Fusion

From rough idea to production code — automatically.

🏭 A software factory, run by a multi-agent orchestrator.

Describe what you want — a team of AI agents plans, builds, reviews, and ships it for you. Fusion is your software factory: an assembly line for code that runs across tasks, agents, missions, git, files, and worktrees, with any model, local or cloud.

runfusion.ai → · Docs · GitHub · npm · Discord

English · 简体中文 · 繁體中文 · Français · Español · 한국어

---

Your entire dev environment. On a single pane of glass.

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.

---

Quick start

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

Fully-qualified install auto-taps and, on Homebrew 6.0+, trusts only the Fusion formula. If you already ran brew tap runfusion/fusion and short-name install fails with “untrusted tap”, run brew trust --formula runfusion/fusion/fusion then brew install 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.

First-run setup

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.

Mobile

For Capacitor + PWA workflow, see MOBILE.md.

---

The flow

  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)

See every step, before the merge

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.

---

What makes it different

🧠 AI planning	Describe a task in plain language. Planning agents turn it into a PROMPT.md plan with steps, file scope, and acceptance criteria.
🔁 Selectable workflows	Built-ins cover coding, quick fixes, review-heavy work, stepwise execution, plugin-gated Compound Engineering, and PR lifecycle fragments. Pick a workflow per task or author custom ones in the Workflow Editor.
🛡️ Planner oversight	Per-task or per-workflow oversight level (off / observe / steer / autonomous) governs how closely a planner overseer watches and intervenes — merge/PR and destructive actions always require explicit human confirmation. See Settings Reference and Dashboard Guide.
🌳 Worktree isolation	Each task runs in its own branch and worktree (fusion/{task-id}). Parallel tasks. Zero conflicts. Optional worktrunk delegation via worktrunk.enabled (see WorktreeBackend abstraction).
🗄️ PostgreSQL by default	Fusion uses zero-config embedded PostgreSQL for local runtime metadata. Legacy SQLite files are one-time migration inputs only; use a shared external database for multi-project and multi-node setups. (Storage)
⚡ Smart merge controls	Passing every gate? Fusion squash-merges and moves on. Opt into manual approval anywhere, inherit the live global auto-merge default, or set explicit per-task auto/manual overrides.
🛰️ Multi-node mesh	Laptop, Mac mini, Linux server, cloud VM, phone — all synced. Desktop, mobile, web.
🧩 Any model	Anthropic, OpenAI, Ollama, Google Generative AI, Z.ai, Kimi K3, local runtimes, and user-defined custom providers. Local and cloud coexist, with workflow model/fallback lanes configurable per project.
🏢 Agent companies	Import pre-built teams — 440+ agents across 16 companies — and run them autonomously for weeks.
📬 Inter-agent messaging	Built-in mailbox between agents. Delegate, clarify, coordinate; engineer-role agents can opt into backlog auto-claim when you want implementation help beyond executor-only pickup.
🗨️ Agent chat	Direct chat, task chat that proactively narrates step progress, failures, and review outcomes, attachments, in-chat question cards, resumable streams, and experimental multi-agent Chat Rooms where mentioned members respond directly and ambient members can join up to a cap. (Chat docs)
🗺️ Missions	Hierarchical planning (Mission → Milestone → Slice → Feature → Task) with autopilot and validation contracts.
🔬 Research	Bounded research runs with web search, GitHub, local docs, and LLM synthesis (plus runtime builtin WebSearch/WebFetch support in planning + synthesis flows when available). Turn findings into tasks. (Docs)
🧪 Self-improvement	Agents reflect on their own output and update their prompts as they learn your codebase.
🔓 Open source. MIT.	No vendor lock-in. Run it on your own hardware. Shipping weekly.

---

See it in action

The newest surfaces in Fusion, at a glance — the live board, your agent team, mission control, visual workflows, agent chat, multi-agent rooms, and inter-agent mail.

📋 The board & your agent team — live, from a real fleet

Every task, every column, every step — live. Cards carry GitHub links, step counts, review levels, and promote/move/archive actions. Switch to the Graph view to see task dependencies as an interactive node graph:

Board — kanban columns

Graph — dependency map

Here is the same fleet re-skinned into the Ember theme (dark graphite with an orange accent), alongside the Agents roster:

Board — Ember

Agents — Tokyo Night

Import a team and every agent shows up here — role, reports-to chain, heartbeat, and token share. Each agent card's heartbeat dropdown shows Disabled when scheduling is persisted off; choose Disabled to pause heartbeats while retaining its cadence, or select an interval to re-enable them. The Agents roster in Ember:

🛰️ Command Center — mission control for your agent fleet

One screen for everything your agents are doing. Tune live scheduler capacity, watch token spend by model in real time, and prove the value with hard numbers. The Overview tab opens with live gauges and charts:

Every tab is a different lens on the same live fleet:

Tokens — spend by model, cached vs. input vs. output, over time.	Productivity — outcomes, duration percentiles, language mix.	Team — agent org chart and token share per agent.
Activity — task throughput and event timelines.	Signals — anomaly detection and fleet health.	More — Tools · Ecosystem · GitHub · System · Reliability.

Tokens · Tools · Activity · Productivity · Team · Ecosystem · GitHub · Signals · System · Reliability · Mission Control — every tab is a different lens on the same live fleet.

The same fleet, your way — Command Center (and the whole dashboard) re-skins live across 70+ color themes, including Cobalt, Clay, and Moss. Here it is in Shadcn Light, Shadcn Dark Gray, and Ember:

Shadcn Light

Shadcn Dark Gray

Ember

A dozen light themes & a dozen dark themes (click to expand)

🔁 Selectable workflows, authored visually

A task's journey from idea to merge is a workflow — and it's yours to choose and shape. Pick a built-in (Coding, Quick fix, Review-heavy, Stepwise, PR lifecycle, Compound engineering, and more), inspect its graph, then duplicate and customize columns, gates, model lanes, and review policy in the visual Workflow Editor. No engine fork required.

Here's the Stepwise coding graph — plan, execute, and review every step before the next — explored node-by-node in Shadcn Light, Dark Gray, and Ember:

Shadcn Light

Shadcn Dark Gray

Ember

🗨️ Agent chat — talk to your agents, mid-flight

Direct chat and per-task chat with any agent, on any model. Ask why a task failed, steer an approach, drop attachments, answer in-chat question cards, and resume streams where you left off — full markdown and code rendering throughout.

Shadcn Light

Shadcn Dark Gray

👥 Multi-agent chat rooms

Put multiple agents in a room and let them coordinate. Mention a member and it responds directly; ambient members can join the conversation up to a cap. Here the CEO, Product Manager, and CTO agents align on task ownership in #leads — no human in the loop. (Chat docs)

Shadcn Light

Shadcn Dark Gray

Ember

📬 Agent mail — an inbox between your agents

A built-in mailbox for delegation, clarification, and hand-offs. Agents file triage summaries, request approvals, and coordinate work across the fleet — with Inbox, Outbox, Agents, and Approvals views, so you can audit every exchange.

Shadcn Light

Shadcn Dark Gray

Ember

📱 Fusion is an AI factory in your pocket

The full board, Command Center, missions, agents, and chat travel with you — native iOS and Android apps (Capacitor) plus an installable PWA. Start a run on your laptop, steer it from your phone.

_{See MOBILE.md for the Capacitor + PWA workflow.}

---

How it works

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).

---

Workflow overview

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.

Planner oversight

Each workflow (and optionally each task) can set a planner oversight level — off, observe, steer, or autonomous (default) — controlling how closely a planner overseer watches and intervenes in that task's execution. Even at autonomous, merge/PR progression and any destructive or external-service side effect always require an explicit, recorded human confirmation before they run. Notification verbosity is controlled separately. Set the default in the Workflow Editor → Values tab, or override per task from the New Task dialog / Task Detail edit form. Read Settings Reference for the full setting semantics and Dashboard Guide for the UI controls.

---

Multi-node. One board. Every platform.

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.

---

Run an agent company

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

---

Compatible with the tools you already use.

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.

Hermes _experimental

_{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 _experimental

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 _experimental

_{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.

---

Documentation

Guide	What it covers
Getting Started	Installation, onboarding, first task, and workflow-selection basics
Dashboard Guide	Board/list views, chat, workflow editor, git manager, settings, and UI tools
Task Management	Task lifecycle, prompt specs, comments, archiving, and GitHub integration
CLI Reference	Full fn command and daemon reference
Settings Reference	Global/project settings, model hierarchy, workflow settings, and custom providers
Workflow Steps	Workflow runtime, built-in workflows, gates, templates, and phases
Workflow Editor	Visual authoring, importing/exporting, custom fields/columns/settings, and mobile editor
Research	Bounded research runs, findings, exports, and task integration
Agents	Agent management, spawning, heartbeat, and mailbox workflows
Missions	Mission hierarchy, planning, autopilot, and validation contracts
Plugin Management	Discovering, installing, enabling, configuring, and troubleshooting plugins
Plugin Authoring	Building plugins with lifecycle hooks, routes, tools, runtimes, and dashboard surfaces
Remote Access	Tokenized remote dashboard access, Tailscale/Cloudflare setup, and troubleshooting
Multi-Project	Central registry, isolation modes, and migration paths
Storage	PostgreSQL runtime storage, migration compatibility, and file-backed payloads
Docker	Container deployment

---

Core features

• 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)
• Planner oversight — Workflow-native plannerOversightLevel (off/observe/steer/autonomous), with an optional per-task override and a separate notification-verbosity setting; merge/PR progression and destructive actions always require explicit human confirmation, even at autonomous (overview; Settings Reference)
• GitHub + PR lifecycle — Import issues with optional translation and screenshot attachments, skip previously imported issues even after edits or repository casing changes, 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, a project Overview with local codebase token estimate and on-disk size, 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)

Provider authentication

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)

Model system

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

Lane	Purpose	Global Baseline Keys	Project Override Keys
Executor	Task execution agent	executionGlobalProvider + executionGlobalModelId	executionProvider + executionModelId
Planning	Task planning agent	planningGlobalProvider + planningGlobalModelId	planningProvider + planningModelId
Validator	Plan/code reviewer	validatorGlobalProvider + validatorGlobalModelId	validatorProvider + validatorModelId
Merger	Merge conflict / clean-room merge agent	mergerGlobalProvider + mergerGlobalModelId	mergerProvider + mergerModelId
Title Summarization	Auto-title generation	titleSummarizerGlobalProvider + titleSummarizerGlobalModelId	titleSummarizerProvider + titleSummarizerModelId
Workflow Step Refinement	AI prompt refinement	(uses defaultProvider/defaultModelId)	(uses modelProvider/modelId on WorkflowStep)

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: Quick Add and Inline Create let tasks override the planning, executor, validator, and merger lanes; planning, validator, and merger selections also support task-specific thinking levels. (modelProvider/modelId, validatorModelProvider/validatorModelId, planningModelProvider/planningModelId, and merger model/thinking overrides.)

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

For full settings documentation, see Settings Reference.

Scheduled tasks / automations

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

Scheduling scope

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.

Automations

Endpoint	Method	Description
/api/automations	GET	List all automations (filtered by scope if specified)
/api/automations	POST	Create automation (scope defaults to project)
/api/automations/:id	GET	Get automation by ID
/api/automations/:id	PATCH	Update automation
/api/automations/:id	DELETE	Delete automation
/api/automations/:id/run	POST	Trigger manual run
/api/automations/:id/toggle	POST	Toggle enabled/disabled
/api/automations/:id/steps/reorder	POST	Reorder automation steps

Routines

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

Endpoint	Method	Description
/api/routines	GET	List all routines (filtered by scope if specified)
/api/routines	POST	Create routine (scope defaults to project)
/api/routines/:id	GET	Get routine by ID
/api/routines/:id	PATCH	Update routine
/api/routines/:id	DELETE	Delete routine
/api/routines/:id/run	POST	Manual trigger
/api/routines/:id/trigger	POST	Canonical manual trigger
/api/routines/:id/runs	GET	Get execution history
/api/routines/:id/webhook	POST	Webhook trigger (signature verification supported)

---

CLI quick examples

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

---

Packages

Package	Description
@fusion/core	Domain model — tasks, board columns, PostgreSQL stores
@fusion/dashboard	Web UI — Express server + kanban board with SSE
@fusion/engine	AI engine — planning, execution, scheduling, workflow steps
@runfusion/fusion	CLI + extension — published to npm

---

Development

pnpm install                  # Install dependencies
pnpm local                    # Start local dashboard/API + AI engine on a non-4040 port
pnpm local --no-engine        # Start local dashboard/API only
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 standalone executable

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

---

License

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

runfusion.ai →