Open-Source AI Agents You Can Run Locally on Your Mac in 2026

The open-source AI agent ecosystem has exploded. A year ago you had maybe three credible projects. Now there are dozens, each with different philosophies about how an agent should perceive and act on a computer.

But most of them still assume you are running Linux, deploying to the cloud, or piping everything through an API. If you are on a Mac and you want something that runs locally - with your data staying on your machine - the options narrow fast.

Here is an honest look at 10 open-source agent projects, what they actually do well, where they fall short, and who each one is best for.

The Roundup

1. Fazm - Desktop Automation via Accessibility Tree

GitHub stars: ~2.5k | License: MIT

Fazm takes a fundamentally different approach to desktop automation. Instead of taking screenshots and feeding them to a vision model, it reads the macOS accessibility tree - the same structured data that screen readers use. This means it knows what every button, text field, and menu item actually is, not just what they look like.

Pros: Native macOS support is the primary focus, not an afterthought. The accessibility tree approach is faster and more reliable than screenshot-based methods. Runs fully locally. Low latency because there is no image processing overhead.

Cons: Still early - the ecosystem of pre-built automations is small. Requires macOS accessibility permissions. Less useful for web-only workflows where browser agents shine.

Best for: Mac power users who want reliable desktop automation without sending screen data to the cloud. Developers building local-first automations.

2. GPT-Automator - Voice-Controlled Desktop Agent

GitHub stars: ~3k | License: MIT

GPT-Automator lets you control your Mac with voice commands. You speak, it interprets, it acts. The idea is compelling - tell your computer what to do in natural language and watch it happen.

Pros: Voice interface feels natural for certain workflows. Integrates with macOS system commands. Quick to set up for basic tasks.

Cons: Relies on OpenAI API, so not truly local inference. Voice recognition adds latency and error. Complex multi-step tasks often break down. The project has not seen much activity recently.

Best for: People who want a voice-first interaction model and are okay with API dependency. Good for accessibility use cases.

3. BrowserOS - Browser as Operating System

GitHub stars: ~1.8k | License: Apache 2.0

BrowserOS treats the browser as the entire computing environment. The agent lives in the browser, acts in the browser, and treats web apps as its workspace. On macOS, this means Chrome or Firefox becomes the surface the agent operates on.

Pros: Cross-platform by nature since it only needs a browser. Good at multi-tab workflows. Clean architecture for web-based task automation.

Cons: Cannot interact with native macOS apps at all. Limited to what is possible inside a browser. Performance depends heavily on the underlying LLM.

Best for: Users whose entire workflow lives in web apps - Gmail, Notion, Slack, spreadsheets.

4. Open ChatGPT Atlas (Composio)

GitHub stars: ~5k | License: Apache 2.0

Composio's open-source agent framework focuses on tool integration. It comes with hundreds of pre-built integrations for SaaS tools and provides a structured way for agents to authenticate and interact with external services.

Pros: Massive library of integrations. Well-documented. Active development and community. Handles OAuth and authentication flows well.

Cons: More of a framework than a standalone agent. The integrations are mostly cloud services, not local apps. macOS-specific support is limited. Steeper learning curve than simpler tools.

Best for: Developers building agents that need to connect to many external services. Teams that want a structured integration layer.

5. AgentQL - Structured Web Queries

GitHub stars: ~1.5k | License: Apache 2.0

AgentQL takes a query-language approach to web interaction. Instead of telling an agent "click the submit button," you write structured queries that describe what data you want from a page and what actions to take. Think of it as SQL for web pages.

Pros: Deterministic and predictable - the query language reduces ambiguity. Great for scraping and data extraction. Works well for repetitive, structured tasks.

Cons: Not a general-purpose agent - it is a tool for specific web interaction patterns. Requires learning its query syntax. Not designed for desktop automation.

Best for: Developers who need reliable, repeatable web data extraction and form filling. People who want precision over flexibility.

6. Browser Use - Python Browser Automation

GitHub stars: ~8k | License: MIT

Browser Use is one of the more popular browser agent libraries. It gives an LLM control of a browser through a clean Python API. You describe what you want done, and it figures out the clicks, typing, and navigation.

Pros: Large community. Good documentation. Supports multiple LLM backends. Active development. Works on macOS through standard browser drivers.

Cons: Browser-only - no desktop app interaction. Can be slow on complex pages. Still struggles with dynamic web apps that use heavy JavaScript. Debugging failures requires patience.

Best for: Python developers who want a mature browser automation library with LLM integration. Good middle ground between simplicity and capability.

7. LaVague - Web Agent with Selenium

GitHub stars: ~4k | License: Apache 2.0

LaVague turns natural language instructions into Selenium browser actions. It is built for people who are already familiar with web testing frameworks and want to add AI-driven automation on top.

Pros: Builds on the mature Selenium ecosystem. Good for teams already using Selenium. Supports local LLM backends. Clean separation between the AI planning and browser execution layers.

Cons: Inherits Selenium's limitations - slow, brittle on dynamic pages. Limited to browser interactions. Setup can be fiddly, especially with driver versions on macOS.

Best for: QA engineers and web developers who already know Selenium and want to augment their existing test infrastructure with AI.

8. Auto-GPT - The Original Autonomous Agent

GitHub stars: ~160k | License: MIT

Auto-GPT was the project that kicked off the autonomous agent hype in 2023. It chains LLM calls together to accomplish goals, with the agent deciding its own next steps. The project has evolved significantly and now includes a more structured agent platform.

Pros: Enormous community and ecosystem. Lots of plugins and extensions. Well-known, so finding help is easy. The newer versions are much more stable than the original.

Cons: Primarily cloud-LLM dependent - running fully local requires significant setup. Can burn through API credits fast with its iterative approach. Often over-engineers simple tasks. macOS is supported but not a priority.

Best for: Experimenters who want to explore autonomous agent behavior. People who do not mind API costs and want maximum flexibility.

9. CrewAI - Multi-Agent Orchestration

GitHub stars: ~22k | License: MIT

CrewAI is about coordinating multiple agents that work together. You define agents with different roles - researcher, writer, analyst - and they collaborate on tasks. It is less about controlling your computer and more about orchestrating AI workflows.

Pros: Elegant multi-agent model. Good for complex workflows that benefit from specialization. Active community. Works with multiple LLM providers including local ones.

Cons: Not a desktop automation tool at all. The multi-agent overhead can be wasteful for simple tasks. Requires careful prompt engineering for each agent role. More of a framework than a ready-to-use tool.

Best for: Developers building complex AI workflows where task decomposition and agent specialization matter. Content teams, research pipelines, data analysis workflows.

10. OpenHands - Full Development Agent

GitHub stars: ~12k | License: MIT

OpenHands (formerly OpenDevin) is an AI software engineer that can write, test, and debug code. It operates in a sandboxed environment and can interact with a full development setup including terminal, browser, and file system.

Pros: Impressive coding capabilities. Sandboxed execution is safer than giving an agent full system access. Active development backed by a strong team. Good at end-to-end development tasks.

Cons: Focused on software development, not general desktop automation. The sandbox adds overhead. macOS support works but the project is primarily Linux-focused. Resource-heavy.

Best for: Developers who want an AI pair programmer that can actually execute code and test its own work. Teams looking to automate development workflows.

Comparison Table

How to Choose

The right agent depends on what you are actually trying to automate. Here is a simple decision framework:

"I want to automate macOS desktop apps" - Start with Fazm. It is the only project here that treats macOS desktop automation as its core mission rather than an afterthought. The accessibility tree approach means it understands your apps structurally, not just visually.

"I mostly work in the browser" - Browser Use has the largest community. AgentQL is better if you need precision and repeatability. LaVague makes sense if you already use Selenium.

"I need agents that talk to many APIs and services" - Composio gives you the widest integration library out of the box.

"I want AI to write code for me" - OpenHands is the strongest option here. It can plan, code, test, and iterate.

"I want multiple agents working together" - CrewAI is the most mature multi-agent orchestration framework.

"I want everything to stay on my machine" - This narrows the field significantly. Fazm runs locally by design. Browser Use and LaVague can work with local LLMs. Most others lean heavily on cloud APIs. Read more about why local-first matters for AI agents.

"How do these compare to commercial options?" - If you are weighing open-source against products like OpenAI Operator, the tradeoff is usually polish and reliability versus privacy and customization. Open-source agents give you control. Commercial ones give you convenience.

The Honest Reality

None of these projects are fully mature yet. Desktop automation is hard. Browser automation is hard. Giving an LLM reliable control of a computer is an unsolved problem that everyone is working on simultaneously.

The best approach in 2026 is to pick the tool closest to your actual use case, accept that it will break sometimes, and stay close to the projects that are shipping improvements fastest. If you want to understand the technical tradeoffs behind different control approaches, read our breakdown of accessibility APIs vs screenshot-based computer control.

The good news is that the pace of improvement is rapid. What was barely functional six months ago is now genuinely useful for real workflows. And because these are all open source, you can see exactly where they are headed - just check the commit history.