langstage 0.7.0

The web stage for your LangGraph agent — AI-powered workspace with streaming, file browser, and canvas

LangStage

The web stage for your LangGraph agent. A chat workspace for LangGraph and deepagents agents — real-time streaming, a workspace file browser, scheduled runs, and a canvas for visualizations.

Renamed from cowork-dash (the old package name now just installs this one, and the cowork-dash command still works).

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

Every stage for your LangGraph agent

langstage is the web stage (and namesake) of the LangStage family: write your agent once — any LangGraph CompiledGraph — and run it on every stage with the same spec string (module:attr or path/to/file.py:attr), the same langstage.toml config file, and the same LANGSTAGE_* environment variables.

Stage	Package	Try it
Web app	langstage	you are here
JupyterLab	langstage-jupyter	pip install langstage-jupyter, then the chat sidebar in jupyter lab
Terminal	langstage-cli	langstage-cli -a my_agent.py:graph
VS Code	langstage-vscode	chat participant + stdio sidecar
Reference agent	langstage-hermes	LANGSTAGE_AGENT_SPEC=langstage_hermes.agent:graph on any stage
Shared core	langgraph-stream-parser	typed events + config resolver behind every stage

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 langstage

Quick Start

No agent or API key yet?

langstage 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 langstage 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
langstage run --agent my_agent.py:agent --workspace ./workspace

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

Shorthand

from langstage 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 langstage import CoworkApp
from langstage.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 / langstage.toml key that sets it):

langstage --show-config

Option	CLI Flag	Env Var	Default
Agent spec	--agent	LANGSTAGE_AGENT_SPEC	Built-in default agent
Workspace	--workspace	LANGSTAGE_WORKSPACE_ROOT	.
Host	--host	LANGSTAGE_HOST	localhost
Port	--port	LANGSTAGE_PORT	8050
Debug	--debug	LANGSTAGE_DEBUG	false
Title	--title	LANGSTAGE_TITLE	Agent's .name or "Cowork Dash"
Subtitle	--subtitle	LANGSTAGE_SUBTITLE	"AI-Powered Workspace"
Welcome message	--welcome-message	LANGSTAGE_WELCOME_MESSAGE	(empty)
Theme	--theme	LANGSTAGE_THEME	auto
Agent name	--agent-name	LANGSTAGE_AGENT_NAME	Agent's .name or "Agent"
Icon URL	--icon-url	LANGSTAGE_ICON_URL	(none)
Auth username	--auth-username	LANGSTAGE_AUTH_USERNAME	admin
Auth password	--auth-password	LANGSTAGE_AUTH_PASSWORD	(none — auth disabled)
Save workflow prompt	--save-workflow-prompt	LANGSTAGE_SAVE_WORKFLOW_PROMPT	(built-in)
Run workflow prompt	--run-workflow-prompt	LANGSTAGE_RUN_WORKFLOW_PROMPT	(built-in, use {filename})
Create workflow prompt	--create-workflow-prompt	LANGSTAGE_CREATE_WORKFLOW_PROMPT	(built-in)
Show Canvas tab	--show-canvas/--no-show-canvas	LANGSTAGE_SHOW_CANVAS	Auto — on when CanvasMiddleware is attached
Show Files tab	--show-files/--no-show-files	LANGSTAGE_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 langstage/static/
npm run dev      # dev server with hot reload (proxy to backend on :8050)

License

MIT