Napkin

<p align="center"> <img src="assets/NapkinLogo.png" alt="Napkin" width="300"> </p>

<p align="center"> Napkin is a shared Excalidraw canvas for agent + human collaboration. </p>

Why We Built It

I love using AI assistants for everyday tasks — design, programming, and brainstorming. I’ve always found visual thinking helpful, but there was a gap: I could describe something, and the agent could describe it back, but we couldn’t point to the same thing. What if my agent and I could share a whiteboard — where I draw, it responds, and we build together?

Napkin works with any MCP-capable agent. For true two-way canvas-to-agent, you'll also need a webhook.

Examples (all drawn by Perry the agent, with Napkin)

Brainstorming

<p align="center"> <img src="assets/brainstorm.png" alt="Brainstorm" width="300"> </p>

Gaming

<p align="center"> <img src="assets/snake_game.png" alt="Games" width="300"> </p>

"Art" - Don't know why Perry invented a Platabird, but there you are.

<p align="center"> <img src="assets/self_portrait.png" alt="Art" width="300"> </p>

Collaboration

<p align="center"> <img src="assets/collab.png" alt="Collaboration" width="300"> </p>

What It Is

Napkin is an Excalidraw whiteboard connected to an MCP server. Any MCP-capable agent can read the canvas, draw on it, annotate it, animate it, and respond to what you draw — all while you interact with the agent through your normal channel (Slack, WhatsApp, Telegram, Terminal, or any MCP client).

The conversation stays in your channel. The canvas is pure whiteboard.

<p align="center"> <img src="assets/overview.png" alt="Overview" width="600"> </p>

Quick Start

1) Start the MCP server

cd mcp
npm install
npm run build
npm start

Defaults:

• MCP HTTP: http://localhost:3003
• WebSocket: ws://localhost:3002

2) Start the browser UI

cd ui
npm install && npm run dev
# Open http://localhost:5173

3) Connect your agent

Napkin works with any MCP-capable agent. Add to your MCP client config:

{
  "napkin": {
    "type": "http",
    "url": "http://localhost:3003"
  }
}

Two-way vs one-way: MCP alone gives you agent→canvas (read, write, animate). For canvas→agent (the agent waking up when you draw something), you need a webhook receiver on your agent's side.

Mode	What you get	Requires
MCP only	Agent can read and write the canvas	Any MCP client
MCP + webhook	Agent also reacts to human canvas activity	Webhook receiver (see below)

4) Configure the webhook (two-way only)

When you draw on the canvas, Napkin POSTs a trigger to your agent's webhook endpoint. The agent wakes up, reads the canvas diff, and responds.

OpenClaw — built in. Set NAPKIN_TRIGGER_WEBHOOK in your environment and it routes automatically.

NanoClaw — add the webhook channel via PR #1488:

# .env additions
WEBHOOK_PORT=3200
WEBHOOK_LINKED_JID=<your channel JID>   # the chat where the agent should post

Then pass the webhook URL when starting your session:

start_session({
  session_id: "<your channel ID>",
  webhook_url: "http://localhost:3200/webhook"
})

Custom / other frameworks — any HTTP server that accepts POST /webhook with { message, sender? } JSON and forwards to your agent works fine.

Environment Variables

Common server variables:

• NAPKIN_TRANSPORT (http or stdio, default http)
• NAPKIN_MCP_PORT (default 3003)
• MCP_WS_PORT (default 3002)
• AGENT_TRIGGER_DEBOUNCE_MS (default 3000)
• NAPKIN_TRIGGER_WEBHOOK (optional global webhook URL)
• NAPKIN_COMPACT_TRIGGERS (true/false, default false)
• NAPKIN_TRIGGER_INCLUDE_CANVAS (true/false, default false)
• NAPKIN_SESSION_TTL_MS (default 7200000)
• NAPKIN_EXPORT_DIR (optional base dir for relative exports)
• ANTHROPIC_API_KEY (required for vision tools only - others may be used)

See ARCHITECTURE.md for full details.

Core Concepts

The Intent API

No coordinates. No boilerplate. Describe what you want.

add_node("Auth Service", shape: "rectangle", metadata: { intent: "entry point" })
add_node("Token Store")
connect("Auth Service", "Token Store", label: "issues token")
layout()

The server handles placement, bindings, and layout. A 3-node diagram takes one short exchange.

Reading the Canvas

get_canvas() returns a semantic structure — nodes, edges, zones — not raw coordinates:

{
  "nodes": [
    { "id": "abc", "label": "Auth Service", "type": "box",
      "metadata": { "intent": "entry point", "status": "wip" } }
  ],
  "edges": [
    { "id": "xyz", "from": "abc", "to": "def", "label": "issues token" }
  ]
}

For cheap reasoning passes, get_canvas_summary() returns nodes and edges only — no zones, sketches, or proximity properties.

Metadata

Every element carries a customData object — invisible in the UI, readable by agents:

add_node("Deploy Production", metadata: {
  type: "task",
  step: 8,
  owner: "platform-team",
  status: "wip"
})

Agents use metadata to reason about what elements represent, not just what they look like. trace_path() can filter by metadata to find specific flows.

Triggers and Webhooks

When you draw or annotate, Napkin notices. After a quiet period, it POSTs a trigger to your agent's webhook:

{
  "session_id": "slack:D0AGREPG84R",
  "source": "debounce",
  "message": "[napkin] Canvas updated (idle)",
  "changed_element_ids": ["rect-42"],
  "changed_elements_compact": [
    { "id": "rect-42", "type": "rectangle", "label": "Change the timer to 120s?" }
  ],
  "change_summary": "added text \"Change the timer to 120s?\"",
  "change_type": "semantic"
}

The agent wakes up, reads what changed, and responds — on the canvas and in your channel.

Trigger sources:

• debounce — canvas went quiet (configurable per session, default 3s)
• chat — injected by your agent framework
• reconnect — browser connected or reconnected

Echo suppression: agent writes don't trigger webhooks. Only genuine human edits fire triggers.

Thought Bubbles

Agents express tentative ideas as thought bubbles — dashed purple, clearly "agent suggestion":

add_thought_bubble("Should this connect to the cache layer?", near_node_id: "db-node")

You can confirm them (makes permanent) or dismiss them. Keeps agent suggestions visually distinct from established content.

Batch Execution: apply_intents

Collapse many sequential calls into one:

apply_intents([
  { tool: "add_node", args: { label: "A" }, ref: "n1" },
  { tool: "add_node", args: { label: "B" }, ref: "n2" },
  { tool: "connect", args: { from_id: "$ref:n1.id", to_id: "$ref:n2.id" } },
  { tool: "layout", args: {} }
])

Ref substitution ($ref:n1.id) lets later ops use outputs from earlier ones — no round-trips needed to get IDs. A 10-node diagram with connections and layout ships in a single MCP call.

Path Traversal: trace_path

Find flows through the graph without manual graph walking:

trace_path({
  from_label: "Button 1",
  direction: "downstream",
  filter: { node_metadata: { status: "error" } }
})

Returns ordered nodes and edges. Filter by metadata to isolate happy paths, error paths, or any subset. Combine with apply_intents to animate the result.

---

Tool Examples

Flow Diagram

add_node("Input")
add_node("Process")
add_node("Output")
connect("Input", "Process")
connect("Process", "Output")
layout("LR")

State Machine (with animation)

# Build a traffic light state machine
apply_intents([
  { tool: "add_node", args: { label: "RED",    shape: "ellipse", style: { background: "#ff6b6b" } }, ref: "red" },
  { tool: "add_node", args: { label: "GREEN",  shape: "ellipse", style: { background: "#69db7c" } }, ref: "grn" },
  { tool: "add_node", args: { label: "YELLOW", shape: "ellipse", style: { background: "#ffd43b" } }, ref: "yel" },
  { tool: "connect",  args: { from_id: "$ref:red.id", to_id: "$ref:grn.id", label: "60s" } },
  { tool: "connect",  args: { from_id: "$ref:grn.id", to_id: "$ref:yel.id", label: "45s" } },
  { tool: "connect",  args: { from_id: "$ref:yel.id", to_id: "$ref:red.id", label: "5s"  } },
  { tool: "layout",   args: { style: "LR" } }
])

# Animate the cycle
apply_intents([
  { tool: "animate_element", args: { id: "$ref:red.id", to: { opacity: 100 }, duration_ms: 700 } },
  { tool: "animate_element", args: { id: "$ref:red.id", to: { opacity: 20  }, duration_ms: 200 } },
  { tool: "animate_element", args: { id: "$ref:grn.id", to: { opacity: 100 }, duration_ms: 700 } },
  ...
])

CI/CD Pipeline (real example)

22 operations — 10 nodes, 11 connections, layout — in a single MCP call. The agent then calls trace_path to find the happy path and animates it with another single call.

(See cicd-pipeline-demo.excalidraw for the saved canvas.)

Responding to Human Input

A human types "Change the timer to 120s?" directly on the canvas. The agent's webhook fires. The agent reads changed_elements_compact, finds the text, acts on it, and responds with a thought bubble confirming the change — all without leaving the whiteboard.

---

Agent Instructions Template

Add this to your agent's instructions (adapt webhook URL for your framework):

## Napkin (Collaborative Canvas)

When the napkin MCP server is available, you have a shared Excalidraw whiteboard.

**First — always call:**
  start_session({ session_id: "<your channel ID>", webhook_url: "<your webhook>" })

**Drawing — use the intent API (no coordinates needed):**
  add_node, connect, move, resize, style, add_label, delete_element, layout

**For multi-step operations:**
  apply_intents([...ops...]) — build entire diagrams in one call, chain refs

**Reading:**
  get_canvas_summary() — cheap reasoning pass (nodes + edges only)
  get_canvas() — full semantic output including zones and metadata
  trace_path(from_label, direction, filter) — graph traversal

**On webhook trigger:**
  1. add_thought_bubble() — acknowledge visually before processing
  2. Use changed_elements_compact from the payload — no round-trip needed
  3. Skip triggers with change_type "cosmetic" if you only care about structure
  4. Skip triggers with source "reconnect"

**Metadata convention:**
  intent, notes, status (wip|review|done|parking_lot), owner

---

Tool Reference

Read

Tool	Purpose
get_canvas	Full semantic canvas — nodes, edges, zones, metadata
get_canvas_summary	Compact read — nodes and edges only
get_canvas_raw	Raw Excalidraw JSON
get_canvas_diff	Elements changed since a timestamp
get_pending_triggers	Poll for triggers (pull mode)
trace_path	Traverse graph from a node, with optional metadata filtering

Write

Tool	Purpose
apply_intents	Execute ordered batch of operations in one call
add_node	Add labeled node — server handles placement
connect	Connect nodes with an arrow
move	Move element by offset (dx, dy)
resize	Resize element, center preserved
style	Apply color, fill, opacity, stroke
add_label	Floating text near an element
delete_element	Remove element and bound text
patch_canvas	Partial field updates on existing elements
update_canvas	Add elements with full definitions
clear_canvas	Remove everything

Layout

Tool	Purpose
layout	Auto-arrange via Dagre (TB, LR, tree, hierarchy)

Thought Bubbles

Tool	Purpose
add_thought_bubble	Dashed purple tentative suggestion
confirm_thought_bubble	Make permanent
dismiss_thought_bubble	Remove
list_thought_bubbles	List current bubbles

Vision

Tool	Purpose
describe_elements	Render to PNG, send to Claude vision for description
describe_sketch	Convenience wrapper for freehand elements

Animation & Export

Tool	Purpose
animate_element	Interpolate position/size/opacity/color over time
export_canvas	Save to .excalidraw, .svg, or .png

Sessions

Tool	Purpose
start_session	Begin session with channel ID, webhook, debounce override
end_session	End session

---

Troubleshooting

Canvas appears blank after agent draws something The browser is the source of truth. If it reconnected between your agent's write and the next read, the server cache was reset. Call start_session again and redraw.

Webhook isn't firing Confirm start_session was called with the correct session_id and webhook_url. Triggers are suppressed on agent writes — only human canvas edits fire them.

Agent can't see what I drew Check get_canvas_diff with a recent timestamp. If the browser tab was closed and reopened, the canvas state was re-synced from the browser — call get_canvas for a fresh read.

export_canvas produces a blank file Export immediately after drawing — don't let the browser reconnect between the write and export calls. Use an absolute path on the host machine.

Design Decisions

Canvas state lives in the browser. The server caches it for agents but the browser is the source of truth. If the server restarts, the browser reconnects and re-syncs — no data loss as long as the tab is open. Export to .excalidraw for persistence.

No chat UI. The conversation lives in your agent's channel. Napkin is a visual tool. The trigger/webhook system routes canvas events back to the right conversation automatically.

Echo suppression. Agent writes don't trigger webhooks. The server tracks which element IDs were written by agents and ignores browser echoes of those writes for ~2s. Only genuine human edits fire triggers.

Per-session isolation. Each start_session call gets its own debounce timer, webhook URL, and trigger context. Multiple agents can share a canvas with independent notification channels.

Contributing

PRs welcome. The most useful directions right now: additional layout engines, integrations with other agent frameworks, and tighter agentic loop patterns. Open an issue first for anything structural.

Security

Please review SECURITY.md for reporting guidance.

License

MIT