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# 🚀🧠 Deep Agents UI
[Deep Agents](https://github.com/langchain-ai/deepagents) is a simple, open source agent harness that implements a few generally useful tools, including planning (prior to task execution), computer access (giving the able access to a shell and a filesystem), and sub-agent delegation (isolated task execution). This is a UI for interacting with deepagents.
## 🚀 Quickstart
**Install dependencies and run the app**
```bash
git clone https://github.com/langchain-ai/deep-agents-ui.git
cd deep-agents-ui
yarn install
yarn dev
```
**Deploy a Deep Agent**
As an example, see our [Deep Agents quickstarts](https://github.com/langchain-ai/deepagents/tree/main/examples) for examples and run the `deep_research` example.
The `langgraph.json` file has the assistant ID as the key:
```
"graphs": {
"research": "./agent.py:agent"
},
```
Kick off the local LangGraph deployment:
```bash
cd deepagents-quickstarts/deep_research
langgraph dev
```
You will see the local LangGraph deployment log to terminal:
```
╦ ┌─┐┌┐┌┌─┐╔═╗┬─┐┌─┐┌─┐┬ ┬
║ ├─┤││││ ┬║ ╦├┬┘├─┤├─┘├─┤
╩═╝┴ ┴┘└┘└─┘╚═╝┴└─┴ ┴┴ ┴ ┴
- 🚀 API: http://127.0.0.1:2024
- 🎨 Studio UI: https://smith.langchain.com/studio/?baseUrl=http://127.0.0.1:2024
- 📚 API Docs: http://127.0.0.1:2024/docs
...
```
You can get the Deployment URL and Assistant ID from the terminal output and `langgraph.json` file, respectively:
- Deployment URL: <http://127.0.1:2024>
- Assistant ID: `research`
**Open Deep Agents UI** at [http://localhost:3000](http://localhost:3000) and input the Deployment URL and Assistant ID:
- **Deployment URL**: The URL for the LangGraph deployment you are connecting to
- **Assistant ID**: The ID of the assistant or agent you want to use
- [Optional] **LangSmith API Key**: Your LangSmith API key (format: `lsv2_pt_...`). This may be required for accessing deployed LangGraph applications. You can also provide this via the `NEXT_PUBLIC_LANGSMITH_API_KEY` environment variable.
**Usage**
You can interact with the deployment via the chat interface and can edit settings at any time by clicking on the Settings button in the header.
<img width="2039" height="1495" alt="Screenshot 2025-11-17 at 1 11 27PM" src="https://github.com/user-attachments/assets/50e1b5f3-a626-4461-9ad9-90347e471e8c" />
As the deepagent runs, you can see its files in LangGraph state.
<img width="2039" height="1495" alt="Screenshot 2025-11-17 at 1 11 36PM" src="https://github.com/user-attachments/assets/86cc6228-5414-4cf0-90f5-d206d30c005e" />
You can click on any file to view it.
<img width="2039" height="1495" alt="Screenshot 2025-11-17 at 1 11 40PM" src="https://github.com/user-attachments/assets/9883677f-e365-428d-b941-992bdbfa79dd" />
### Optional: Environment Variables
You can optionally set environment variables instead of using the settings dialog:
```env
NEXT_PUBLIC_LANGSMITH_API_KEY="lsv2_xxxx"
```
**Note:** Settings configured in the UI take precedence over environment variables.
### Usage
You can run your Deep Agents in Debug Mode, which will execute the agent step by step. This will allow you to re-run the specific steps of the agent. This is intended to be used alongside the optimizer.
You can also turn off Debug Mode to run the full agent end-to-end.
### 📚 Resources
If the term "Deep Agents" is new to you, check out these videos!
[What are Deep Agents?](https://www.youtube.com/watch?v=433SmtTc0TA)
[Implementing Deep Agents](https://www.youtube.com/watch?v=TTMYJAw5tiA&t=701s)