Reinventing.AI
ReferenceUpdated 2026-09-0511 harnesses

AI Agent HarnessesWhere your agent actually lives

A harness is the program around the model: the chat, the file access, the tools, the scheduler and the approval gate. Eleven of them now read the same skill format, so the choice is smaller than it looks. This page compares them on what differs and ends with a sixty second way to pick one.

What a harness does that the model does not

The model reasons. The harness decides how instructions are read, how tools run, when a person is asked for approval, how sessions persist, how work resumes in the background, and how the agent reaches you: in a terminal, a chat app, a browser or an API. That layer is where practical value is won or lost.

In 2026 the category split into distinct shapes. Some harnesses are code first and terminal native. Some are operator layers that live in chat apps and run cron jobs overnight. One runs on its own cloud computer. The comparison below is organised around those differences rather than around feature lists.

The reason the choice is low stakes: every harness on this page reads the Claude Code skill format, a folder with one file that says how a job gets done. Standards, skills and routines move with you. The harness is a residence, not a marriage.

The parts every harness has

  • A conversation surface: terminal, desktop app, editor, chat app or web UI
  • File and shell access on a real machine
  • Tool calling, including MCP servers and browser control
  • A scheduler for recurring work, or a hook into the operating system's
  • An approval gate for anything that sends, spends or deletes
  • Session memory so work resumes where it stopped

The comparison

Four things actually separate these tools: where the agent runs, how recurring work is scheduled, which models it can drive, and whether you can read the source.

HarnessRunsSchedulingModelsSourceBest for
Claude Code logoClaude Code
Anthropic
Your machine, in a terminal or the Code tab of Claude DesktopScheduled tasks, built inClaude modelsProprietary client, open skill formatThe default starting point, and the harness the Agent Ops Club builds and tests everything on
Codex logoCodex
OpenAI
Your machine, terminal CLI, plus cloud tasksScheduled runsOpenAI modelsOpen source CLITeams whose stack and subscriptions already live on OpenAI
OpenCode logoOpenCode
Open source community
Your machine, terminalRecurring runsAny provider; model agnostic by designOpen sourceOperators who want to switch models freely without rebuilding their setup
Grok Bot logoGrok Bot
xAI
Its own cloud computer: a virtual machine with a browser, a terminal and file accessRoutines it saves and runs on a schedule, on its own machineGrok modelsProprietary, in beta for SuperGrok and Cursor subscribersAlways on operation without building a VPS yourself
OpenClaw logoOpenClaw
Open source community
Your machine or a VPS, reached through WhatsApp, Telegram, Discord, Slack and other chat appsCron jobs and heartbeats, built inAny major providerOpen sourceChat first, always on operator work across messaging surfaces
Hermes logoHermes
Nous Research
Self hosted on your machine or server, with Telegram, Discord and Microsoft Teams surfacesRecurring jobsMany providers, plus an OpenAI compatible local proxy for other clientsOpen sourceAmbitious self hosters who want one harness to absorb the whole frontier
Pi logoPi
Mario Zechner
Your machine, terminal, headless with a single flagNone of its own; use the operating system's schedulerAny providerOpen sourcePeople who want to see every moving part
Cline logoCline
Cline
VS Code extension, plus a CLI that runs without the editor openIts own cron in the CLIAny providerOpen sourceAnyone who already lives in VS Code all day
Qwen Code logoQwen Code
Alibaba
Your machine, terminal, headless with qwen -pScheduled tasks, built inQwen models first, with OpenAI, Anthropic and Gemini keys acceptedOpen sourceOperators whose first question is cost per run
DeepSeek logoDeepSeek
DeepSeek
A local server with a web interface, launched as dshA plugin, like everything else in itDeepSeek models, with models as pluginsOpen sourceEarly adopters who want a plugin shaped harness to watch
Antigravity logoAntigravity
Google
Your machine, as an agent first editor plus the agy command lineThe agy job runnerGemini models first, with other frontier models selectableProprietaryTeams on Google's stack who want an editor built around agents and a job runner for the scheduled work

Also reads the format, no icon on this page yet: .

Each harness, honestly

Claude Code logo

Claude Code

by Anthropic

Claude Code runs in a terminal or inside Claude Desktop, reads and writes real files, runs real commands, and schedules recurring work. It defined the skill format that the other harnesses on this page read, which is why it is the reference implementation for most operator kits published today.

Best for
The default starting point, and the harness the Agent Ops Club builds and tests everything on
Running a scheduled routine
Native format. Leave a kit where you extracted it and register each routine as a scheduled task pointed at its folder. Routines are scheduled work, not on demand skills, so they do not belong in a global skills directory.
Codex logo

Codex

by OpenAI

OpenAI's agent harness is capable, especially on code heavy work, and the natural pick when the whole team already runs on OpenAI. The Claude Code skill format works there with minor adjustment.

Best for
Teams whose stack and subscriptions already live on OpenAI
Running a scheduled routine
Drive the cadence with Codex scheduled runs, one job per routine, each handed that routine's SKILL.md as the run prompt.
OpenCode logo

OpenCode

by Open source community

OpenCode is open source and model agnostic, which is its actual pitch: point it at different models and keep the working setup you already have. Worth it when model flexibility matters more than polish.

Best for
Operators who want to switch models freely without rebuilding their setup
Running a scheduled routine
Reads the Claude Code skill format directly. Mirror the schedule table into its recurring runs, one run per routine folder.
Grok Bot logo

Grok Bot

by xAISince August 2026

Grok Bot arrived in August 2026 as something structurally different: every other harness here runs on your machine, while a Grok Bot gets its own cloud computer. It signs into the tools you use, keeps working while your laptop is shut, and comes back when something needs approval. Show it a workflow once and it saves that workflow as a routine it can run on a schedule.

Best for
Always on operation without building a VPS yourself
Running a scheduled routine
The closest native fit for scheduled routines. Create one recurring task per routine and hand it that routine's SKILL.md as the run prompt.
OpenClaw logo

OpenClaw

by Open source community

OpenClaw is built around always on operation, with recurring work as cron jobs and the agent reachable from the chat apps you already use. The overnight and VPS patterns that operator kits teach map onto it directly. It is the harness this site covers most deeply.

Best for
Chat first, always on operator work across messaging surfaces
Running a scheduled routine
Reads the same SKILL.md format. Register each routine's schedule as an OpenClaw cron job pointed at the kit's routines folder.
Hermes logo

Hermes

by Nous Research

Hermes is the widest open harness in the field: many providers, many chat surfaces, browser and local runtimes, and a local proxy that lets other agent clients borrow its providers. Breadth is the point, and also the cost, so it is a better second harness than a first one.

Best for
Ambitious self hosters who want one harness to absorb the whole frontier
Running a scheduled routine
Point Hermes's recurring jobs at the kit's routines folder and mirror the cadence in SCHEDULE.md, one job per routine.
Pi logo

Pi

by Mario Zechner

Pi is a deliberately minimal terminal agent: extensions, skills and prompt templates, and not much else on purpose. It reads skills folders directly and runs headless with one flag, so recurring work goes through cron or Task Scheduler rather than a built in scheduler.

Best for
People who want to see every moving part
Running a scheduled routine
Reads skills folders directly and runs headless with pi -p. Mirror the schedule table into the operating system's scheduler, one job per routine, with the kit folder as the working directory.
Cline logo

Cline

by Cline

Cline started as the VS Code extension many people met agents through, and now ships a CLI with its own cron for agents that run without a terminal open. Open source, any model provider, and the shortest walk for an editor first workflow.

Best for
Anyone who already lives in VS Code all day
Running a scheduled routine
Register one scheduled task per routine with cline schedule create, handed that routine's SKILL.md as the prompt, with auto approve on so a run never hangs.
Qwen Code logo

Qwen Code

by Alibaba

Alibaba's open source terminal agent is tuned for the Qwen models but takes other providers' keys too. It reads skills, has scheduled tasks and runs headless. The Qwen models are among the cheapest that can do this work, which makes it the budget pick.

Best for
Operators whose first question is cost per run
Running a scheduled routine
Reads the skill format and ships scheduled tasks. Register one per routine, or drive qwen -p from the operating system's scheduler, one run per routine folder.
DeepSeek logo

DeepSeek

by DeepSeekSince August 2026

DeepSeek's harness runs as dsh and arrived in August 2026 as an open source system where everything is a plugin: models, tools, skills, scheduling, even the interface. It runs a local server with a web interface rather than a chat in the terminal. Newest on this list and moving fastest.

Best for
Early adopters who want a plugin shaped harness to watch
Running a scheduled routine
dsh runs a local server and scheduling is one of its plugins. Register one scheduled run per routine, handed that routine's SKILL.md as the prompt, with the kit folder as the working directory.
Antigravity logo

Antigravity

by Google

Antigravity is Google's agent first development environment: agents plan, run and verify work across the editor, a terminal and a browser, and report back with artifacts a person can review. Its agy command line runs the same agents headless, which is what makes scheduled routines possible on it.

Best for
Teams on Google's stack who want an editor built around agents and a job runner for the scheduled work
Running a scheduled routine
Schedule each routine with the agy job runner, pointed at the routine folder rather than registered as a global pack.

The skill format is the portable layer

A skill is a folder with a file in it, SKILL.md, that says how one job gets done. Once installed, the agent reads it whenever that job comes up. By the third time you explain the same process to an agent, you should stop explaining and start installing.

Every harness on this page reads that folder. The differences that matter are mostly naming: Claude Code calls recurring work scheduled tasks, OpenClaw calls them cron jobs, Cline has its own cron in the CLI, and Pi hands the schedule to the operating system. They function the same way.

One rule holds everywhere: a scheduled routine is pointed at its own folder, never copied into a global skills directory. Routines are scheduled work, not on demand skills.

Use a second harness for second opinions

An agent does not have to be the only one working. It can drive another harness's command line, hand off a job, keep working while that job runs, and read the result when it comes back. Three reasons to set this up:

  1. A genuinely independent set of eyes. A second opinion from the same model in the same session shares its context and blind spots. A different model reading the same code fails in different places, which is the entire value of a review.
  2. Parallelism. A full repository audit runs in the background while the main agent stays responsive.
  3. Cost spread across subscriptions you already pay for. Long audits and research sweeps go to a provider you are already subscribed to, so the primary tokens go on orchestration and verification.

Choosing in sixty seconds

The decision guide from Reinventing.AI's Agent Ops Masterclass, where the harness lesson opens by deflating the question: everything the club ships runs on all of these.

  1. 01
    Already paying for Claude?
    Use Claude Code. Stop reading, go to the next section.
  2. 02
    Already deep in OpenAI, or the team is?
    Use Codex.
  3. 03
    Starting from nothing and want the machine to keep running without you?
    Look at Grok Bot first.
  4. 04
    Need to switch models freely?
    OpenCode.
  5. 05
    Already in VS Code all day?
    Cline.
  6. 06
    Still undecided after sixty seconds?
    Claude Code. The harness you start today beats the harness you research for a week. Every hour spent comparing is an hour your first routine is not running.

The ten minute test

Before committing to any of them, take any skill folder, hand it to the harness, and ask it to summarise what the skill does and when it would use it. A harness that reads the folder correctly will run everything else. A harness that cannot is telling you in ten minutes what you would otherwise learn over a week.

The cloud harness tradeoff

A cloud harness signs into your accounts from a machine you do not control. That is a real tradeoff rather than a dealbreaker. Handle it the way you would handle a contractor with credentials: a separate browser profile, no password manager attached, and access to nothing you would not hand a new hire on their first morning.

Frequently asked questions

What is an AI agent harness?

A harness is the program an AI agent lives in: the chat, the file access, the tools, the scheduler and the approval gate around the model. The model reasons; the harness decides how instructions are read, how tools run, how work persists between sessions and how the agent reaches a person. That layer is where practical value is won or lost.

Which AI agent harness should a beginner choose?

The Agent Ops Masterclass recommends Claude Code for almost everyone, through the Code tab in Claude Desktop, because every published operator kit was built and tested there and there is nothing to configure. Pick Codex if the team already lives on OpenAI, Grok Bot if the machine needs to keep running without you, OpenCode to switch models freely, and Cline if you spend the day in VS Code.

Do skills and routines transfer between harnesses?

Yes. Every harness on this page reads the Claude Code skill format: a folder with a SKILL.md file describing one job. Standards, skills and routines move with you, so the harness is a residence rather than a marriage. The differences are mostly naming: Claude Code calls recurring work scheduled tasks and OpenClaw calls the same thing cron jobs.

Which harnesses run in the cloud instead of on my machine?

Grok Bot is the one on this page that gets its own cloud computer, a virtual machine with a browser, a terminal and file access that keeps working while your laptop is shut. OpenClaw and Hermes are commonly run on a VPS for the same always on effect, but you set that up yourself.

Which harness is cheapest to run?

Qwen Code, because the Qwen models it is tuned for are among the cheapest that can do agent work, and it accepts other providers' keys when a task needs a stronger model. Open source harnesses like OpenClaw, OpenCode, Hermes, Pi and Cline cost nothing themselves; you pay only for the model API.

Can one harness use another as a second opinion?

Yes, and it is worth setting up. An agent can drive another harness's command line, hand off a job, keep working while it runs, and read the result. A different model reading the same code fails in different places, which is the whole value of a review, and it spreads cost across subscriptions you already pay for.

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