design-agent

Name: design-agent
Author: langchain-ai

Pending

Open-source design agent: on-brand stills, decks, diagrams, charts and motion graphics from a chat thread, built on Deep Agents.

65stars

10forks

JavaScript

Installation

# Add to your Claude Code skills
git clone https://github.com/langchain-ai/design-agent

Getting Started

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README.md

Frequently Asked Questions

What is design-agent?

design-agent is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by langchain-ai. Open-source design agent: on-brand stills, decks, diagrams, charts and motion graphics from a chat thread, built on Deep Agents. It has 65 GitHub stars.

Is design-agent safe to use?

design-agent's catalog security scan is still queued. You can run an instant dependency and prompt-injection check now with the "Scan for vulnerabilities" button above.

How do I install design-agent?

Clone the repository with "git clone https://github.com/langchain-ai/design-agent" and add it to your Claude Code skills directory (see the Installation section above).

What programming language is design-agent written in?

design-agent is primarily written in JavaScript. It is open-source under langchain-ai on GitHub, so you can review or fork the full source.

Are there alternatives to design-agent?

Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh design-agent against similar tools.

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⚠️ Third-Party Software Notice

This skill is third-party open-source software developed and hosted independently on GitHub. SkillsLLM is an informational directory and does not control or maintain the underlying repository.

Any security checks, ratings, or warnings displayed by SkillsLLM are automated and limited in scope. They do not constitute a security certification or guarantee that the software is safe, error-free, or free from malicious code, vulnerabilities, compromised dependencies, or prompt-injection risks.

Review the source code, permissions, dependencies, and configuration before installing or running any third-party skill. Use is at your own risk. To the maximum extent permitted by applicable law, SkillsLLM is not liable for losses arising from third-party software.

Read the Terms of Service

Content Studio is the design agent LangChain runs, now open source. Add your brand and deploy it to your team's Slack with Managed Deep Agents. Anyone can ask it like they'd ask a designer, attach what they have, and get finished, on-brand files back, from a resized ad to a launch film.

Since August it has shipped over 3,000 assets for us, posts with its work drew about 4.5M views on X, and three in four requests come from outside marketing. How we built it goes deeper into each part.

See it work

We asked Content Studio for its own launch film. It went full Rocky.

https://github.com/user-attachments/assets/ee5b98e0-7ec6-4b24-ada6-1f27087947a3

How it works

Content Studio is a Deep Agents agent deployed on Managed Deep Agents. Managed Deep Agents runs the infrastructure: Slack, sandboxes, memory, schedules and tracing. This repo is the design agent on top. The model makes the creative calls, and code renders, checks and delivers.

Slack. Mention it in a thread with a brief and any files. MDA's Slack app hands the message and attachments to the agent, and the finished files come back to the same thread. While it works, one message updates step by step, a footer shows what's running and the cost so far, and anything you add mid-job reaches the work in progress.
A computer per thread. Each thread gets its own LangSmith sandbox: a Linux machine with Chrome, FFmpeg, HyperFrames, D2 and your fonts, built from one pinned recipe (sandbox/setup.sh). Everything it makes is code in a project folder there (a slide is HTML, a film is an animation timeline), so a change is an edit and a re-render, not a restart. API keys never enter it.
Skills and a design wiki. The always-on prompt is small. For each ask it first reads a router skill that picks the right workflow, from a resized ad to a launch film, out of 35 skills. The wiki holds the craft (type, colour, layout, charts) and your brand pack holds the facts. Skills load only when a job needs them, served by MDA from Context Hub.
Your tools. Video is built in HyperFrames, which treats a film as a web page on a timeline. Paper is a shared canvas where you edit a storyboard or layout directly. Image, voice and music models run as tools outside the sandbox, and MCP servers like Notion, Linear, GitHub and Figma connect through MDA, with each person signed in as themselves.
Async subagents. On a big job the main agent plans, splits the work and starts subagents in the background on the same sandbox and files. MDA wakes it when one finishes, fails or stalls, so it never sits waiting and can keep talking to you. Films go storyboard, animatic, then production, and code makes every part pass review before assembly.
Memory. Tell it a rule once ("never use orange on LinkedIn") and it saves it that turn. MDA gives it two layers of files: team memory everyone shares and personal memory for each person, and each layer's index loads into every run.
It gets better. After a job, learn_from_job writes notes on what worked against the skill it used. Later jobs read and rate those notes, and the ones that don't hold up are dropped.

Watch it explain itself

We asked it to explain its own harness as a nature documentary, starring a pixel parrot. It walks through the computer, the tools and the context behind its work.

https://github.com/user-attachments/assets/65eac8e4-0fac-4a5a-9cca-02e26a0a8baa

The agent made it from this ask and a few rounds of notes:

Make a short explainer of how you create videos in code, end to end, for our blog post on the media harness we built on Deep Agents. Narrate it like a David Attenborough nature documentary, starring the pixel parrot from our promo, with your subagents as its flock. Keep it technical enough for an engineer, in real terms: skills, wiki, tools, apps. Storyboard it before you build, and end on "Built on Managed Deep Agents".

Read the architecture, or start changing it: instructions.md · skills/ · Customization · Evals · Operations · Contributing.

Why it doesn't look like AI

It looks at its work. It renders, then looks at the image before it posts anything.
Your brand is code. Layouts only take your tokens: your colours, fonts and spacing.
It checks what a machine can count. Colours, fonts, contrast, sizes and chart math are checked in code on every render. Taste stays with your designers.