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Spec-driven development · Multi-agent orchestration

Aigon ... if Trello and GitHub Copilot had a baby ... but you choose your agents.

You bring your own API keys and models, run multiple agents in parallel, and use LLMs to judge and refine each task—on your terms.

Aigon ... if Trello and Cursor had a baby ... but you're in full control.

You bring your own model subscriptions and run tasks through multiple agents in parallel, comparing and refining outcomes.

Aigon ... if Linear and Claude Code had a baby ... and they had triplets.

You can stick with Claude Code as the star sibling, or bring in more brothers, sisters, and cousins—Aigon coordinates them all, making your workflow a true family effort.

Aigon Dashboard Kanban board — feature cards across inbox, backlog, in-progress, evaluation, and done columns

Start in 5 minutes How it works

Work visually with a Kanban board, drop to the CLI for scripting, or drive from slash commands inside your agent — one workflow at every level of control
Four clear modes: Drive, Fleet, Autopilot, and Swarm
Specs, logs, and research findings in Git — searchable, portable, permanent
Works with Claude, Gemini, Cursor, and Codex — swap freely or run them head-to-head
No required SaaS account, plain files in Git, no proprietary formats

Claude Code and Cursor compete, Codex evaluates — best implementation wins.

1. Fleet started — agents implementing in parallel

Fleet started — Codex and Gemini implementing in parallel

2. Both submitted — Codex evaluating

Claude Code evaluating the implementations

3. Evaluation complete — winner, Claude Code merged, with the best from Cursor implementation

Evaluation complete — winner selected and merged

The problem

AI agents multiply output fast, but coordination breaks down even faster.

Terminal tabs everywhere

Parallel agents generate momentum, but tracking what each one did and why becomes manual overhead.

Decisions get lost

Without spec-linked workflows, merge decisions and implementation tradeoffs disappear into chat history.

No clean comparison step

Teams rarely compare multiple implementations side-by-side before merging the best approach.

Feedback loops stay disconnected

User feedback, research, and implementation often live in separate tools with no shared lifecycle.

CLI in action

Slash commands inside your agent — one workflow at every level of control.

Value proposition

Aigon turns multi-agent output into an auditable delivery system.

Traceability

Specs and decisions live in your repo

Research, feature specs, implementation logs, and evaluations stay in Markdown files your team can review and version.

docs/specs/features/03-in-progress/
docs/specs/features/logs/

Vendor independent

Use the agents you already prefer

Run the same workflow with Claude, Gemini, Cursor, and Codex without rewriting your process around one tool.

aigon install-agent cc gg cx cu

Shared lifecycle

Research, delivery, and feedback connect end to end

Aigon links discovery, implementation, review, and follow-up so each cycle improves the next one.

research -> feature -> eval -> close

Operational clarity

Mode-based commands reduce team confusion

Pick the right execution mode for each task and make expected behavior explicit before coding starts.

aigon feedback-triage 14

How it works

Research, build, learn — on repeat.

Choose your mode

Hands-on or hands-off, one agent or many.

Hands-on + one agent

Drive mode

Use when you want tight control over implementation details and review checkpoints.

aigon feature-start 07

Outcome: one guided implementation branch with a full spec and log trail.

Hands-on + multi-agent

Fleet mode

Orchestrate competing implementations you can evaluate and adopt the best from.

aigon feature-start 07 cc gg cx

Outcome: parallel worktrees and comparable outputs for structured selection.

Hands-off + one agent

Autonomous mode, Single Agent

Use when the scope is clear, but you want the peace of mind of a reviewing agent.

aigon feature-autonomous-start 07 cx --review-agent=cc --stop-after=closed

Outcome: automated implement-validate loop that stops when checks pass or budget ends.

Hands-off + multi-agent

Autonomous mode, Multi Agent

Fully orchestrated, fully autonomous — parallel agent runs converge into comparable outputs ready for auto evaluation and completion.

aigon feature-autonomous-start 07 gg cx --eval-agent=cc --stop-after=closed

Outcome: concurrent autonomous runs across agents with logs ready for comparison.

The steps

From idea to merged code.

Create a feature spec and move it into the backlog.

Your workflow, visualised

A Kanban board for spec-driven development.

The Aigon Dashboard is the visual way into your spec-driven workflow. Same pipeline, same agents — but managed through a browser UI instead of CLI commands. Drag features across columns, launch agent sessions with one click, and watch your pipeline move in real time. No terminal required.

Aigon Dashboard Kanban board showing feature cards in inbox, backlog, in-progress, evaluation, and done columns

Visual Workflow

Drag specs from inbox to done

Move features through your development pipeline with a familiar Kanban interface. Every column maps to an Aigon workflow state — no commands to memorise.

Monitor

See every agent, every repo, at a glance

The monitor tab shows running sessions, attention items, and recent events across all your repositories — everything you need to know at a glance.

Reports

Track what matters in your AI workflow

Five aligned charts — features completed, commits, cycle time, commits per feature, and rework ratio — show whether your team is shipping faster, writing tighter code, and fixing less. Summary cards, agent leaderboard, and filterable detail tables give you the full picture.

Telemetry

Cost & token visibility across all agents

Track token usage and cost across Claude, Gemini, and Codex sessions — broken down by activity type (implement, evaluate, review) and attributed per agent. See exactly where your spend goes. Learn more →

Remote Access

Monitor agents from your phone

The dashboard runs on your local network — open it on your phone or tablet over LAN, or use Tailscale to check on your agents from anywhere. Monitor from the couch.

Aigon Dashboard viewed on iPhone Safari showing active agent sessions across multiple repositories

Peeking into a live Claude agent session from iPhone — viewing real-time tool calls and reasoning

Workflow Intelligence

Know if your AI workflow is actually working

Five aligned charts track the metrics that matter: are features shipping faster? Are agents producing cleaner code? Is rework going down? Reports gives you the data to answer these questions — across every repo, every agent, every period.

Aigon Reports — five aligned charts showing features completed, commits, cycle time, commits per feature, and rework ratio

Feature velocity

Features completed and commits over time — stacked to show feature-attributed vs non-feature work.

Cycle time & focus

Median cycle time and commits-per-feature trending over time. See whether features are getting tighter.

Rework ratio

Percentage of fix commits per period. Trending down means agents are landing correct code on the first pass.

Documentation

Install in minutes, then run your first end-to-end loop.

git clone https://github.com/jayvee/aigon.git
cd aigon
npm install
npm link
cd /path/to/your/project
aigon init
aigon install-agent cc gg cx cu

Then, in Claude Code:

/aigon:feature-now dark-mode
Implement a dark mode capability to the website,
with dark mode/light mode toggle in the top right
position of the menu bar

Workflow Guide

Research, specs, implementation, evaluation, and completion flow.

CLI Commands

Every Aigon CLI command, organised by feature, research, feedback, and infrastructure.

Tech & philosophy

Open source, git-native, and intentionally simple.

Aigon is built for teams who want disciplined AI-assisted engineering, not opaque automation.

Open SourceMIT licensed, no paid-tier lockouts.
Repo-Native ContextSpecs, logs, and evaluations stay as plain Markdown in your repository.
Agent-AgnosticWorks with whichever coding agents your team chooses.
Adapts to Your StackWorkflow templates and defaults adjust for web apps, APIs, iOS, Android, and libraries.

Community

Help shape the next generation of collaborative AI development.

Contribute specs, improve workflows, and share real-world patterns for running multi-agent engineering teams effectively.

Star on GitHub Install guide

Aigon Pro

Go deeper
than the dashboard.

Agent quality metrics, synchronized trend charts, and AI-powered coaching — so you know which agents deliver, how your workflow evolves, and where to improve.

First-pass rate, rework ratio & agent leaderboard
Five synchronized trend charts — cycle time, commits, rework
AI-generated observations & coaching
Token usage & cost attributed per agent and phase

Explore Aigon Pro

Aigon Pro — AI-powered insights and coaching dashboard