Set up DeerFlow 2.0 for AI-powered research and tasks

Olares version: 1.12.7+▾

Olares 1.12.7+Olares 1.12.6

DeerFlow is an open-source agent harness by ByteDance, built on LangGraph and LangChain. It orchestrates sub-agents, memory, and sandboxes to handle complex tasks through extensible skills.

DeerFlow 2.0 is a ground-up rewrite of the original DeerFlow. While version 1.0 was a deep research framework, version 2.0 is a general-purpose agent platform.

Learning objectives

In this guide, you will learn how to:

• Install DeerFlow 2.0 on Olares and configure it with a local model.
• Run tasks such as deep research.

Prerequisites

Before you begin, you need:

• An Olares device running Olares 1.12.7 or later, with sufficient disk space and memory.

• Access to Router for local model connections.

• The following model:

  Model type	Model	How to get it
  Chat	Qwen3.8-27B (llama.cpp)	Install from Market

Optional: Use a different model

Install another chat model from Market, or create a model instance with Engine Base apps. Then select it as the default chat model on Router's Default models page. Clients configured with default-chat use the new default without changing their model settings.

To keep a client on one specific model, use the full model name from Router's How to call this model window instead.

Install DeerFlow 2.0

1. Open Market and search for "DeerFlow 2.0".

2. Click Get, then click Install, and wait for the installation to complete.

Configure the model

DeerFlow 2.0 uses a config.yaml file for its core configuration. To connect it to a local model, add the model and its connection details to this file.

Get model connection details

How model connections work

On Olares 1.12.7 and later, AI clients connect through Olares Router. Router provides the Base URL and routes default-chat to the model selected on its Default models page.

This guide uses Qwen3.8-27B (llama.cpp) as the default chat model. For connection formats and API key requirements, see Connect AI apps.

For Qwen3.8-27B (llama.cpp):

1. Open Router from Launchpad. On Default models, set Qwen3.8-27B (llama.cpp) as the default chat model.

2. Go to LLM, find Qwen3.8-27B (llama.cpp), and click View connection example on its row.

3. In How to call this model, select Apps in Olares and copy the Base URL, including /v1.

4. For the model name, use default-chat.

   Selecting a model in your app

   default-chat is a routing name and does not appear in model lists loaded from Router. If the app you are connecting to Router loads a model list, add default-chat manually. If the app only supports selecting a listed model, select the full model name shown in Router's How to call this model window.

5. For the API key, leave the field empty. If the app you are connecting to Router requires a value, enter olares.

Edit config.yaml

1. Open Files and navigate to the DeerFlow 2.0 app data directory: /Data/deerflowv2/config/.

2. Open config.yaml, and click edit_square in the top-right corner to open the editor.

3. Under the models: section, add the model configuration below. Replace PASTE_ROUTER_BASE_URL with the Base URL copied from Router.

   models:
     - name: default-chat      # Unique identifier for the model
       display_name: Qwen3.8-27B      # Name shown in the UI
       use: langchain_openai:ChatOpenAI      # LangChain class for OpenAI-compatible APIs
       model: default-chat      # Model ID
       api_key: olares      # Use any non-empty text
       base_url: PASTE_ROUTER_BASE_URL
       supports_thinking: true      # Set to true if the model supports extended thinking

4. Click save to save the changes.

Restart to apply changes

1. Open Control Hub and select the DeerFlow 2.0 project.

2. Under Deployments, locate the backend container and click Restart.

   

3. In the confirmation dialog, confirm the restart.

4. Wait for the status icon to turn green.

Use DeerFlow 2.0

Once the model is configured, you can start using DeerFlow 2.0.

1. Open DeerFlow 2.0 from Launchpad and click Get Started with 2.0.

2. On the first launch, create the administrator account used to access and manage the current DeerFlow 2.0 instance:

   • Email: Enter the email address for the administrator account.
   • Password: Enter a password with at least eight characters.
   • Confirm Password: Enter the password again.

   Account data, including login details, stays within the current DeerFlow 2.0 instance. Account creation and sign-in do not connect to an external account service.

   Click Create Admin Account to access the chat interface.

   

3. Select your preferred execution mode.

   

   DeerFlow 2.0 offers several execution modes that control how the agent processes your request, from quick single-pass answers to multi-step research with sub-agents.

4. Enter your prompt in the chat box, or select a suggested topic for inspiration.

   For example, you can conduct deep research on a topic:

   You can also upload attachments and ask DeerFlow to use them as input:

FAQs

DeerFlow 2.0 does not generate a response

If the agent fails to start or hangs:

• Check model compatibility: Ensure the model you selected is properly configured in config.yaml. Verify the endpoint URL is correct.
• Check connection details: Use default-chat as the model and the Base URL copied from Router. Check the default chat model on Default models.

How do I enable follow-up suggestions?

By default, follow-up suggestions are turned off in DeerFlow 2.0 on Olares to reduce unnecessary GPU usage after a response is generated.

To enable it:

1. Open Control Hub and select the DeerFlow 2.0 project.

2. Under Deployments, click the deerflowv2-frontend deployment.

3. Click edit_square to edit the YAML.

4. Find the ENABLE_FOLLOWUP_SUGGESTIONS environment variable and change its value to 'true'.

5. Click Confirm to apply the changes.