cowork-dash 0.3.0

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.

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 visualizations (Plotly, matplotlib, Mermaid diagrams, DataFrames, Markdown, images)
• 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
• Token usage — cumulative counter with per-turn breakdown chart
• Theming — light, dark, and system-auto modes
• Customization — title, subtitle, welcome message, agent name, and custom icon

Installation

pip install cowork-dash

Quick Start

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")

Configuration

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

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)

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