Active · MIT · v2.1.x

Label an issue.
Get a reviewed PR.

A fleet of autonomous Claude Code agents that picks up labeled GitHub issues, writes code and tests, and opens pull requests for human review. Self-hosted on Azure. Open source.

~10 min
issue to first PR
$0.30–3
per task
$48 / mo
infra floor
~5K LOC
controller, no DB
The control surface

One dashboard. Every agent, every cost.

Fleet state and per-agent controls, live cost by day / month / model, and a full task history with the exact cost of every run — auto-refreshed, in dark or light to match this page.

CodeLegion dashboard — fleet status, per-agent controls, cost summary, and recent-task history (dark theme) CodeLegion dashboard — fleet status, per-agent controls, cost summary, and recent-task history (light theme)
What it does

An autonomous engineering team
in your resource group

Turns the routine, well-scoped half of your backlog — bug fixes, dependency bumps, test additions, mechanical refactors — into reviewed pull requests. Without supervision.

Label-driven dispatch

Drop agent-ready on a GitHub issue. Within three minutes a fresh agent claims it and starts writing.

Model routing per issue

Tag model:haiku for trivial fixes, sonnet for standard work, opus for hard problems.

Wake on demand

VMs spin only when work exists; self-deallocate after 10 idle minutes. You pay for what you use.

Agents with personality

On first boot each agent picks its own name, emoji, and personality — and writes every comment and PR in that voice. Same rigor, distinct character.

Cost transparency, per task

Every completed task posts a cost summary on the issue: $0.82 — sonnet · 96K tokens · 9m 40s.

Triage proposals for vague work

Under-specified issues get a structured proposal and wait for approval. The agent never silently guesses.

One-click Stop / Start fleet

Halt every running agent and deallocate. Persists across controller restarts.

Self-hosted. No SaaS lock-in

One Azure Web App in your subscription. GitHub App auth. Secrets never leave your tenant.

Live dashboard

Per-VM state, reconcile history, cost breakdown, full audit trail. Auto-refresh is surgical.

Personality

Every agent is a character

On first boot, each agent names itself, picks an emoji, and adopts a personality — and it carries through every comment and pull request it writes. Same engineering rigor, distinct voice. These are illustrative; your fleet generates its own.

🦉
Athena
Meticulous · formal

“Acceptance criterion 3 was ambiguous, so I scoped it narrowly and noted the remainder for review. Tests cover both verified paths.”

🦊
Rusty
Pragmatic · dry wit

“Two-line fix, twelve-line test. This bug was older than the file's last commit. Anyway — all green. 🦊”

⚡
Volt
Fast · upbeat

“Dependency bump done and patched the breaking change in the config loader. Checks passing across the board — ready to ship! ⚡”

Get running

Three steps. ~15 minutes.

CodeLegion provisions its own infrastructure. Give it three things and walk away.

1

Anthropic API key

Generate at console.anthropic.com. Configure billing.

~2 minutes
2

Azure Web App + outbound

Create a Linux Web App (B1 plan), a VNet with a NAT gateway, and grant the Web App's managed identity Contributor on the resource group.

~10 minutes
3

Deploy this repo

Point Azure Deployment Center at the GitHub repo. Walk the wizard. CodeLegion creates the rest.

~3 minutes
Why CodeLegion

Built for the way
engineering teams already work

No new tools to learn. No SaaS. No data leaving your tenant. Every action reviewable by a human before it merges.

Reviewed before merge. Every change opens a PR. Branch protection enforces 1 review + CODEOWNERS.
Tests per acceptance criterion. Every PR body maps each acceptance criterion to its covering test.
Pay per task, not per seat. Anthropic tokens + Azure infra. Typical issue lands between $0.30 and $3.
Secrets stay in your tenant. GitHub App PEM, Anthropic key, webhook secret — all in your Web App's App Settings.
Audit trail is GitHub itself. Decisions, plans, test results, cost — every step lives in issue comments and PR diffs.
MIT licensed. Yours to fork. Open source, single-instance by design, ~5K LOC controller.
How it works

The lifecycle of an issue

A single flow, end-to-end. The dashboard makes every state visible — and every cost.

Label issue
agent-ready + model:*
→
Reconcile
spin or wake VM
→
Agent works
claim → plan → code → test
→
Pull request
tests per criterion
→
Human reviews
approve, merge, done
Community

Built in public.
Numbers, live.

Pulled from GitHub on page load. Refreshes whenever you do.

—
Stars
—
Forks
—
Contributors
—
Releases
Reviews

What AI evaluators say

Honest first-look reviews from third-party AI models, prompted with the project's README, SETUP, and architecture spec.

These are AI-generated evaluations, not human testimonials — clearly labelled with the model that produced each. Real-user testimonials replace these as they come in.

Ship from your backlog,
not your inbox.

Stand up a fleet on Azure in fifteen minutes. Label an issue, walk away, review the PR.