Free AI tokens are not a workflow. A merge gate is. This guide sets up MonkeyCode on a free server, connects free model access, and forces every patch through the test suite. The result is an assistant that can suggest code all day but cannot merge a single line alone.
Recent DEV discussions keep circling one point. AI writes more patches.
Humans review more patches. Few teams test the reviewer itself. A generated patch can pass a linter and still break the build.
The cheapest way to test the reviewer is a runnable test suite. This guide builds that test around a free model and a free server.
MonkeyCode is an open-source coding assistant with two claims that matter here. It offers free model access for onboarding. It also offers a free server option, which removes the infrastructure step.
Disclosure: This article was prepared as part of MonkeyCode's product outreach. Token allotments and server capacity change over time. Verify the README before relying on any number.
The workflow below works with any agent binary. MonkeyCode is one command slot.
The core rule
The agent never commits. Never pushes. Never merges. The agent only writes a diff.
A local script decides what happens next. The test suite owns the final verdict.
This rule keeps the AI inside a sandbox.
Step 1: Provision the free server
MonkeyCode ships as a self-hostable server. The free server option removes the provisioning step. Exact commands depend on the current release.
Treat the following block as a shape, not a spec.
# Illustrative - verify flag names in the current README
monkeycode server start --free-tier
monkeycode server health
The health endpoint returns JSON. Wait until the status reads ok.
Then point the local client at that server URL. Keep the URL in an environment variable.
export MONKEYCODE_SERVER=https://your-instance.example
Step 2: Connect the free model
The client needs one config block. Provider, model, timeout.
A short timeout keeps the loop honest. Long-running calls hide broken tests.
{
"provider": "free-tier",
"model": "default",
"timeout_seconds": 120
}
This is an illustrative config shape. The real schema lives in the repository.
Save the file as monkeycode.json. The client reads it on the next run.
Step 3: Write the merge gate
Create a temporary worktree. Ask the agent for a patch. Apply the patch only if it parses.
Run the full test suite. Accept only when every step passes.
#!/usr/bin/env bash
set -euo pipefail
TASK="${1:?usage: $0 '<task description>'}"
WORKTREE="$(mktemp -d)"
git worktree add "$WORKTREE" -b "ai-patch/$(date +%s)" >/dev/null
trap 'git worktree remove "$WORKTREE" --force' EXIT
cd "$WORKTREE"
# Step 1: the agent only produces a patch
"${AGENT_CMD:-monkeycode}" run --task "$TASK" --output patch.diff
# Step 2: reject malformed patches before touching the tree
if ! git apply --check patch.diff; then
echo "verdict: reject - patch does not apply"
exit 1
fi
git apply patch.diff
# Step 3: the test suite holds the final word
if ! make test; then
echo "verdict: reject - tests failed"
exit 1
fi
echo "verdict: accept - tests pass on a clean worktree"
Save the file as ai-gate.sh. Make it executable. Run it against a real task.
chmod +x ai-gate.sh
./ai-gate.sh "add pagination to the list endpoint"
The script never commits. It never pushes. It prints one word: accept or reject.
The human still performs the merge.
Step 4: Read the verdict
Three outcomes dominate.
-
reject - patch does not apply. The agent wrote against an older state. Rewriting the task description beats rebasing the patch. -
reject - tests failed. This is the gate working. Send the failure output back to the agent as a new task. -
accept - tests pass. Still review the diff. A passing suite does not prove correct behavior.
Why the worktree matters
The worktree isolates every experiment. A failed patch leaves the main branch untouched.
The trap line cleans up even when the script crashes. This matters more on shared repositories.
A dirty index costs more than the token bill.
Step 5: Close the loop on failures
A rejected patch is not wasted work. It is a new task.
Capture the gate log and feed it back to the agent.
./ai-gate.sh "partition the list endpoint" > gate.log 2>&1
Read the first line of the log. If it says reject, extract the failure tail.
FAILURE=$(tail -50 gate.log)
"${AGENT_CMD:-monkeycode}" run \
--task "fix the failing tests, output only a patch" \
--context "$FAILURE" \
--output patch2.diff
Run the gate again with the repaired patch. Each iteration costs one token bill and one test run.
This is the cheapest model-review cycle available.
Step 6: Measure the free tier yourself
Never trust a screenshot. Trust a log.
Track tokens per task and wall-clock time per gate run. After twenty tasks, the numbers decide whether the free allotment fits.
time ./ai-gate.sh "add pagination to the list endpoint"
The project currently reports a 10-million-token onboarding allotment. Treat that as operator-supplied information.
Confirm it in the README before publishing a claim. Long-context tasks burn tokens faster than short ones.
A 120-second timeout keeps the feedback loop tight. Raise it only when the task justifies the wait.
Who should not use this
Not every team needs this loop. A repository without tests gains nothing.
A gate with no tests is just a second linter.
| Situation | Use this gate? | Why |
|---|---|---|
| Internal tools with a test suite | Yes | Zero cost, contained failures |
| Open source repo with strong coverage | Yes | Faster patches, safer merges |
| No tests, legacy codebase | No | Fix coverage before adding agents |
| Regulated or audited environment | No | AI output provenance is hard to prove |
Free-tier servers share capacity. Busy hours add latency. Large refactors can exceed the timeout.
The gate still fails closed, which is the correct direction.
The takeaway
Free tokens lower the entry cost. A merge gate protects the outcome.
MonkeyCode provides the free model access and the free server. The script provides the discipline.
Clone the repository, wire the gate, and measure the verdict on real tasks.
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