Production Deployment of LangChain DeepAgent Systems

For enterprise-grade installations, multi-agent systems must run inside secure environments. LangChain's DeepAgent framework provides built-in delegation structures, context isolation boundaries, and a sandboxed file storage subsystem. By combining this framework with the LangGraph CLI server and a specialized web interface, developers can deploy production-ready researcher assistants.

This guide walks through configuring the DeepAgent framework, setting up secure file backends, and deploying the system.

Implementing the Sandboxed DeepAgent

The DeepAgent class structures agent logic by assigning tasks to sandboxed child agents. Each child agent operates with a custom prompt, target tools, and its own context memory.

import os
import sqlite3
from datetime import datetime
from dotenv import load_dotenv
from langchain_google_genai import ChatGoogleGenerativeAI
from langgraph.checkpoint.sqlite import SqliteSaver
from deepagents import create_deep_agent
from deepagents.backends import FilesystemBackend
from scripts.rag_tools import hybrid_search, live_finance_researcher, think_tool
from scripts.deep_prompts import DEEP_RESEARCHER_INSTRUCTIONS, DEEP_ORCHESTRATOR_INSTRUCTIONS

load_dotenv()

# Select the model engine
model = ChatGoogleGenerativeAI(model='gemini-3-pro-preview')

Dynamic Sandboxed File Backends

To prevent agents from accessing or modifying system files outside their assigned workspaces, configure the FilesystemBackend with virtual_mode=True. This locks the agent's file read/write operations inside a specific directory.

RESEARCH_OUTPUT_DIR = os.path.join(os.getcwd(), "research_outputs")

def get_research_backend(user_id: str, thread_id: str) -> FilesystemBackend:
    user_output_dir = os.path.join(RESEARCH_OUTPUT_DIR, user_id, thread_id)
    os.makedirs(user_output_dir, exist_ok=True)
    
    # Configure the filesystem backend sandbox
    backend = FilesystemBackend(
        root_dir=user_output_dir,
        virtual_mode=True  # Locks agent operations inside this folder path
    )
    return backend

Initializing the Orchestrator and Child Sub-Agents

Construct the research sub-agent and attach it to the parent orchestrator:

current_date = datetime.now().strftime("%Y-%m-%d")

# Define the child researcher sub-agent schema
research_sub_agent = {
    "name": "financial-research-agent",
    "description": "Delegate financial research tasks here. Query one target task at a time.",
    "system_prompt": DEEP_RESEARCHER_INSTRUCTIONS.format(date=current_date),
    "tools": [hybrid_search, live_finance_researcher, think_tool]
}

def get_deep_agent(user_id: str, thread_id: str):
    # Setup conversation checkpointer memory
    conn = sqlite3.connect('data/deep_agent_finance_researcher.db', check_same_thread=False)
    checkpointer = SqliteSaver(conn=conn)
    
    # Setup safe sandboxed directory
    backend = get_research_backend(user_id, thread_id)
    
    # Compile the final deep agent
    agent = create_deep_agent(
        model=model,
        tools=[hybrid_search, live_finance_researcher, think_tool],
        system_prompt=DEEP_ORCHESTRATOR_INSTRUCTIONS,
        subagents=[research_sub_agent],
        checkpointer=checkpointer,
        backend=backend
    )
    return agent

When queried, the orchestrator delegates subtasks to the child agent. All output files (such as research_request.md and final_report.md) are saved directly to the user's isolated directory:

from scripts.agent_utils import stream_agent_response

agent = get_deep_agent(user_id="kgptalkie", thread_id="session_1")
stream_agent_response(agent, "What was Amazon's revenue in Q1 2024?", thread_id="session_1")

Writing research files to: C:\Users\your-username\project\research_outputs\kgptalkie\session_1

  [Tool Triggered]: write_todos
   Arguments: {
     'todos': [
       {'status': 'in_progress', 'content': 'Save research request to /research_request.md'},
       {'status': 'pending', 'content': "Research Amazon's Q1 2024 revenue using a sub-agent"},
       {'status': 'pending', 'content': 'Synthesize findings and write final report to /final_report.md'}
     ]
   }

  [Tool Completed]

Production Deployment Steps

To deploy this multi-agent team in production, run the LangGraph graph executor as an API service, and connect it to a web frontend.

Prerequisites and Software Requirements

Install the required runtimes and command-line tools:

• uv: Fast Python package installer and manager.
• Node.js: Version 24 LTS.
• nvm (or nvm-windows on Windows): Node version manager.
• Yarn: Version 4 or newer.
• LangGraph CLI: Version 0.4.11.

Minimum system requirements: 8GB RAM, 10GB disk space, and Python 3.11+.

Target Repositories

Clone the project repositories:

• Backend: https://github.com/your-username/deep-finance-research
• Frontend: https://github.com/langchain-ai/deep-agents-ui

Step 1: Deploying the Backend API Server

Navigate to the cloned backend directory and start the LangGraph developer server:

cd deep-finance-research
langgraph dev

Running LangGraph API server on http://localhost:2024

This starts the graph server on port 2024 and opens the local LangGraph developer console.

Step 2: Running the Frontend UI Web App

Navigate to the frontend project directory, install dependencies, and start the development server:

cd deep-agents-ui
yarn install
yarn dev

Ready on http://localhost:3000

Open http://localhost:3000 in your web browser. In the settings panel, connect the UI to your running backend API at http://localhost:2024.

Setup Resources and Video Guides

For detailed setup walkthroughs across different environments, refer to these step-by-step playlists:

• Windows setup playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQdX4jeLgW4nf5PId4j1msI
• macOS setup playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSQVDu8nzUHb7FEkNCdU-E0o
• AWS cloud setup playlist: https://www.youtube.com/playlist?list=PLc2rvfiptPSRHHDk76XvdfQ3ilX5EnfoV

Verifying the Deployment Environment

Verify that all backend engines, package managers, and runtimes are correctly installed:

uv --version
node --version
yarn --version
langgraph --help

uv 0.1.5
v24.0.0
4.0.2
Usage: langgraph [OPTIONS] COMMAND [ARGS]...