Best open-source AI agents in 2026 (that actually work)

Twelve AI agents you can run today, from Buzz, goose and OpenHands to OpenCode, Hermes Agent, Cline, Codex CLI and Gemini CLI, with the license, last release and GitHub stars checked on each, and the one that is only source-available.

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Short answer: the best open-source AI agents in 2026 are OpenCode and goose for coding with any model, Cline for an approval-gated coding agent in your editor, OpenHands for running several coding agents from one control center, Hermes Agent for an always-on personal agent you reach from chat apps, browser-use for web browsing, and Buzz for agents that work inside your team chat. Eleven of the 12 below ship under MIT or Apache 2.0. Suna is source-available, not open source.

Open-source AI agents stopped being a research curiosity somewhere around mid-2026. Jack Dorsey's Block shipped a workspace where agents sit in the same channel as your team. A two-person research team's browser agent passed 100,000 GitHub stars. And a wave of self-hosted coding agents now do the job that used to require a subscription and an API key you didn't control.

The catch: "open source" gets stretched to cover a lot of things this year, from a fully MIT-licensed project you can fork and ship, to a "source-available" release with a license that quietly blocks you from competing with the vendor's own hosted product. If you're deciding what to actually run, that distinction matters more than the star count.

This guide covers twelve AI agents that are real, currently maintained, and verifiable on GitHub today. Eleven ship under an OSI-approved open-source license (MIT or Apache 2.0). One, Suna, is source-available, and we say so in its entry. For each we cover what it is actually for, where the license has strings attached, and how to think about picking one over building your own. This post is about finished agents you install and run. For the framework layer underneath them, see our breakdown of the best open-source AI agent frameworks, and for coordinating several agents in code, the best multi-agent frameworks.

How we picked and checked these agents

Every entry had to pass three checks. We opened the LICENSE file in its GitHub repository and named the license exactly, calling it open source only when the license is OSI-approved. The project had to ship a release in September 2026. And it had to be a finished agent you install and run, not a framework you build on. The order is editorial, not a star ranking: agents that cover a distinct job (team coordination, multi-agent control, a general assistant) come before agents that repeat an earlier entry's job with less adoption, which is why high-star vendor CLIs such as Codex CLI and Gemini CLI sit lower. Licenses, stars and release dates were checked on 2026-09-26. Evidence tier: maker docs, READMEs and LICENSE files. No hands-on testing is claimed.

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Best open-source AI agents: a brief overview

• Buzz: Best for making AI agents real members of your team chat and codebase, not just a bot you ping.
• goose: Best for a self-hosted coding agent that works with any model, including local ones.
• OpenHands: Best for running several coding agents from one self-hosted control center.
• OpenCode: Best for a terminal coding agent that works with 75+ model providers.
• Hermes Agent: Best for an always-on personal agent you reach from Telegram, Slack or Discord.
• Suna (Kortix): Best for a generalist agent that browses, writes files, and runs commands from plain instructions, source-available rather than fully open source.
• browser-use: Best for giving any agent the ability to actually operate a web browser.
• Open Interpreter: Best for a lightweight agent harness tuned to get strong results out of cheap models.
• Cline: Best for an approval-gated coding agent inside VS Code, the terminal, or a desktop app.
• Codex CLI: Best for running OpenAI's coding agent locally on a ChatGPT plan you already pay for.
• Gemini CLI: Best for a free-tier terminal agent built around Gemini models.
• Cindy: Best for one local agent that carries your existing Claude Code or Codex subscription across every app on your machine.

GitHub stars per agent repository

Source: GitHub repository pages (NousResearch/hermes-agent, anomalyco/opencode, openai/codex, browser-use/browser-use, google-gemini/gemini-cli, OpenHands/OpenHands, cline/cline, openinterpreter/openinterpreter, aaif-goose/goose, block/buzz, kortix-ai/suna, makecindy/cindy), checked 2026-09-26. Stars measure attention, not fitness for your job.

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1. Buzz, best for making AI agents real team members

Buzz is Block's open-source answer to a problem every team running agents eventually hits: your agents live in a different system than your humans do. Chat is in Slack, code review is in GitHub, and the agent's output shows up as a pull request nobody in the channel actually saw get built. Buzz collapses all three into one workspace built on the Nostr protocol, where every message, commit, and workflow step is a signed event in a single log, and every participant, human or agent, has its own cryptographic identity and permission set.

Because agents show up as members rather than as a bolted-on integration, you can watch one work in real time, in the same channel your team already uses, with an audit trail that shows exactly which identity did what. It's early: mobile apps and approval gates are still in development, and the desktop client is the primary way in today. For teams that are past the "one developer with an agent on their laptop" stage and into "several agents touching shared systems," Buzz is worth a serious look before you improvise your own version of this with webhooks and a spreadsheet. See our guide to using Buzz for setup and how agents actually join a channel.

Key features

• Every human and agent gets its own cryptographic identity and permission scope
• Chat, code review, and Git events live in one signed, auditable log
• Self-hostable Nostr relay, written in Rust, or use the free desktop app
• Agents participate as channel members, not as a separate bot integration

Best for

• Teams already running more than one agent that need a shared, auditable workspace
• Engineering teams tired of losing context at the handoff between chat, code, and CI
• Organizations that want a permission model for agents instead of one shared API key

Pricing

• Free and open-source under Apache 2.0
• Self-hosted: your own infrastructure costs only
• Desktop app free for macOS, Windows, and Linux

Pros

• Solves a real coordination problem instead of adding another chat integration
• Cryptographic identity per agent makes accountability and permissioning concrete
• Backed by Block, with active development and a real roadmap

Cons

• Genuinely early: mobile apps, richer approval gates, and voice features are still coming
• Self-hosting the relay is the most capable path, and that means running your own infrastructure
• The ecosystem of pre-built agent integrations is still forming

2. goose, best for a self-hosted coding agent that works with any model

goose started at Block and was donated to the Linux Foundation's Agentic AI Foundation in 2026, and it has grown into one of the most widely used open-source coding agents on GitHub. The design goal is straightforward: an agent that goes beyond code suggestions and actually installs dependencies, edits files, runs tests, and debugs failures, without locking you into one model vendor.

That model independence is the reason teams reach for goose over a closed alternative. It supports 15+ LLM providers, including local models through Ollama, so you can run an entirely offline setup with no API key and no data leaving your machine if that's a requirement. Extensions ship through the Model Context Protocol, and with 70+ available, most tools a coding agent needs to touch (databases, browsers, ticketing systems) already have one. It runs as a CLI, a desktop GUI, or an API, so it fits into a terminal-first workflow or a more structured internal tool.

Key features

• Works with 15+ LLM providers, including fully local models via Ollama
• 70+ Model Context Protocol extensions for tools and data sources
• CLI, desktop app, and API, all built in Rust
• Can install, execute, edit, and test code autonomously, not just suggest it

Best for

• Engineering teams that want a coding agent without committing to one model vendor
• Developers who need a fully offline option for sensitive codebases
• Teams building AI agent development workflows on infrastructure they already control

Pricing

• Free and open-source under Apache 2.0
• No usage fees when self-hosted; you pay only for whichever model provider you connect

Pros

• Provider flexibility means you are not rebuilding your agent stack when a model vendor changes pricing
• Large, active community: 50,000+ stars and daily development activity
• Local-model support makes a genuinely offline agent workflow realistic

Cons

• Rust-based extension development has a steeper learning curve than a pure-Python stack
• Desktop GUI is newer than the CLI and less battle-tested for complex workflows
• No built-in multi-agent orchestration; that's a separate layer you add yourself

3. OpenHands, best for running multiple coding agents from one control center

OpenHands takes a different angle than a single-agent tool: it's a self-hosted control center for running coding agents, including OpenHands' own agent, Claude Code, Codex, Gemini, or anything that speaks the Agent Client Protocol, across local, remote, and cloud backends. Instead of picking one agent and living inside its interface, you get a layer that manages several at once and wires them into the tools your team already uses.

That control-center framing is what makes OpenHands useful past the individual-developer stage. It supports automations tied to Slack, GitHub, and Notion, so an agent run can be triggered by an actual event in your existing stack rather than a manual prompt. For a team standardizing how multiple engineers use multiple agents, that's a materially different problem than "does this one agent write good code," and it's the one OpenHands is built to solve.

Key features

• Runs multiple agent types (its own agent, Claude Code, Codex, Gemini, ACP-compatible agents) from one interface
• Automations that connect to Slack, GitHub, and Notion
• Works across local, remote, and cloud backends
• Self-hosted developer control center rather than a single-agent tool

Best for

• Teams standardizing agent usage across multiple engineers and multiple agent types
• Organizations that need agent runs triggered by real events, not manual prompts
• Teams evaluating custom workflow automation before building an internal agent platform from scratch

Pricing

• Free and open-source under MIT license
• Self-hosted with no usage fees beyond your own infrastructure and model costs

Pros

• One of the largest open-source agent communities on GitHub, with 80,000+ stars
• Genuinely multi-agent and multi-backend rather than tied to a single model or vendor
• Built-in automation hooks reduce the custom glue code most teams end up writing anyway

Cons

• More moving parts to configure than a single-agent CLI tool
• Self-hosting a control center is a bigger operational commitment than running one agent locally
• Best suited to coding and engineering workflows, not general business task automation

4. OpenCode, best for a terminal coding agent that works with 75+ model providers

OpenCode calls itself "the open source AI coding agent", and on GitHub it backs that up: 210,209 stars when we checked on 2026-09-26, MIT license, and a release on 2026-09-21. The repository now lives at anomalyco/opencode. It is a terminal-first agent with a full text interface, plus a desktop app that the project itself still labels beta.

The detail that sets it apart from most terminal agents is the built-in split between two agents you switch with the Tab key. The "build" agent has full access for development work. The "plan" agent is read-only: it denies file edits by default and asks before running bash commands, which makes it a safe way to explore an unfamiliar codebase before you let anything change. Its docs say it uses the AI SDK and Models.dev to support 75+ LLM providers, and it can run local models through Ollama, LM Studio or llama.cpp.

Key features

• Two built-in agents: full-access "build" and read-only "plan", switched with Tab
• A "general" subagent for complex searches and multistep tasks, called with @general
• 75+ LLM providers per its docs, including local models through Ollama and LM Studio
• Installs through npm, Homebrew, Scoop, Chocolatey, pacman, mise or Nix

Best for

• Terminal-first developers who want to swap model providers without swapping tools
• Teams that want a read-only planning pass before an agent touches files
• Anyone standardizing on an MIT-licensed coding agent they can fork

Pricing

• Free and open-source under the MIT license
• You pay only for the model provider you connect, or nothing for local models

Pros

• The largest coding-agent community on this list by GitHub stars
• Read-only plan mode gives you a safer default for exploration
• Wide provider support keeps you out of single-vendor lock-in

Cons

• The desktop app is marked beta in the project's own README, so the terminal is the mature path
• A terminal interface is a harder sell for teammates who live in an IDE
• Skip it if you want agents coordinating across a team; OpenHands or Buzz fit that better

5. Hermes Agent, best for an always-on personal agent you reach from chat apps

Hermes Agent comes from Nous Research and is the most-starred repository on this list: 249,199 stars on 2026-09-26, MIT license, with release v2026.9.24 two days before we checked. It is a general agent, not a coding tool. Nous describes it as "the self-improving AI agent": it writes skills from completed tasks, improves them during use, keeps agent-curated memory, and searches its own past conversations.

The practical difference is where it lives. One gateway process connects it to Telegram, Discord, Slack, WhatsApp, Signal and the CLI, so you can message it from your phone while it works on a cloud machine. A built-in cron scheduler runs natural-language jobs such as daily reports unattended. It supports seven terminal backends (local, Docker, SSH, Singularity, Modal, Daytona and Vercel Sandbox), and you can point it at Nous Portal, OpenRouter, OpenAI or your own endpoint, switching with hermes model.

Key features

• Agent-curated memory, skill creation after complex tasks, and search across past sessions
• One gateway for Telegram, Discord, Slack, WhatsApp, Signal and the CLI
• Built-in cron scheduler that delivers results to any connected platform
• Seven terminal backends, including serverless ones that hibernate when idle

Best for

• Individuals who want a personal agent that runs on a server and answers in their chat apps
• Teams automating recurring reports or audits on a schedule
• Builders who want a self-hosted general agent under a permissive license

Pricing

• Free and open-source under the MIT license
• Model costs depend on the provider you connect; hosting can be a small VPS per the README

Pros

• Broad reach: chat apps, scheduling and subagents in one self-hosted process
• Model-agnostic, with provider switching that needs no code changes
• Date-based release tags (v2026.9.24) make it easy to see how current your install is

Cons

• The README warns that Windows antivirus tools can quarantine its bundled uv.exe as a false positive, which adds setup friction on Windows
• Connecting an agent to your messaging accounts and a shell is a wide permission scope to secure
• Skip it if your job is focused coding work in a repository; OpenCode, goose or Cline fit that better

6. Suna (Kortix), best for a generalist agent that handles real tasks end to end

Suna, built by Kortix, is a generalist agent rather than a coding-focused one: point it at a task described in plain language and it can research a topic, browse the web, manage files, run commands, and deploy a website, chaining those actions together instead of stopping at a single answer. That breadth is the appeal for teams that want one agent handling research, data pulls, and operational busywork rather than stitching together several narrow tools.

One thing worth being precise about: Kortix describes Suna as "fully open source," and the repository does ship a LICENSE file, but it's the Elastic License 2.0, not an OSI-approved open-source license. Elastic 2.0 lets you use, modify, and self-host the code freely, but it specifically restricts offering it as a competing hosted service. That's a meaningfully different deal than Apache 2.0 or MIT, and it's worth reading before you build a product on top of it, not after.

Key features

• Handles browsing, file management, command-line execution, and website deployment from natural-language instructions
• Chains multiple actions together to complete a task rather than answering once and stopping
• Self-hostable with a setup wizard, or run on Kortix Cloud as a managed option
• Integrates with external APIs and services for broader task automation

Best for

• Operations teams that want one generalist agent instead of five narrow ones
• Teams comfortable self-hosting who want full control over a broad-task agent
• Anyone evaluating Kortix Cloud who first wants to confirm what "open source" covers in this specific license

Pricing

• Source-available under the Elastic License 2.0; free to self-host and modify
• Kortix Cloud: managed hosting available, pricing on kortix.com
• Restriction: the license blocks offering Suna itself as a competing hosted service

Pros

• Genuinely broad task coverage in a single agent, not just a coding tool
• Setup wizard lowers the bar for self-hosting compared to a bare repository
• Active project: 20,000+ stars and commits as recent as this week

Cons

• Elastic License 2.0 is source-available, not open source in the OSI sense; check it against your use case
• Smaller community than goose or OpenHands, so fewer third-party integrations
• Broad task coverage means more surface area to sandbox and monitor safely in production

7. browser-use, best for giving any agent the ability to actually use the web

browser-use is not a standalone agent you chat with, it's the layer that lets an agent operate a browser the way a person would: opening pages, clicking, typing into forms, and reading what's on screen, driven by natural-language instructions rather than brittle scraping code. Built originally by two researchers out of ETH Zurich, it has become one of the fastest-growing open-source AI projects on GitHub, passing 100,000 stars.

The reason it matters for this list: most of the other agents here are strong at writing code or managing files, but weak at the huge share of business software that only exposes a web UI with no API. browser-use is what plugs that gap. Pair it with goose, OpenHands, or a custom agent built on any framework, and you get an agent that can log into a portal, pull a report, or fill out a form the same way a person on your team would.

Key features

• Lets any LLM control a real browser: navigate, click, type, and read page content
• Works from natural-language task descriptions rather than hardcoded scraping selectors
• Python library designed to plug into other agents and frameworks
• Actively maintained with frequent releases

Best for

• Teams automating tasks against internal tools or vendor portals that have no API