Aubia · Phase 0 · Pre-launch
The dawn of multi-agent code.
Claude implements, Codex reviews, Mistral tests. You arbitrate, Aubia executes.
- Claude Code
- Codex CLI
- Mistral
- Cursorsoon
- Geminisoon
- Ollamasoon
Preview
Several agents, one interface.
From multi-provider synthesis to supervised execution, everything goes through the same view. You see who is doing what, where it stands, and who reviewed whom.
Preview of Opinions-to-Executable-Plan mode: three AI agents (claude-sonnet-4.6, gpt-5.5-instant, mistral-medium-3.5) analyse encrypted token storage in parallel. Each column presents a distinct opinion with a code snippet: Laravel Crypt Eloquent cast, custom readonly DTO with EncryptedString cast, or OS keychain via NativePHP SecureStorage. The Synthesis column summarises two convergences (AES-256-GCM, never serialise in plaintext), one divergence on storage, and proposes an Executable Plan with 4 numbered tasks ready for approval.
Laravel Crypt via Eloquent cast.
use Illuminate\Support\Facades\Crypt;
protected $casts = [
'token' => EncryptedString::class,
];Readonly DTO with EncryptedString cast.
final readonly class TokenData
{
public function __construct(
public string $token,
public string $provider,
) {}
public static function fromArray(array $data): self
{
return new self(
token: Crypt::decryptString($data['token']),
provider: $data['provider'],
);
}
}NativePHP SecureStorage, OS keychain only.
use Native\Laravel\SecureStorage;
SecureStorage::put('provider.'.$id, $token);
$token = SecureStorage::get('provider.'.$id);- · AES-256-GCM required
- · Never plaintext in logs or serialisation
- · Storage: Eloquent cast / DTO / keychain
- 1Create Providers/Casts/EncryptedString
- 2Refactor StoreProviderToken Action
- 3Cast ProviderConfig::$token
- 4Pest: EncryptedTokenTest
Three agents answer the same prompt. Aubia extracts convergences, marks divergences, generates an executable plan you approve or adjust.
Preview of Cross-review Mode: the implementation agent (claude) refactors StoreProviderToken into a final readonly class with a TokenData DTO. Diff +28 lines, -4 lines on 1 file in 47 seconds. The reviewer agent (codex) marks the status needs_changes and flags two issues: missing DB transaction around save() which may leave an orphaned state, and forgetting to emit the ProviderConfigured event needed to invalidate cached configs. Iteration 2 of 3 sent to claude.
- Missing DB transaction around save(): risk of orphaned state.
- Emit ProviderConfigured to invalidate cached configs.
A model from another family reviews every implementation. The agent iterates until agreement, or escalates the disagreement for arbitration. This is what current orchestrators do not do.
Preview of Kanban board: 6 active sessions spread across three columns. In progress: claude on EncryptedString cast, codex on Cast ProviderConfig. In review: the StoreProviderToken card (claude → codex) marked needs_changes. Done: Wire shadcn Dialog, Provider migration, Saloon GitHub. Window footer: drag to sort · providers-encryption · main.
EncryptedString cast
Cast ProviderConfig
StoreProviderToken
Wire shadcn Dialog
Provider migration
Saloon GitHub
Each AI agent advances on its task, in parallel. Drag a card to approve a review, send it back to the backlog, or mark it as blocked.
Features
What Aubia adds to your AI agents.
Aubia is a desktop application that runs several AI agents on your code, in parallel, and makes them review and challenge each other. Here is what you can do from the very first beta.
Opinions to Executable Plan Mode
Submit a technical decision to multiple models simultaneously. Each argues its position. Aubia synthesises the convergences, isolates the disagreements, and generates an arbitrated, executable action plan. You decide. No more typing.
Several agents, in parallel
Several agents run simultaneously on the same Git repository, each in its own isolated git worktree. Claude, Codex and Mistral analyse the same problem without stepping on each other.
A cross-review running in a loop
The implementer and the reviewer exchange the diff until convergence. You set the number of iterations, Aubia orchestrates them.
One pipeline, several providers
Claude Code, Codex CLI, Mistral. Tomorrow Gemini, local Ollama and your favourite providers. Plug in your own API keys, no vendor lock-in.
Kanban + per-agent terminal
All your sessions in a single view. Kanban for the global state, integrated terminal for agents currently running.
Vision
Why Aubia exists.
Four convictions that drive our technical choices, from the first commit to the features still to come. Before knowing what Aubia does, we want you to know why.
A single model is a single blind spot.
A model does not audit its own biases. As long as the same agent implements then reviews, errors loop back. The second reading can only come from elsewhere: another family, another architecture, another angle.
One prompt, multiple opinions, one Executable Plan.
A senior dev does not code alone in their head: they consult, confront, decide. AI agents should work the same way. Your role is no longer to implement line by line, it is to arbitrate between several proposals and decide on the direction.
A pipeline that works once must work a thousand times.
A sequence of agents that solves a problem well is an asset. It must be saveable, versionable, shareable, replayable on other projects. A well-crafted prompt is worth as much as a well-written function.
Your code stays on your machine.
Aubia runs locally. Your API keys are encrypted in the OS keychain. Your sources never transit our servers. No mandatory intermediary between your code and your providers.
Roadmap
Version 1.0 is built before your eyes.
Each 0.x version unlocks a feature, all the way to 1.0. You install Aubia once. The waves arrive without migration nor pain.
Cross-review as a multi-step DAG
Cross-review extended to graphs of arbitrary agents. Claude implements, Codex and Mistral review in parallel, Gemini arbitrates the disagreements, a final agent merges. Every step, every branch, every condition, under your control.
Visual pipeline editor
Branches, conditions, fan-out/fan-in. Build arbitrary pipelines with the mouse.
Manual plan editor
Write an Executable Plan directly, without going through the Opinions-to-Plan phase. Automatic Cross-review per task.
Reusable prompts
Your favourite templates, versioned and shareable in one click.
100% local inference
Llama, Qwen, Gemma via Ollama, with MLX acceleration on Apple Silicon.
Multi-machine sync
Themes, shortcuts, configs and templates follow your machines: personal, work, backup laptop.
MCP integration
Aubia becomes a tool inside Cursor, Zed, Claude Desktop. An agent callable as an MCP tool.
CI auto-fix
Pipeline that fails, an agent retrieves the logs, fixes, restarts. Notification after 3 attempts.
Budgets and cost alerts
Track what your pipelines cost, by provider and by project. Alerts before overrun.
Gemini CLI provider
Google Gemini joins the roster. One more model family for your cross-reviews.
Mobile companion
Track your sessions from your phone. Launch at the office, resume on the couch.
The other orchestrators run your AI agents. Aubia makes them challenge each other, synthesises them, composes them into shareable pipelines.
Request early accessCreator's letter
A project that refuses to bet on a single model.
I've always been passionate about new technologies, and web development in particular: it lets you deliver your work directly to users. To me, every line of code is an expression of craft. It's in that spirit that I designed Global-Exam's information system from scratch, then CEGELEM's.
Everything shifted in February 2025, with the first preview of Claude Code. Codex CLI followed shortly after, and very quickly, having to switch between them constantly felt wasteful: two model families at hand, one viewpoint at a time.
Over time, one thing became obvious. The easiest way to miss an error is to be the one who wrote it. And it holds just as true for an agent: an agentic tool tied to a single provider is a dead end. Claude misses details Codex catches at first glance, and Codex drives straight to an implementation where Claude would have challenged the approach. That's why, in a mono-provider pipeline, the same biases review the code at every step and errors slip silently from one agent to the next.
Aubia grew out of that frustration of not being able to make several LLM families work together, in the same session. At first, I built this project to help me with my own daily tasks. But if this need drives me enough to pour that much energy into it, I'm probably not the only one who feels it.
It reminds me of multi-cloud for infrastructure: multi-provider plays the exact same role for agentic workflows. Betting everything on one ecosystem means inheriting its access policy, pricing, quotas and roadmap. By contrast, Aubia makes the models review each other: a bug Claude introduces, Codex catches. An architecture Codex accepts, Claude challenges. A second reading, by another family of models.
That's the bet: your AI agents review each other, whichever provider they run on. So let's take this journey together, because your feedback will shape what comes next.
Talk soon,
Ready to have your AI agents review each other?
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