Orchestration
A lead orchestrator splits the task into roles and assigns each a model and skills. Sub-agents run by dependencies.

Control plane for AI coding agents: multi-agent orchestration, a billing fallback chain, and cost control. Cheaper, more reliable, measurable.
A lead orchestrator splits the task into roles and assigns each a model and skills. Sub-agents run by dependencies.
Billing fallback chain: claude-code → wrangler → zen. Out of credits — the task doesn't fail.
Tokens, cost, and cache hits for every run. Spend limits, cost policy, and a savings report.
Apache 2.0, Python 3.10+. Project-agnostic: the target project is passed via --cwd.
One entry point — CLI, Web UI, or CI. From there, the route depends on task complexity.
The lead orchestrator assigns each role a model tier and skills. The flock flies by dependencies, passing results down the chain.
Out of billing — the next executor in the chain picks up the task. All three write files into --cwd.
Who runs the task and what happens when billing runs out.
| Executor | Writes files | Billing | In the chain |
|---|---|---|---|
| claude-code | yes | Anthropic | 1st |
| wrangler | yes | CF Workers AI | 2nd |
| zen | yes | free | 3rd |
| cursor | yes | Cursor | standalone |
| opencode | yes | opencode.ai | standalone |
| deepseek / mimo | no — text only | API | outside the chain |
VOLY counts every token: caching, routing, and fallback turn into concrete numbers, not gut feelings.
* example output — numbers depend on your tasks
A full control plane running locally in a couple of minutes. Requires Python 3.10+ and API keys in .env.
A complex task never goes to a single agent — it is decomposed into roles, each executed by a model of the right tier.
When the current executor runs out of money, VOLY automatically hands the task to the next one in the chain.
Every run leaves a TaskEvent: who executed it, which model, how many tokens, and how much money.
VOLY is not yet another agent — it is an open control plane between the developer and the agents.