AI Agent Free and Unlimited: No Session or Token Limits

You can run a full AI coding agent on your own machine right now, for free. Not a trial. Not a demo. A real agent that reads your files, edits them, runs commands, and checks its own work.

One model on the free shelf says: no session limits, no token limits, code all day. The five next to it say: enjoy the daily cap, see you at midnight. This post is about proving the first one is real, then mapping the rest.

Promises are cheap. So instead of asking you to believe me, I will hand a broken repository to this free agent and let it work while you watch. By the end you will know where every free model on OpenCode Zen hides and what each one costs you in fine print. If you want the full picture first, the setup guide for this stack covers installation and the money story behind it: why free exists and what it replaces. Here, we prove the agent works.

Hand over the whole repo

It is eleven at night. My test suite is red. The failing file is one I barely remember writing.

The patient: a TypeScript invoicing module. Its tests were green two days ago. Then a renaming refactor passed through, one file got missed, and the suite broke. Classic drift.

I typed one sentence into the agent's prompt box:

npm test is failing. Find out why and fix it.

Notice what I did not include. No file name. No error text. No theory about the cause.

The agent started by running the tests itself:

FAIL  src/utils/format.test.ts
Error: TS2305: Module '"./format"' has no exported member 'formatInvoiceTotal'.

Then it went looking. It opened the format module, listed the current exports, found formatCurrency sitting where the old name used to be, and searched the codebase for every caller of formatInvoiceTotal. One stale import in the test file. It fixed that line, ran the whole suite again without being asked, and reported back:

Tests: 23 passed, 23 total. All green.

Nobody told it which file hurt. It read the situation itself, formed a plan, and closed the loop. Total time: about ninety seconds. My old record for this type of bug was twenty minutes, most of it spent copying text between windows.

Watch the loop work

What you just saw deserves a name. An AI agent is a language model connected to tools, running in a loop: think, act, observe, repeat.

• It thought about the error.
• It acted by running commands and editing a file.
• It observed the fresh test output.
• It repeated until the goal was met.

Three things make this loop possible, and each one separates an agent from a chatbot:

• Tools give it hands. Reading files, searching code, running shell commands, editing lines. A chatbot describes. An agent does.
• Session memory gives it continuity. Everything it learned in step one stays known in step four. You never re-explain context between messages.
• Autonomy gives it sequencing. I never said "first run the tests, then search for callers." It chose the order from what it observed. Choosing steps is where most of the value lives.

One line sums it up: if software takes a goal, plans its own steps, uses tools, and checks its own results, it is an agent. If it waits for context and returns text, it is a chat.

One bug fixed. Here is the rest of the menu

Bug hunting makes the best demo because success is easy to verify. The same loop drives bigger jobs:

Take a product from idea to internet:

• Plan: describe the product, get architecture and file structure back for approval.
• Build: approve, and it writes every file.
• Test: it writes tests, runs them, fixes failures.
• Deploy: static site to Vercel, GitHub Action, container.

Plan, build, test, deploy. The whole pipeline in one conversation, with you steering at the decision points.

Daily jobs fit the same loop:

• Write and repair tests, including edge cases you would rather not enumerate by hand.
• Explain unfamiliar code, answering from your actual files when you reference them with @.
• Run the chores: dependency upgrades, release notes, renames across thirty files.
• Automate your repetition: anything you do weekly becomes a script it writes while you drink coffee.

The pattern: wherever "done" can be checked objectively, agents pay rent fastest.

Where it still fails

Trust needs the failure list too:

• Wrong with confidence. A fix can compile, pass the narrow case, and still miss the point. Every meaningful change gets my eyes before commit.
• Context has edges. Whole-repo awareness is real. Whole-company awareness is not.
• Security stays yours. An agent will paste whatever a file holds into whatever tool you point it at. Secrets stay out of scope because you enforce it.
• Scope matters. Clear goals with checkable outcomes suit agents. Deciding what to build stays human.

More free fuel sits in OpenCode Zen

The unlimited model I used is called Ox Alpha Free, served through OpenCode Zen, the model gateway built into the agent. And here is the part almost nobody mentions: Zen carries more free models than this one.

Each one is free for a limited time while its team collects feedback. Each one has a daily cap that resets at midnight, your local time:

Three facts worth holding onto:

• The daily caps reset every night at midnight, local time. Hit the wall, sleep, continue.
• OpenCode does not publish the exact cap sizes. Community measurements land around 0.3 to 0.5 million tokens per day per model. Treat that as an estimate, not a promise.
• Ox Alpha Free plays by different rules. Its provider states zero retention and no training on your prompts, and during the preview window it does not count against the paid-tier allowance either. That combination is why it unlocked unlimited for me.

So the practical play is simple. Drive Ox Alpha Free all day without watching a meter. If it ever disappears from the list tomorrow, the other five free models are sitting right there under /models, each refilled by midnight.

The next red test is yours to hand over

Back to eleven at night. Red tests still happen, and they always will. What changed is the next ten minutes. Mine stopped belonging to copy-paste translation and started belonging to a loop that reads the error, finds the cause, fixes it, proves the fix, and reports back. On a stack that costs nothing and watches nothing.

If you want this running on your machine tonight, I documented every step, including how the unlimited model got unlocked, in the ten-minute setup guide for this exact free stack. No credit card anywhere in it.

Then tonight, when your tests go red, skip the chat window. Hand over the whole repo.

🎯 What is in my hands now!

• Ox Alpha Free runs with no session limits and no token limits; every other free model on Zen refills at midnight.
• One sentence was enough: the agent ran the tests, found the stale import, fixed it, and proved it.
• Agents cover the whole pipeline now: plan the product, build it, test it, deploy it.
• OpenCode Zen hides more free models behind /models, each with a daily cap that resets at midnight.
• Free has fine print: several free models train on your prompts; Ox Alpha Free states zero retention.
• Checkable goals are agent fuel: green tests beat good intentions.