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fifony

0.1.14-next.1a28dfb
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Filesystem-backed local Fifony orchestrator with a TypeScript CLI, MCP mode, and multi-agent Codex or Claude workflows.

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    Readme

    Fifony

    AI agents that actually ship code. You just watch.

    Point at a repo. Open the dashboard. AI plans, builds, and reviews — you approve.

    One command. Full orchestra.

    License Node


    Quick Start

    npx -y fifony --port 4040

    Open http://localhost:4040. First run launches the onboarding wizard — it detects your CLIs, scans your project, and configures everything.

    State lives in .fifony/. No accounts, no cloud, no setup.


    What Makes Fifony Different

    Mixed-Agent Pipeline

    The core idea: different AI providers handle different stages of a single task.

      Plan          Execute        Review
    ┌─────────┐   ┌─────────┐   ┌─────────┐
    │ Claude   │──▶│ Codex   │──▶│ Claude  │
    │ Opus 4.6 │   │         │   │ Sonnet  │
    │ high     │   │ medium  │   │ medium  │
    └─────────┘   └─────────┘   └─────────┘

    Claude plans. Codex executes. Claude reviews. Each stage gets its own provider, model, and reasoning effort — configurable per-project in Settings → Workflow.

    Onboarding Wizard

    First run detects your environment and walks you through setup:

    1. Pipeline — Choose which CLI runs each stage (planner, executor, reviewer)
    2. Project Scan — AI analyzes your codebase to detect language, stack, and domains
    3. Domains — Pre-selected by AI, 21 options across Technical/Industry/Role
    4. Agents & Skills — Curated catalog of 15 agents and 5 skills, auto-recommended by domain
    5. Effort — Per-stage reasoning effort, reactive to which CLI is selected
    6. Workers & Theme — Parallel worker count + visual theme

    Settings saved progressively. Re-run anytime from Settings.

    Language Agnostic

    The project scanner works with any codebase — it detects build files for 18+ ecosystems:

    package.json · Cargo.toml · pyproject.toml · go.mod · build.gradle · Gemfile · mix.exs · pubspec.yaml · CMakeLists.txt · composer.json · Package.swift · deno.json · pom.xml · Dockerfile · and more

    Uses the detected CLI with --reasoning-effort low for fast, accurate analysis.


    How It Works

    Planning → Todo → Queued → Running → In Review → Done
        ↑                                      ↓
        └──── Blocked ←── Rework ──────────────┘
    1. Create an Issue — Click "+", describe what you want done
    2. AI Plans It — Structured execution plan with steps, risks, file paths, complexity
    3. You Approve — Review the plan, approve → agents pick it up
    4. Agents Execute — Isolated workspace, live output streaming, PID tracking
    5. Automated Review — Diff inspection, approve/rework/block decision
    6. You Ship — Review the diff, merge

    Dashboard

    Page What you see
    Kanban Drag-and-drop board. Cards flow through the pipeline with state-colored borders and stagger animations.
    Issues Searchable list with descriptions, labels, token usage, duration, and filter chips by state.
    Agents Live cockpit with agent slots, real-time log output, queue, token usage sparkline.
    Settings Tabbed: General, Workflow (pipeline config), Notifications, Providers.

    Kanban Drag & Drop

    Drag issues between columns to change state. Works on desktop (click + drag) and mobile (long-press). Valid drop targets highlight green, invalid ones dim. State machine enforces valid transitions only.

    Micro-interactions

    Every interaction has visual feedback:

    • Cards lift on hover with state-colored left border
    • Running cards pulse with a breathing border glow
    • Buttons scale on press, hover lift
    • Toasts slide in with progress bar, typed as success/error/info
    • Drawers slide in/out with backdrop fade
    • View transitions fade between routes
    • Theme changes cross-fade in 300ms
    • Counters bounce when values change
    • Skeleton loaders shimmer during initial load
    • Empty states animate in with helpful guidance
    • Confetti bursts on issue creation

    PWA

    Install as a desktop app. Works offline. Desktop notifications when issues change state. Service worker with stale-while-revalidate caching.


    Agent & Skill Catalog

    Fifony ships with a curated catalog of specialist agents:

    Agent Domain
    Frontend Developer React, Vue, CSS, responsive design
    Backend Architect APIs, microservices, scalable systems
    Database Optimizer Schema design, query optimization, indexing
    Security Engineer OWASP, threat modeling, secure code review
    DevOps Automator CI/CD, Docker, Kubernetes, cloud infra
    Mobile App Builder iOS, Android, React Native, Flutter
    AI Engineer ML models, LLM integration, data pipelines
    UI Designer Visual design, component libraries, design systems
    UX Architect UX patterns, accessibility, information architecture
    Code Reviewer Code quality, best practices, constructive feedback
    Technical Writer Docs, READMEs, API references, tutorials
    SRE Reliability, observability, incident response
    Data Engineer ETL, data warehousing, analytics infrastructure
    Software Architect System design, DDD, architectural patterns
    Game Designer Game mechanics, level design, cross-engine

    Skills: commit, review-pr, debug, testing, impeccable (frontend design).

    Agents are installed to .claude/agents/ during onboarding. Compatible with both Claude Code and Codex CLI.


    MCP Server

    Use Fifony as tools inside your editor:

    npx -y fifony mcp
    {
      "mcpServers": {
        "fifony": {
          "command": "npx",
          "args": ["-y", "fifony", "mcp", "--workspace", "/path/to/repo"]
        }
      }
    }

    Create issues, check status, review workflows — without leaving the editor.


    API

    REST + WebSocket API with auto-generated OpenAPI docs:

    http://localhost:4040/docs
    Endpoint Description
    GET /api/state Runtime state with issues, metrics, config
    POST /api/issues/create Create a new issue
    POST /api/issues/:id/plan Generate AI execution plan
    POST /api/issues/:id/approve Approve plan, start execution
    GET /api/live/:id Live agent output (PID, log tail, elapsed)
    GET /api/diff/:id Git diff of workspace changes
    GET /api/config/workflow Pipeline workflow configuration
    GET /api/scan/project Project structure scan
    POST /api/scan/analyze AI-powered project analysis
    GET /api/catalog/agents Agent catalog (filterable by domain)
    POST /api/install/agents Install agents to project
    GET /api/settings All persisted settings
    /ws WebSocket for real-time state updates

    Run Modes

    # Full experience — dashboard + API + scheduler
    npx -y fifony --port 4040
    
    # Dev mode — Vite HMR on port+1
    npx -y fifony --port 4040 --dev
    
    # Headless — scheduler only, no UI
    npx -y fifony
    
    # MCP server — stdio for editor integration
    npx -y fifony mcp
    
    # Custom workspace
    npx -y fifony --workspace /path/to/repo --port 4040

    Architecture

    .fifony/                ← all state (gitignore it)
      s3db/                     ← durable database (issues, events, sessions, settings)
      source/                   ← codebase snapshot
      workspaces/               ← isolated per-issue agent workspaces

    Persistence: s3db.js — filesystem-backed database. Issues, events, settings, agent sessions — all persisted and recoverable across restarts.

    State Machine: Planning → Todo → Queued → Running → Interrupted → In Review → Blocked → Done → Cancelled

    Agent Protection: Detached child processes survive server restarts. PID tracking for recovery. Graceful shutdown marks running issues as Interrupted.

    Token Tracking: Per-model token usage with daily/weekly rollups and cost estimates.


    Credits

    Fifony is built on the shoulders of:


    License

    Apache License 2.0 — see LICENSE for details.

    This project includes code from OpenAI Codex CLI. See NOTICE for attribution.