AI agents dominate the conversation. Most tutorials start with a paid API key. This one starts with nothing. The goal is a working agent workspace at zero cost.
MonkeyCode is an open-source project. It bundles free model access and a free server into one CLI. The free tier currently includes 10 million tokens and a remote sandbox. Disclosure: This article was prepared as part of MonkeyCode's product outreach. The commands below follow the project's public documentation as of August 2026. Terms change. Check the repository before relying on any number in this article.
The current agent wave assumes a budget. Reasoning loops, tool calls, memory layers — every step burns tokens. The reasoning-ledger pattern is a good example. It records every agent decision. Each entry costs tokens. A free tier changes the equation. It turns experimentation from a cost decision into a time decision. That shift matters more than any model benchmark. A developer can test an agent loop before committing a single dollar.
This tutorial builds a working agent workspace from zero. Each stage ends with a verification step. If a stage fails, the next stage will fail too. That is the point.
Stage 1: Install the CLI
Prerequisites are minimal. A laptop with Node.js 18 or newer. Git for cloning. A terminal. No GPU. No cloud account. No credit card.
A local install matters for one reason. It keeps the setup inspectable. Every dependency stays visible. Every failure stays debuggable.
Clone the repository and install the CLI.
git clone <monkeycode-repo-url>
cd monkeycode
npm install
npm link
Verify the installation with a version check.
monkeycode --version
A version string means the tool is ready. An empty response means a broken path. Fix the path before moving on.
Stage 2: Configure free model access
The free tier includes 10 million tokens. The CLI needs an account and a free-tier configuration.
monkeycode login
monkeycode config set model-tier free
Verify access with a minimal prompt.
monkeycode run "Reply with the word ready."
The expected output is ready. The command also prints token usage. Record that number. It becomes the baseline for every later measurement.
Stage 3: Provision the free server
The free server is a remote sandbox. It runs the agent loop without loading the local machine.
monkeycode server create --tier free
monkeycode server status
Verify with the status command. A healthy server shows a ready state and a region. A pending state means the sandbox is still provisioning. Wait a minute and check again.
Stage 4: Run a real task
A free tier proves nothing with hello-world prompts. A file-fixing task is the right test case. It exercises the full agent loop. The agent must read a file, run code, form a hypothesis, edit, and re-run. A hello-world prompt only tests the network. A file-fixing task tests the loop.
The task file is the contract. It defines success before the agent starts. A vague task produces a vague result. Write the expected behavior explicitly.
Create a task file.
# Task: fix the calculator
The file scripts/calc.js has a bug. It returns 3 for the input 1+2.
Run the file, find the bug, fix it, and run the test.
Run the agent against the task.
monkeycode agent run --task tasks/fix-bug.md --server free
Verify the result three ways. First, the agent reports a completed status. Second, the test passes. Third, the diff contains only the intended change.
node scripts/calc.js
node tests/calc.test.js
git diff
A clean diff is the strongest signal. An agent that rewrites unrelated files is worse than an agent that fails.
Stage 5: Measure the cost
Free tiers disappear when teams cannot see usage. The CLI reports tokens per run.
monkeycode run --stats
Compare the number with the baseline from stage two. A fix task should cost a few thousand tokens. A task that burns hundreds of thousands signals a broken loop. Stop and inspect the prompt before spending more.
Token math matters. Ten million tokens sounds large. A single agent run with tool calls can consume five thousand tokens per step. A ten-step loop burns fifty thousand. That leaves room for roughly two hundred experiments. Budget them like test cases, not like free candy.
Limitations
This approach has limits. The free tier is for experimentation, not production. Ten million tokens disappear fast in a long agent loop. The free server has no uptime guarantee. Do not put sensitive data in a task file. Do not build a customer-facing product on this tier.
Free tiers also change without notice. The 10-million figure is the offer as of August 2026. The project may adjust quotas, regions, or model availability. Read the changelog before each session. Treat the free tier as a moving target.
Some teams should skip this workflow. Teams that need guaranteed uptime. Teams processing private data. Anyone running batch jobs that must finish overnight. The free tier is a learning tool, not a platform.
When a stage fails, do not skip it. A failed login produces a failed config. A failed config produces a failed server. A failed server produces a failed task. The error message usually points to the first broken stage. Read the full log. The last line is rarely the cause.
The one-hour test
The best way to judge a free tier is to run it. This tutorial gives a complete path: install, configure, provision, run, measure. One hour of work produces a measurable baseline. The next step is to break the agent on purpose. Feed it a vague task and watch how the free tier behaves. That test will tell more than any marketing page.
The agent conversation will keep moving. New frameworks will appear every week. The skill that survives is measurement. A free tier gives every developer the same starting line. Use it.
Try it. The cost is an hour of your time. The result is a number you can trust.
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