Aigon GitHub

Spec-driven development for AI teams

Spec-driven AI development.
Your context, committed to code —
with any agent, or all of them at once.

Aigon structures the full product lifecycle — research, feature delivery, and user feedback — as specs and logs committed directly to your repository. Every decision is traceable, every implementation is reviewable, and your project memory stays portable across tools.

Start in 5 minutes Choose your mode
  • 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

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.

Value proposition

Aigon turns AI 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 -> done

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

Choose your mode

The 2x2 model: hands-on vs hands-off, one agent vs many.

Hands-on + one agent

Drive mode

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

aigon feature-setup 07

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

Hands-on + multi-agent

Fleet mode

Use when you want competing implementations you can evaluate and combine.

aigon feature-setup 07 cc gg cx

Outcome: parallel worktrees and comparable outputs for structured selection.

Hands-off + one agent

Autopilot mode

Use when the scope is clear and you want autonomous retries against validation checks.

aigon feature-implement 07 --autonomous

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

Hands-off + multi-agent

Swarm mode

Use when you want autonomous parallel exploration and then review converged outcomes.

aigon feature-setup 07 cc gg cx
# in each worktree:
aigon feature-implement 07 --autonomous --auto-submit

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

Terminal examples by mode

One canonical command path for each mode.

The big picture

Aigon keeps research, delivery, and feedback in one continuous system.

  1. Research

    Explore alternatives, constraints, and market context with multiple agents.

  2. Features

    Convert winning ideas into specs that define scope, intent, and success criteria.

  3. Code Review

    Evaluate implementations, merge the strongest option, and keep rationale in logs.

  4. Feedback

    Capture user signals and route them back into research and planning.

How it works

Core feature workflow in four explicit steps.

01

Define

aigon feature-create "jwt-auth"
aigon feature-prioritise jwt-auth

02

Set up mode

# Drive
aigon feature-setup 07
# Fleet
aigon feature-setup 07 cc gg cx
# Swarm
aigon feature-setup 07 cc gg cx
# then in each worktree:
aigon feature-implement 07 --autonomous --auto-submit

03

Implement

aigon worktree-open 07 --all
# then in each agent:
/aigon:feature-implement 07

04

Evaluate, merge, and adopt

# in your agent:
/aigon:feature-eval 07
aigon feature-done 07 cx --adopt
aigon feature-cleanup 07

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
aigon feature-now "dark-mode"

Workflow Guide

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

CLI README

Install instructions, command reference, and real examples.

Tool Comparisons

Where Aigon is stronger, where it is weaker, and how to combine tools.

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