Skip to content
Stork.AIstork.ai

OpenJarvis

OpenJarvis v1.0 is an open-source framework for building personal AI agents that run on your own hardware, with Ollama support built-in.

Freemium · API

OpenJarvis in short

It's an open-source framework for private, low-cost AI agents, but requires technical setup and adequate local compute.

What is OpenJarvis?

OpenJarvis is an open-source framework for building personal AI agents developed by researchers at Stanford University and Lambda Labs that enables developers, software engineers, and power users to build and run AI agents on their own hardware. It prioritizes privacy, low latency, and reduced operational costs by enabling on-device execution of AI inference, agents, memory, and learning.

Key Features of OpenJarvis

OpenJarvis provides a comprehensive set of features designed for building and deploying local-first AI agents, emphasizing privacy and efficiency.

  • Open-source framework for building personal AI agents.
  • On-device execution of AI inference, agents, memory, and learning.
  • Built-in support for Ollama for local model management and execution.
  • LLM-guided spec search for local-cloud collaboration, optimizing on-device performance.
  • Multi-hop research capabilities with citations across web and local documents.
  • Cron-based agents for automated monitoring, summarization, and action.
  • Interaction via command-line interface (CLI), browser-based dashboard, and Tauri-based desktop application.
  • Modular five-primitive architecture (Intelligence, Engine, Agents, Tools & Memory, Learning) for extensibility.
  • Runtime opt-out for analytics, introduced in version 1.0.1.

Who Should Use OpenJarvis?

OpenJarvis is designed for technical users and organizations requiring robust, privacy-focused AI agent solutions that operate primarily on local hardware.

  • Developers & Software Engineers: For building, inspecting, modifying, and composing local-first AI agent systems.
  • Power Users & Creators: For automating personal AI tasks such as email triage, morning briefings, daily digests, and content organization.
  • Companies with Sensitive Personal Data: For deploying AI solutions that maintain data on-device, ensuring privacy and compliance requirements.
  • Founders & Forward Deployed Engineers: For rapid prototyping and deployment of AI agents with the benefits of local execution.
  • Users Seeking Local-First AI: For running AI agents without significant reliance on cloud APIs, aiming to reduce operational costs and latency.

How to Use OpenJarvis

OpenJarvis is designed for technical users to set up and run personal AI agents on their local hardware. It leverages Ollama for managing local large language models.

  1. Install Ollama on your local machine to manage and run local large language models.
  2. Download and install the OpenJarvis framework, available via CLI, web dashboard, or desktop application for macOS, Linux, and Windows.
  3. Configure OpenJarvis to integrate with your desired local models managed by Ollama.
  4. Define and customize AI agents using the framework's modular architecture for specific tasks.
  5. Interact with agents via the CLI, web interface, or desktop app for tasks such as research, automation, or personal assistance.
  6. Utilize the LLM-guided spec search feature for optimizing agent configurations for enhanced on-device performance.

OpenJarvis Pricing & Plans

OpenJarvis operates on a freemium model, offering a free tier for basic usage and paid tiers (Pro and Max) for increased cloud usage. The core framework is open-source and runs locally without direct cost, but cloud-assisted features or specific integrations may incur charges or usage limits.

  • Free: Includes session limits (reset every 5 hours) and weekly limits (reset every 7 days) for cloud usage.
  • Pro: Offers 50x more cloud usage than the Free tier, subject to session and weekly limits.
  • Max: Provides 5x more cloud usage than the Pro tier, also with session and weekly limits.

Pros

  • Prioritizes privacy and data security through on-device execution of AI agents.
  • Significantly reduces marginal API costs (approximately 800x lower) and latency (approximately 4x lower) for agentic workloads compared to cloud models.
  • Open-source framework allows for deep customization, inspection, and modification by developers.
  • Built-in Ollama support simplifies the setup and management of local large language models.
  • Modular five-primitive architecture promotes clean code and extensibility for AI system development.
  • LLM-guided spec search optimizes local model performance to achieve results within 3.2 percentage points of cloud models.

Cons

  • Requires technical comfort and setup for deployment and customization, making it less suitable as a plug-and-play end-user product.
  • Performance is dependent on local hardware capabilities, with complex agent workflows potentially slow on less powerful machines.
  • The Windows experience is noted as 'second-class' compared to WSL2 for optimal performance.
  • While offering cloud usage tiers, the primary benefit is local execution, which might not suit users without adequate local compute resources.
  • As a framework, users need to actively build and configure agents rather than utilizing pre-built, ready-to-use solutions.

OpenJarvis vs Competitors

OpenJarvis distinguishes itself within the local AI agent landscape through its open-source framework approach, emphasizing on-device execution and efficiency for personal AI.

See every OpenJarvis alternative, compared

OpenJarvis

Freemium · API

Visit OpenJarvis

Enjoying this? Get one like it in your inbox each morning.

one email a day · unsubscribe in two clicks · no third-party tracking

𝕏
X / Twittertwitter.com/ollama