langstage-cli 0.5.1

The terminal stage for your LangGraph agent — Claude Code-style CLI for any CompiledGraph

langstage-cli

The terminal stage for your LangGraph agent. A Claude Code-style CLI that runs any LangGraph CompiledGraph — yours, not a bundled one — with streaming, tool-call rendering, and human-in-the-loop approval.

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

Every stage for your LangGraph agent

langstage-cli is the terminal stage 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	langstage run --agent my_agent.py:graph
JupyterLab	langstage-jupyter	pip install langstage-jupyter, then the chat sidebar in jupyter lab
Terminal	langstage-cli	you are here
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

📖 Full documentation: https://dkedar7.github.io/langstage-docs/

Serve over AG-UI

This surface's agent — any LangGraph CompiledGraph — can also be served over the AG-UI protocol:

pip install "langgraph-stream-parser[agui]"
langstage-agui --agent my_agent.py:graph

Installation

pip install langstage-cli

Or install directly from GitHub:

pip install git+https://github.com/dkedar7/langstage-cli.git

Quick Start

No agent or API key yet? See the CLI working in one command:

langstage-cli --demo "hello"

Run with the default agent (requires ANTHROPIC_API_KEY):

export ANTHROPIC_API_KEY="your_api_key"
langstage-cli

Or specify your own agent:

langstage-cli -a path/to/your_agent.py:graph

This launches an interactive conversation loop with your agent.

Usage

# Use the default agent
langstage-cli

# Send a message directly
langstage-cli "Hello, agent!"

# Specify a custom agent file
langstage-cli -a my_agent.py:graph

# Use a module path
langstage-cli -a mypackage.agents:chatbot

# Read message from a file
langstage-cli -f ./prompt.md

# Non-interactive mode (auto-approve tool calls)
langstage-cli --no-interactive

# Verbose output
langstage-cli -v

# Keyless demo agent (no API key needed)
langstage-cli --demo

# Print the resolved configuration: each value, its source, and the
# env var / langstage.toml key that sets it
langstage-cli --show-config

Commands

In the interactive loop:

• /q or /quit - Exit
• /c - Clear conversation history
• /h or /help - Show help

Environment Variables

# Agent location (path/to/file.py:variable_name or module:variable)
# (DEEPAGENT_AGENT_SPEC / DEEPAGENT_SPEC still accepted as deprecated aliases)
export LANGSTAGE_AGENT_SPEC="my_agent.py:graph"
langstage-cli

# Working directory
export LANGSTAGE_WORKSPACE_ROOT="/path/to/workspace"

# Stream mode (updates or values)
export LANGSTAGE_STREAM_MODE="updates"

Configuration Files

langstage-cli reads TOML config from two locations and merges them (project overrides global):

• Global: ~/.langstage/config.toml
• Project: langstage.toml in the current directory or any ancestor

Legacy locations (~/.deepagents/config.toml, deepagents.toml) are still read as fallbacks; move your config when convenient — ~/.deepagents/ now belongs to LangChain's dcode.

Precedence: CLI args > env vars > project TOML > global TOML > defaults.

Example langstage.toml:

[agent]
spec = "my_agent.py:graph"
workspace_root = "."

[ui]
verbose = true
async_mode = false
stream_mode = "updates"

[configurable]
# seeds LangGraph RunnableConfig.configurable
thread_id = "my-thread"

CLI Options

Usage: langstage-cli [OPTIONS] [MESSAGE]

Arguments:
  MESSAGE  Optional input to send to the agent immediately

Options:
  -a, --agent TEXT                Agent spec (path/to/file.py:graph or module:graph)
  -g, --graph-name TEXT           Graph variable name (default: "graph")
  -f, --file PATH                 Read message from a file (any extension)
  --interactive/--no-interactive  Handle interrupts (default: interactive)
  --async-mode/--sync-mode        Async streaming (default: sync)
  --stream-mode TEXT              Stream mode (updates or values)
  -v, --verbose                   Verbose output

Creating Your Own Agent

Your agent file should export a compiled LangGraph graph:

# my_agent.py
from deepagents import create_deep_agent
from langgraph.checkpoint.memory import MemorySaver

agent = create_deep_agent(
    name="My Agent",
    model="anthropic:claude-sonnet-4-20250514",
    checkpointer=MemorySaver(),
)

Then run it:

langstage-cli -a my_agent.py:agent

Programmatic Use

from langstage_cli import stream_graph_updates, prepare_agent_input

input_data = prepare_agent_input(message="Hello!")

for chunk in stream_graph_updates(graph, input_data):
    if chunk.get("chunk"):
        print(chunk["chunk"], end="")

License

MIT License - see LICENSE file for details.