cowork-dash 0.6.0

Web UI for deepagents — AI-powered workspace with streaming, file browser, and canvas

Cowork Dash

Web UI for LangGraph and deepagents agents. Provides a chat interface with real-time streaming, a workspace file browser, and a canvas for visualizations.

Stack: Python (FastAPI + WebSocket) backend, React (TypeScript + Vite) frontend.

One agent, every surface

Cowork Dash is the web surface of the deep-agent family: write your agent once — any LangGraph CompiledGraph — and run it on every surface with the same spec string (module:attr or path/to/file.py:attr), the same deepagents.toml config file, and the same DEEPAGENT_* environment variables.

Surface	Package	Try it
Web app	cowork-dash	you are here
JupyterLab	deepagent-lab	pip install deepagent-lab, then the chat sidebar in jupyter lab
Terminal	deepagent-code	deepagent-code -a my_agent.py:graph
VS Code	deepagent-vscode	chat participant + stdio sidecar
Reference agent	deepagent-hermes	DEEPAGENT_AGENT_SPEC=deepagent_hermes.agent:graph on any surface
Shared core	langgraph-stream-parser	typed events + config resolver behind every surface

Features

• Chat with real-time token streaming via WebSocket
• Tool call visualization — inline display of arguments, results, duration, and status
• Rich inline content — HTML, Plotly charts, images, DataFrames, PDFs, and JSON rendered directly in the chat
• Canvas panel — persistent report surface for charts, tables, diagrams, images, and narrative markdown. Opt-in via CanvasMiddleware; auto-detected by the UI.
• File browser — workspace file tree with syntax-highlighted viewer and live file change detection
• Task tracking — sidebar todo list with progress bar, synced with agent write_todos calls
• Human-in-the-loop — interrupt dialog for reviewing and approving agent actions
• Slash commands — /save-workflow, /create-workflow, and /run-workflow with autocomplete
• Print / export — print conversations via browser Print dialog with optimized CSS
• Token usage — cumulative counter with per-turn breakdown chart
• Authentication — optional HTTP Basic Auth for all endpoints
• Theming — light, dark, and system-auto modes
• Customization — title, subtitle, welcome message, agent name, and custom icon

Installation

pip install cowork-dash

Quick Start

No agent or API key yet?

cowork-dash run --demo

launches the full UI against a built-in keyless echo agent, so you can explore the surface before wiring up a real agent.

From Python

from cowork_dash import CoworkApp

app = CoworkApp(
    agent=your_langgraph_agent,  # Any LangGraph CompiledGraph
    workspace="./workspace",
    title="My Agent",
)
app.run()

From CLI

# Point to a Python file exporting a LangGraph agent
cowork-dash run --agent my_agent.py:agent --workspace ./workspace

# With options
cowork-dash run --agent my_agent.py:agent --port 8080 --theme dark --title "My Agent"

Shorthand

from cowork_dash import run_app

run_app(agent=your_agent, workspace="./workspace")

Enabling the Canvas

The canvas is opt-in. Attach CanvasMiddleware to your agent and the Canvas tab appears in the UI automatically:

from deepagents import create_deep_agent
from cowork_dash import CoworkApp
from cowork_dash.middleware import CanvasMiddleware

agent = create_deep_agent(
    tools=[...],
    middleware=[CanvasMiddleware()],   # <-- adds canvas tools + report guidance
    ...
)

CoworkApp(agent=agent, workspace="./workspace").run()

The middleware injects five tools (add_to_canvas, update_canvas_item, remove_canvas_item, add_canvas_section, reorder_canvas) and appends report-building instructions to the system prompt at each model call. Canvas items persist to .canvas/canvas.md in the workspace.

To force the tabs on/off regardless of middleware: --show-canvas/--no-show-canvas, --show-files/--no-show-files, or the Python-API show_canvas / show_files kwargs.

Configuration

Configuration priority: Python args > CLI args > environment variables > defaults.

Never remember a variable name — print the resolved configuration (each value, its source, and the env var / deepagents.toml key that sets it):

cowork-dash --show-config

Option	CLI Flag	Env Var	Default
Agent spec	--agent	DEEPAGENT_AGENT_SPEC	Built-in default agent
Workspace	--workspace	DEEPAGENT_WORKSPACE_ROOT	.
Host	--host	DEEPAGENT_HOST	localhost
Port	--port	DEEPAGENT_PORT	8050
Debug	--debug	DEEPAGENT_DEBUG	false
Title	--title	DEEPAGENT_TITLE	Agent's .name or "Cowork Dash"
Subtitle	--subtitle	DEEPAGENT_SUBTITLE	"AI-Powered Workspace"
Welcome message	--welcome-message	DEEPAGENT_WELCOME_MESSAGE	(empty)
Theme	--theme	DEEPAGENT_THEME	auto
Agent name	--agent-name	DEEPAGENT_AGENT_NAME	Agent's .name or "Agent"
Icon URL	--icon-url	DEEPAGENT_ICON_URL	(none)
Auth username	--auth-username	DEEPAGENT_AUTH_USERNAME	admin
Auth password	--auth-password	DEEPAGENT_AUTH_PASSWORD	(none — auth disabled)
Save workflow prompt	--save-workflow-prompt	DEEPAGENT_SAVE_WORKFLOW_PROMPT	(built-in)
Run workflow prompt	--run-workflow-prompt	DEEPAGENT_RUN_WORKFLOW_PROMPT	(built-in, use {filename})
Create workflow prompt	--create-workflow-prompt	DEEPAGENT_CREATE_WORKFLOW_PROMPT	(built-in)
Show Canvas tab	--show-canvas/--no-show-canvas	DEEPAGENT_SHOW_CANVAS	Auto — on when CanvasMiddleware is attached
Show Files tab	--show-files/--no-show-files	DEEPAGENT_SHOW_FILES	true

Slash Commands

Type / in the chat input to access built-in commands:

Command	Description
/save-workflow	Capture the current conversation as a reusable workflow in ./workflows/
/create-workflow	Create a new workflow from scratch — prompts for a topic description
/run-workflow	Execute a saved workflow — shows an autocomplete dropdown of .md files from ./workflows/

All commands support inline arguments:

/save-workflow focus on the data cleaning steps
/create-workflow daily sales report pipeline
/run-workflow etl-pipeline.md skip step 3

The prompt templates behind each command are configurable via Python API, CLI flags, or environment variables (see Configuration table above).

Stream Parser Config

Control how agent events are parsed by passing stream_parser_config to CoworkApp:

app = CoworkApp(
    agent=agent,
    stream_parser_config={
        "extractors": [...],  # Custom tool extractors
    },
)

See langgraph-stream-parser for details.

Architecture

Browser  <--WebSocket-->  FastAPI  <--astream_events-->  LangGraph Agent
            /ws/chat         |
                        REST APIs:
                          /api/config
                          /api/files/tree
                          /api/files/{path}
                          /api/canvas/items

The frontend is pre-built and bundled into the Python package as static files. No Node.js required at runtime.

Development

# Backend
pip install -e ".[dev]"
pytest tests/

# Frontend
cd frontend
npm install
npm run build    # outputs to cowork_dash/static/
npm run dev      # dev server with hot reload (proxy to backend on :8050)

License

MIT