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Set up DeerFlow 2.0 for AI-powered research and tasks ​

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.

This guide covers installing DeerFlow 2.0 on Olares and configuring it with a local model, using Qwen3.5 27B Q4_K_M (Ollama) as an example.

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 ​

  • A model app installed from Market with the model fully downloaded.

Install DeerFlow 2.0 ​

  1. Open Market and search for "DeerFlow 2.0". 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 your local model, add a model entry that points to your model app's shared endpoint.

Get the model endpoint and model name ​

  1. Open the model app from Launchpad. The model name is displayed on the page (e.g., qwen3.5:27b-q4_K_M). Note it for later. Get model name

  2. Open Settings, then navigate to Application > your model app (e.g., Qwen3.5 27B Q4_K_M (Ollama)).

  3. Under Shared entrances, select the model app to view the endpoint URL.

    Get shared endpoint

  4. Copy the shared endpoint. For example:

    text
    http://94a553e00.shared.olares.com

Edit config.yaml ​

  1. Open Files and navigate to the DeerFlow 2.0 app data directory: Application/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 your model configuration using the shared endpoint you copied. For example:

    yaml
    models:
  - name: qwen3.5:27b-q4_K_M            # Unique identifier for the model
    display_name: Qwen3.5 27B            # Name shown in the UI
    use: langchain_openai:ChatOpenAI     # LangChain class for OpenAI-compatible APIs
    model: qwen3.5:27b-q4_K_M           # Model ID
    api_key: ollama                      # Use any non-empty text
    base_url: http://94a553e00.shared.olares.com/v1  # Shared endpoint with /v1 suffix
    supports_thinking: true              # Set to true if the model supports extended thinking

    Edit config.yaml

  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.

    Restart DeerFlow 2.0

  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 to access the chat interface.

  2. Select your preferred execution mode.

    Select 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.

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

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

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

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 endpoint configuration: Check that the API endpoint includes the /v1 suffix.

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'. Enable follow-up suggestions

  5. Click Confirm to apply the changes.