7 Best Open Source AI Assistants You Can Run Yourself in 2026

The best open source AI assistant for most people in 2026 is Jan, a free desktop app that runs AI models offline or connects to cloud models with your own key. Developers should look at OpenClaw, a personal agent that lives in your chat apps and acts for you. Open WebUI, AnythingLLM and LibreChat round out the strongest picks.

For each project below you get what it does, the hardware it needs, how hard it is to set up, its license and how active development is. Development status comes from each project's GitHub repository, checked on October 19, 2026.

Which open source AI assistant should you choose?

• Best for non-developers: Jan. Install it like any app, download a model and chat offline.
• Best for developers: OpenClaw. A personal agent that answers you in WhatsApp, Telegram or Slack and can run tasks on your machine.
• Best for chatting with your files: AnythingLLM.
• Best for a shared home or office server: Open WebUI or LibreChat.

Most of these apps have two parts: the interface you talk to and the model that writes the answers. The model can run on your own computer through an engine such as Ollama or llama.cpp, or in the cloud through an API key from a company like OpenAI or Anthropic. Local keeps your text private and costs nothing per message. Cloud gives smarter answers on modest hardware, but you pay per use and your prompts leave your machine.

Not sure you need to run anything yourself? If you just want the most capable chatbot with zero setup, our roundup of the best paid AI assistants covers ChatGPT, Claude and Gemini.

1. Jan: best for non-developers who want offline chat

Jan is a free desktop app for Windows, macOS and Linux that looks and feels like ChatGPT but runs on your own computer. You pick an open model such as Llama, Gemma, Qwen or gpt-oss, download it once and chat without an internet connection. When you want a stronger model, you can add an OpenAI or Anthropic API key.

It matters because it removes the usual setup pain. There is no terminal, no Docker and no server to manage. Jan also runs a local API at localhost:1337, so other apps can use your local model, and it supports MCP (Model Context Protocol, a standard way to plug tools into an AI).

• Hardware: Jan's system requirements on GitHub suggest 8 GB of RAM for 3B models, 16 GB for 7B models and 32 GB for 13B models on a Mac. ("7B" means 7 billion parameters, a rough measure of model size.)
• Setup: Easy. Download, install, choose a model.
• License: Apache 2.0, maintained by Menlo Research.
• Development: Very active. Version 0.8.7 shipped in October 2026.

Who it suits: anyone who wants a private assistant for drafting, summarizing and brainstorming without sending text to a cloud company. If raw answer quality matters more than privacy, compare the best AI subscription plans before you commit.

2. AnythingLLM: best for chatting with your own documents

AnythingLLM, from Mintplex Labs, is an all-in-one app built around workspaces. You drop PDFs, Word files or notes into a workspace, and the assistant answers questions about them with citations back to the source. It comes as a desktop app for Mac, Windows and Linux, plus a Docker version for servers.

Its strength is turning a folder of contracts, course notes or client briefs into something you can question in plain English. Recent versions add a built-in local model engine, so you don't need a separate tool like Ollama, along with memories, scheduled tasks, MCP support and a no-code agent builder.

• Hardware: Light with a cloud model. For local models, plan on the same RAM tiers as Jan.
• Setup: Easy on desktop, moderate with Docker.
• License: MIT.
• Development: Very active. Version 1.18.0 shipped on October 12, 2026.

Who it suits: freelancers, students and researchers who work from piles of documents. One thing to change on day one: AnythingLLM sends anonymous usage telemetry by default, and you can switch it off under Privacy in the sidebar. Multi-user accounts and the website chat widget need the Docker version.

3. Open WebUI: best self-hosted ChatGPT-style hub

Open WebUI is a web interface you host yourself and open in any browser on your laptop, phone or tablet. It pairs naturally with Ollama for local models and connects to any OpenAI-compatible API, so one screen can hold a private local model and a paid cloud model side by side.

It is the most feature-packed option here: document search, web search, voice calls, persistent memory, a calendar with AI scheduling, automations, user accounts with permissions and a plugin system. That makes it a strong choice for a family or small office sharing one AI server.

• Hardware: Anything that runs Docker or Python. Local models need the RAM tiers above, and a graphics card helps.
• Setup: Moderate. Install with pip, uv or Docker, then create an admin account.
• License: Custom. The Open WebUI license text is BSD-style but adds a branding clause: deployments with more than 50 users in a 30-day period can't remove the Open WebUI name without permission or an enterprise license.
• Development: Very active. Version 0.12.3 shipped on October 16, 2026.

Who it suits: people who already run a home server or NAS (a network storage box), and office tech leads who want a private chat tool for a small team. Purists should note the branding clause means this is not a license approved by the Open Source Initiative, the group that maintains the open-source definition.

4. LibreChat: best for teams that mix many AI providers

LibreChat is a ChatGPT-style interface that plugs into almost every major model provider at once: OpenAI, Anthropic, Google, Azure and AWS Bedrock, plus local engines like Ollama. You bring the API keys, and your team picks the right model for each job from one login.

Its agent features are the big draw. You get no-code custom assistants, MCP tools, a sandboxed code interpreter, web search and reusable instruction bundles called skills. Version 0.8.8 added an agent management API and approval controls that ask before an agent writes files or runs commands.

• Hardware: Light. A small cloud server or spare PC is enough, because the heavy work usually happens at the model provider.
• Setup: Moderate. Docker Compose plus a configuration file.
• License: MIT.
• Development: Active. Version 0.8.8 shipped on September 30, 2026, with new commits through mid-October.

Who it suits: small agencies and teams that want one private workspace instead of a stack of separate chat subscriptions. To see what a single seat on a consumer plan buys instead, compare the $20 AI plans.

5. OpenClaw: best for developers who want a personal agent

OpenClaw is the most ambitious project on this list. It runs a small server, called the Gateway, on your own computer and meets you in the chat apps you already use, including WhatsApp, Telegram, Slack, Discord, Signal and iMessage. Unlike a chat window, it can take action: read files, run commands, browse the web and work on a schedule.

Models are swappable plugins, so you can use Claude, OpenAI's Codex or a local model through Ollama. Your memory, settings and credentials stay on your hardware. The project is run by the OpenClaw Foundation, an independent US nonprofit (a 501(c)(3), the US tax category for charities), and has no paid tier.

• Hardware: Light with a cloud model. A spare Mac mini, an old laptop or a small Linux box that stays on works well.
• Setup: Advanced. A one-line installer sets up Node.js, then the openclaw onboard wizard connects your model and chat apps.
• License: MIT.
• Development: Extremely active, with date-based releases. Version 2026.10.5 landed on October 18, 2026.

That power cuts both ways. A WebSocket flaw tracked as CVE-2026-25253, rated 8.8 out of 10 (high severity) in the US National Vulnerability Database, affected versions before 2026.1.29. Prompt injection, where hidden instructions in an email or webpage hijack the agent, is still a risk for any agent like this.

Keep it updated, never expose the Gateway to the open internet, treat community skills as untrusted code and run openclaw security audit after every configuration change, a step the OpenClaw security guide recommends. Who it suits: developers and technical hobbyists who want an assistant that does things, not just talks, and who are comfortable locking it down.

6. Goose: best for automating tasks on your computer

Goose is a general-purpose AI agent that started at Block, the company behind Square and Cash App, and now sits in the Agentic AI Foundation at the Linux Foundation. It comes as a native desktop app for macOS, Windows and Linux, a command-line tool and an API.

It is popular with programmers, but it isn't only for code. You can ask it to research a topic, clean up a spreadsheet, rename a folder of files or draft a report, and it uses tools to finish the job. It works with more than 15 model providers, including Ollama for local models, and connects to more than 70 extensions through MCP.

• Hardware: Light with a cloud model. Agent work is demanding, so small local models may struggle with longer tasks.
• Setup: Moderate. Install the desktop app, then add a provider key.
• License: Apache 2.0.
• Development: Very active. Version 1.55.0 shipped on October 16, 2026.

Who it suits: developers and power users who want an agent at their desk rather than in their phone's chat apps. For tasks to hand it on day one, borrow a few from our copy-ready AI prompts library.

7. Leon: best for tinkerers who want to shape a project

Leon is a veteran among open source personal assistant AI projects: its first release came in 2019. It is now being rebuilt around tools, memory and agent-style planning. It works with local or cloud AI providers, keeps layered memory of your preferences, and its new core can operate desktop and browser apps for you.

The catch is maturity. A notice in the repository dated March 29, 2026, says the new Leon 2.0 lives on a developer preview branch, its new documentation isn't ready, and the stable branch still holds the older version.

• Hardware: Depends on your provider. Local models need the same RAM tiers as the others.
• Setup: Advanced. Expect to build from source and read the code.
• License: MIT.
• Development: Active, with commits in mid-October 2026, but no stable 2.0 release yet.

Who it suits: developers who want to join an assistant early and help steer it. Everyone else should wait for a stable release.

What should you check before you install one?

Start with where the model runs, because that decides almost everything else. Then work through this checklist.

1. Check your RAM. Using Jan's guidance, 8 GB handles small 3B models, 16 GB handles 7B models and 32 GB handles 13B models. On a Windows PC, a recent NVIDIA, AMD or Intel Arc graphics card speeds things up.
2. Pick local, cloud or both. Local keeps your text private. Cloud gives better answers on weak hardware, but read the provider's data policy. If you handle customer data at work, UK GDPR still applies in the UK, while US rules depend on your state and industry.
3. Match setup to your comfort level. If Docker means nothing to you, start with Jan or the AnythingLLM desktop app.
4. Read the license if you'll use it at work. MIT and Apache 2.0 allow commercial use. Open WebUI's branding clause matters for larger deployments.
5. Check recent activity. Open the GitHub repository and look at the dates of the latest release and commit. Stale projects fall behind on new models and security fixes.

That last check is why GPT4All, once a common recommendation, isn't on this list: its latest release dates to February 2025 and its last code change to May 2025. Khoj, an AGPL-licensed "second brain" assistant, still works, but its last code change was on August 1, 2026, and its team now promotes a newer app, Pipali. LM Studio is free to download, but the app itself isn't open source.

If a hosted tool built for your job sounds easier than running your own, see our guide to the best AI productivity assistants.