How to Use OpenWork as a Desktop Interface for DeepAgents

OpenWork is an opinionated desktop interface for deepagentsjs, it exposes filesystem planning, subagent delegation, and direct tool access so agents can run complex, autonomous workflows on your machine.

openwork gives AI agents direct access to your filesystem and the ability to execute shell commands. Always review tool calls before approving them, and only run in workspaces you trust.

What it is

OpenWork is a desktop harness for deepagentsjs, providing a UI and runtime that makes it easier to build and interact with deep agent workflows locally. It bundles planning primitives, a desktop front end, and helpers for delegating work to subagents, so you can prototype autonomous flows that interact with files and local tools. This approach mirrors what Hermes Agent Desktop does for Windows, but OpenWork focuses on the DeepAgents ecosystem with built-in subagent support.

How it works

At a high level, OpenWork runs deepagentsjs in a desktop context, exposing a set of tools and a planner that agents can call. The UI lets you inspect planned steps, approve tool calls, and delegate tasks to subagents. Because it surfaces filesystem and shell access, you get powerful automation, but you also take on the security responsibility to sandbox and review actions. For a different take on local agent orchestration, Eigent provides a multi-agent workforce without the desktop UI layer.

# quick start
git clone https://github.com/langchain-ai/openwork.git
cd openwork
# follow the README to install dependencies and run the desktop app

Feature	Notes
Desktop UI	Opinionated interface for running deepagents workflows
Filesystem tooling	Agents can read, write, and manipulate files, with approval UI
Subagent delegation	Support for agent teams and delegated tasks
Use case	Prototyping autonomous developer workflows, local automation

Run OpenWork in a disposable VM or a sandboxed user account while you evaluate it, so you can validate behavior without risking your main environment.

Pros and cons

Pros

Keep the momentum going with these: How to Use PageIndex for Vectorless RAG on Complex Documents and How to Build Interactive UIs for Agents with MCP Apps.

• Makes deep agent workflows accessible via a desktop UI
• Useful for prototyping long running, multi step automations locally
• Open source, so you can audit and adapt tool access policies

Cons

• Direct filesystem and shell access is a security risk, review and sandbox carefully
• Not a hosted or managed solution, you are responsible for stability and access controls
• Some integrations may need manual tuning to match local environments

Video Demo & Walkthrough

Project link

• https://github.com/langchain-ai/openwork

Here are what peoples are saying:

“Awesome, gonna try it with ollama! And fork it of course” @little_hakr

“Damn this is insane!” @gargi_gupta97

Quick commands and examples

# clone the repo and inspect
git clone https://github.com/langchain-ai/openwork.git
ls openwork
# read the README and run demos in a sandboxed workspace

Claims and demos in curated projects should be validated before use in production, and be careful with tools that execute shell commands.

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