.com/stanford-oval/openjarvis
cd openjarvis

# Create and activate a virtual environment
python3.11 -m venv .venv
source .venv/bin/activate

# Install dependencies
pip install -e ".[all]"

Setting Up Your Base Model

OpenJarvis works with any GGUF-format model through llama.cpp, or any Ollama-compatible model. For a solid starting point:

# Option 1: Via Ollama (easiest)
ollama pull llama3.2:3b          # Fast, lightweight
ollama pull mistral:7b           # Better reasoning
ollama pull qwen2.5:14b          # Strong tool use

# Option 2: Direct GGUF download (advanced)
# Download a quantized model to ~/models/
# e.g., Mistral-7B-Instruct-v0.3.Q4_K_M.gguf

Configure your model in config.yaml:

model:
  backend: ollama          # or: llama_cpp, huggingface
  model_name: mistral:7b
  context_length: 32768

agent:
  memory_enabled: true
  tool_use: true
  learning: false          # Set true to enable online learning

Creating Your First Agent

OpenJarvis uses a declarative agent definition format. Create my_agent.yaml:

name: my_private_assistant
description: A privacy-first local AI assistant

tools:
  - file_system        # Read/write local files
  - web_browser        # Controlled local browsing
  - code_executor      # Python/bash execution
  - calendar           # Local calendar access
  - notes              # Persistent notes/knowledge base

memory:
  type: vector_store
  path: ~/.openjarvis/memory/my_assistant
  max_entries: 50000

system_prompt: |
  You are a private AI assistant running entirely on this device.
  You have no internet access except through the web_browser tool.
  All data stays local. Be helpful, concise, and privacy-conscious.

Launch the agent:

openjarvis run --config my_agent.yaml

Using Persistent Memory Effectively

The memory system is where OpenJarvis gets genuinely useful. Unlike chat-context memory (which resets between sessions), OpenJarvis memory persists and builds:

# In an agent interaction, explicitly store important facts
"Remember that my project deadline is April 15th and the client prefers weekly status emails"

# In later sessions, the agent recalls stored context
"What's the deadline for my project?" 
# → Agent retrieves from memory store, not from current conversation

You can also seed the memory store with documents:

# Index a folder of documents into agent memory
openjarvis index --path ~/Documents/client-project/ --agent my_assistant

Adding Custom Tools

Tool use is extensible. Add a custom tool by creating a Python function with the right decorator:

# tools/my_custom_tool.py
from openjarvis import tool

@tool(
    name="read_sensor",
    description="Read temperature from local IoT sensor",
    parameters={
        "sensor_id": {"type": "string", "description": "Sensor identifier"}
    }
)
def read_sensor(sensor_id: str) -> dict:
    # Your implementation here
    return {"temperature": 22.5, "unit": "celsius"}

Register it in your agent config:

tools:
  - file_system
  - custom: tools/my_custom_tool.py

Privacy Architecture: What Stays Local

For the privacy-sensitive use cases OpenJarvis targets, here’s what never leaves your device:

• Model weights (downloaded once, stored locally)
• Conversation history
• Memory store (vector embeddings)
• Tool outputs (file contents, code execution results)
• Online learning updates

The only network calls are explicit ones through the web_browser tool, which you can disable entirely for fully air-gapped deployments.

Performance Expectations

On a MacBook Pro M3 Pro with a Mistral 7B model:

• First token latency: ~0.5–1.5 seconds
• Generation speed: ~50–80 tokens/second
• Memory operations: < 100ms for recall

For larger models (14B+), expect 20–40 tokens/second on Apple Silicon, and faster on a dedicated NVIDIA GPU.

When to Use OpenJarvis vs. Cloud Agents

Getting Help

• GitHub: stanford-oval/openjarvis
• Documentation: Primary source for current API reference
• Primary coverage: MarkTechPost — Stanford OpenJarvis release

Researched by Searcher → Analyzed by Analyst → Written by Writer Agent (Sonnet 4.6). Full pipeline log: subagentic-20260312-2000

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