Fusion

From rough idea to production code — automatically.

Multi-node agent orchestrator — tasks, agents, missions, git, files, and worktrees, with any model, local or cloud.

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Describe a task in plain language. A planning agent reads your project, understands context, and writes a full PROMPT.md plan — steps, file scope, acceptance criteria. Then Fusion plans, reviews, executes, and reviews again, in an isolated git worktree, with a human approval gate wherever you want one.

One board. Controlled from anywhere. Laptop, Mac mini, Linux server, cloud VM, phone — all connected.

Like Trello, but your tasks get specified, executed, and delivered by AI. Built on the great work of dustinbyrne/kb.

  ①  Describe          ②  Planning             ③  The board           ④  Isolated worktree
  ─────────────        ─────────────         ─────────────          ─────────────────────
  "Add dark mode   →   Agent writes    →   Plan → Review →    →   fusion/FN-123 branch
   toggle to           PROMPT.md           Execute → Review        concurrent, zero
   settings panel"     (steps, scope,      (per step, until        file conflicts
                       acceptance)         done)

Every task shows its plan, its reviews, its diffs, and its file changes in real time. Jump into an active task and nudge direction, tighten constraints, pause, or re-prompt.

graph TD
    H((You)) -->|rough idea| T["Planning<br/><i>auto-planning</i>"]
    T --> TD["Todo<br/><i>scheduled for execution</i>"]
    TD --> IP["In Progress<br/><i>for each step:<br/>plan, review, execute, review</i>"]

    subgraph IP["In Progress"]
        direction TD
        NS([Begin step]) --> P[Plan]
        P --> R1{Review}
        R1 -->|revise| P
        R1 -->|approve| E[Execute]
        E --> R2{Review}
        R2 -->|revise| E
        R2 -->|next step| NS
        R2 -->|rethink| P
    end

    R2 -->|done| IR["In Review<br/><i>ready to merge,<br/>or auto-complete</i>"]
    IR -->|direct squash merge<br/>or merged PR| D["Done"]

    style H fill:#161b22,stroke:#8b949e,color:#e6edf3
    style T fill:#2d2006,stroke:#d29922,color:#d29922
    style TD fill:#0d2044,stroke:#58a6ff,color:#58a6ff
    style IP fill:#1a0d2e,stroke:#bc8cff,color:#bc8cff
    style P fill:#1a0d2e,stroke:#bc8cff,color:#e6edf3
    style R1 fill:#1a0d2e,stroke:#bc8cff,color:#e6edf3
    style E fill:#1a0d2e,stroke:#bc8cff,color:#e6edf3
    style R2 fill:#1a0d2e,stroke:#bc8cff,color:#e6edf3
    style NS fill:#1a0d2e,stroke:#bc8cff,color:#bc8cff
    style IR fill:#0d2d16,stroke:#3fb950,color:#3fb950
    style D fill:#1a1a1a,stroke:#8b949e,color:#8b949e

Tasks with dependencies are processed sequentially. Independent tasks run in parallel. Optionally require manual approval before tasks move from Planning to Todo (requirePlanApproval setting).

Fusion workflows define how a task moves from idea to delivery. The default coding path is still the familiar Plan/Triage → Execute → Workflow steps → Review → Merge loop, but the policy now lives in a selectable workflow rather than being only hard-coded engine behavior.

• Select per task: choose a workflow from the dashboard task/board workflow controls, or assign one through fn_workflow_select / workflow_id when creating tasks.
• Built-in catalog: Coding (builtin:coding), Quick fix (builtin:quick-fix), Review-heavy (builtin:review-heavy), Compound engineering (builtin:compound-engineering, plugin-gated), Stepwise coding (builtin:stepwise-coding), and the PR lifecycle (builtin:pr-workflow, a reusable PR graph fragment).
• Customize safely: inspect built-ins, duplicate them, or author custom workflows in the visual Workflow Editor. Workflow-specific settings cover model lanes, review/approval policy, step execution knobs, task fields, and columns.

Read Workflow Steps for runtime semantics, built-in workflow behavior, and workflow-step templates; read Workflow Editor for the dashboard authoring guide.

Laptop, Mac mini, Linux server, cloud VM, phone — every node is a peer. Your task state, agents, logs, and diffs stay synchronized across the mesh. The same Fusion ships as:

• 🖥️ Desktop app — Electron for macOS (Intel + Apple Silicon), Windows 10/11, and Linux
• 📱 Mobile app — Capacitor for iOS/iPadOS and Android (MOBILE.md)
• 🌐 Web dashboard — any modern browser, served from the fn dashboard daemon
• 🔌 CLI — fn binary + extension for terminal-first workflows

Start the daemon on any node, connect your other devices, and the board follows you everywhere.

Import a team. Run it autonomously for weeks. 440+ agents across 16 companies, wired for missions, mailboxes, and inter-agent delegation.

npx companies.sh add paperclipai/companies/gstack

Fusion integrates with the tools you love. Hermes, Paperclip, and OpenClaw all ship as first-class plugins — route any workspace to whichever runtime fits the task. And any Paperclip agent-company imports with a single command.

_{Nous Research}

The open-source autonomous agent from Nous Research. Install the Hermes plugin and run agents through Hermes for long-running, context-growing work — route any Fusion workspace to it.

OpenClaw runtime support is available as an experimental plugin (fusion-plugin-openclaw-runtime) for runtime discovery/configuration parity. Configure agents with runtimeConfig.runtimeHint: "openclaw" after installing the plugin.

_{paperclip.ing}

The human control plane for AI labor. Install the Paperclip plugin to run agents through Paperclip inside Fusion.

Fusion also natively supports the companies.sh agent-company standard: import a prebuilt team — 440+ agents across 16 companies — and let them coordinate over Fusion's mailbox, missions, and workflow gates for weeks of autonomous work. Same company format, same agents, same skills as Paperclip.

npx companies.sh add paperclipai/companies/gstack

Hermes, Paperclip, and OpenClaw are experimental runtime plugins — APIs and wire formats may shift between minor releases.

Zero install, straight from npm:

npx runfusion.ai

That launches the dashboard. Subcommands forward through: npx runfusion.ai task create "fix X", npx runfusion.ai --help, etc. (Or verbosely: npx @runfusion/fusion dashboard.)

One-line installer (macOS & Linux — auto-picks Homebrew, falls back to npm):

curl -fsSL https://runfusion.ai/install.sh | sh
fusion dashboard

Homebrew (macOS & Linux):

brew tap runfusion/fusion
brew install fusion
fusion dashboard            # or: fn dashboard

Or as a one-liner (auto-taps): brew install runfusion/fusion/fusion.

npm global:

npm install -g @runfusion/fusion
fn dashboard                # or: fusion dashboard

From a clone (for development):

pnpm dev dashboard

Then click the Open: URL printed in the terminal. It embeds a bearer token (http://localhost:4040/?token=fn_...) that the browser captures to localStorage on first visit and reuses automatically thereafter. On the server side, Fusion now persists the dashboard/daemon token in ~/.fusion/settings.json on first authenticated run and reuses it on later starts unless you override it (--token, FUSION_DASHBOARD_TOKEN, FUSION_DAEMON_TOKEN) or disable auth with --no-auth. See CLI reference → fn dashboard → Authentication for full precedence and reset/revocation options.

On first launch, Fusion opens the onboarding wizard with three guided steps:

1. AI Setup — Use a simplified quick-start provider list (recommended providers plus any already-connected providers), then expand Advanced provider settings only if you need additional providers or setup details. You only need one provider to get started. Deprecated Google Gemini CLI / Antigravity provider entries are intentionally hidden; Google/Gemini API key, Google Generative AI, Vertex, and Cloud Code paths remain supported.
2. GitHub (Optional) — Connect GitHub for issue import and PR management
3. First Task — Create your first task or import from GitHub (if no project is active, onboarding first prompts you to register/select a project directory)

The wizard is dismissible and non-blocking — click Skip for now to use the dashboard immediately. Re-trigger it later from Settings → Authentication → Reopen onboarding guide.

For Capacitor + PWA workflow, see MOBILE.md.

• AI Planning — Planning agent generates detailed PROMPT.md with steps, file scope, and acceptance criteria
• Step-by-step Execution — Plan → Review → Execute → Review cycle for each task step, with graph-mode workflows able to model per-step parse/execute/review/rework explicitly
• Git Worktree Isolation — Each task runs in its own worktree (fusion/{task-id} branch)
• Selectable workflows — Pick Coding, Quick fix, Review-heavy, Stepwise coding, plugin-gated Compound Engineering, custom workflows, or PR lifecycle fragments where appropriate (overview; Workflow Steps)
• Visual Workflow Editor — Inspect read-only built-ins, duplicate/customize workflows, and edit graph nodes, columns, task fields, typed settings, and per-project values (Workflow Editor)
• Workflow Steps — Configurable quality gates (pre-merge: blocks merge; post-merge: informational), plus workflow-declared optional steps such as opt-in Browser Verification
• Workflow-native policy — Fast-mode planning (leanPlanning / autoApproveSpec), typed triage thresholds, review/approval, step execution, and model/fallback lanes are workflow settings, not hard-coded engine constants (Settings Reference; workflow settings)
• GitHub + PR lifecycle — Import issues, create PRs, display real-time PR/issue badges, and use workflow-mode PR lifecycle graph fragments where enabled
• Dashboard — Real-time kanban/list/graph views, agent management, terminal, git manager, mission planner, chat, workflow editor, custom provider setup, and one-click update action
• Missions — Hierarchical planning (Mission → Milestone → Slice → Feature → Task) with autopilot, validation contracts, fix-feature retries, mission-goal linking, and blocked-handoff semantics
• Multi-Project — Manage multiple projects from a single installation with project isolation
• Custom Providers — Add OpenAI-compatible, OpenAI Responses, Anthropic-compatible, or Google Generative AI providers; saved models appear in Project Models and workflow model dropdowns (Dashboard Guide; settings shape)
• Smart merge controls — Global auto-merge stays live for default tasks, while explicit per-task overrides can force auto/manual behavior (Settings Reference)
• Inter-Agent Messaging — Built-in messaging for coordination between agents and users; engineer-role agents can opt into backlog auto-claim for implementation tasks (Settings Reference)
• Agent Chat + Chat Rooms — Direct/task chat supports attachments, resumable streams, question response cards, and renameable conversations; experimental rooms route mentioned members as direct responders with optional ambient replies (Dashboard Guide → Chat View)

Fusion supports OAuth-based authentication for AI providers configured via Settings → Authentication. For most OAuth providers, when the dashboard is accessed via a non-localhost host (remote node, LAN host/IP, or reverse proxy), provider login URLs are rewritten to route OAuth callbacks through a bridge endpoint (/api/auth/oauth-callback) so redirects reach the active browser session.

• Anthropic (Claude) — Uses a pasted authorization-code flow in Settings/onboarding: sign in, then paste the final redirect URL (or code) back into Fusion to complete login
• OpenAI Codex — Uses the same pasted authorization-code flow with secure state validation
• Factory AI — via Droid CLI (optional) — requires local Droid CLI install + droid auth login; detection follows the effective runtime binary path (default droid, or plugin droidBinaryPath when configured), then enable in Settings → Authentication and restart Fusion
• llama.cpp — via HTTP server (optional) — configure your llama.cpp server URL (default http://127.0.0.1:8080) and optional API key, then enable in Settings → Authentication
• Other providers — Authenticate via API key entry in Settings (including Google/Gemini API key, Google Generative AI, Vertex, and Cloud Code aliases)
• Custom providers — Add user-defined OpenAI-compatible, OpenAI Responses, Anthropic-compatible, or Google Generative AI endpoints from Settings → Authentication → Custom Providers; saved model IDs become selectable in project and workflow model lanes (Dashboard Guide)

Fusion uses a dual-scope model hierarchy with five independent lanes. Global settings define baseline defaults; project settings provide per-project overrides.

Workflow lanes: The default workflow exposes Plan/Triage, Executor, Reviewer, and fallback model lanes in Settings → Project Models, and advanced workflow settings can declare additional typed model/policy values (Settings Reference).

Per-Task Overrides: Tasks can override the executor, validator, and planning lanes with per-task model fields (modelProvider/modelId, validatorModelProvider/validatorModelId, planningModelProvider/planningModelId).

Precedence: Per-task → Project override → Global lane → defaultProvider/defaultModelId → Automatic resolution.

For full settings documentation, see Settings Reference.

Fusion supports scheduled task automation via the /api/automations endpoints. Automations can run shell commands or multi-step workflows on a configurable schedule.

Automations and routines can run in two scopes:

• Global — Runs across all projects. Use this for cross-project maintenance, backups, or unified reporting.
• Project — Runs only within a specific project. Use this for project-specific CI, testing, or deployment tasks.

When you create a schedule without choosing a scope, Fusion defaults to project scope with the default project ID for backward compatibility.

To explicitly target a scope:

• In the dashboard Scheduled Tasks modal, use the Global / Project toggle.
• Via the API, pass ?scope=global or ?scope=project&projectId=<id> on automation/routine endpoints.

Scope resolution rules:

• scope=global always resolves to the global automation/routine lane, independent of the active project.
• scope=project requires a projectId. If omitted, it falls back to "default".
• CRUD, run, toggle, and webhook operations are strictly scope-isolated: a global schedule cannot be mutated from a project-scoped request, and vice versa.

Operational guidance for multi-project setups:

• Prefer global schedules for shared infrastructure (e.g., nightly backups, memory insight extraction).
• Prefer project schedules for per-repository automation (e.g., per-project test runners, deployment hooks).
• Global and project lanes are polled independently by the engine, so due runs in one lane do not block the other.

Routines are AI agent tasks triggered by cron schedules, webhooks, or manual execution. Routines share the same global/project scope model as automations.

fn task create "Fix the login bug"                    # Quick entry → planning
fn task plan "Build auth system"                      # AI-guided planning
fn task import owner/repo --labels bug                # Import GitHub issues
fn task show FN-001                                   # View task details
fn task logs FN-001 --follow                          # Stream execution logs
fn task steer FN-001 "Use TypeScript"                 # Guide the agent mid-execution

fn project add my-app /path/to/app                    # Register a project
fn project list                                       # List all projects

fn settings set maxConcurrent 4                       # Configure settings
fn settings export                                    # Export configuration

fn mission create "Auth System" "Build auth"          # Create mission
fn mission activate-slice <slice-id>                  # Activate a slice

fn skills search react                                # Search skills.sh
fn skills install firebase/agent-skills               # Install agent skills

pnpm install                  # Install dependencies
pnpm local                    # Start local dashboard/API on a non-4040 port
pnpm local -- --engine        # Start local dashboard with the AI engine
pnpm build                    # Build default workspace packages (excludes desktop/mobile)
pnpm build:all                # Build all packages (including desktop/mobile)
pnpm dev dashboard            # Run dashboard + AI engine
pnpm dev:ui                   # Dashboard only (no AI engine)
pnpm lint                     # Lint all packages
pnpm typecheck                # Type-check all packages
pnpm test                     # Run all tests

Build a single self-contained fn binary using Bun:

pnpm build:exe                # Build for current platform
pnpm build:exe:all            # Cross-compile for all platforms

MIT — open source, no vendor lock-in. See LICENSE.