Install DeerFlow Effortlessly: ServBay Provides a One-Stop Solution

Has the AI world been as lively as Super bowl recently? DeepSeek's buzz hasn't faded, Qwen3 came to steal the show, and now, ByteDance's DeerFlow is making a dazzling entrance with its halo!

DeerFlow: An Accelerator Born for Deep Research

DeerFlow is a Deep Research project open-sourced by ByteDance on GitHub. It helps you collect and organize literature data, perform in-depth analysis, assist with model training and result validation, and even helps polish reports, generate PPTs, and podcasts! It's practically a "divine soldier descending from the heavens" to rescue research dogs from the depths of despair!

Traditional Deployment's "Roadblock": The Invisible Barrier of Environment Configuration

However, like many cutting-edge open-source projects, experiencing the powerful features of DeerFlow first often requires overcoming the hurdle of environment configuration.

According to the official documentation, DeerFlow has clear and relatively strict requirements for the operating environment: Python version needs to be 3.12 or higher, and Node.js version needs to reach 22 or higher.

For many developers, precisely managing and switching multiple Python or Node.js versions in a local environment, ensuring dependency isolation and correct activation (e.g., using tools like pyenv, nvm, and manually configuring environment variables), is often a complex and time-consuming task. A slight oversight can easily lead to the quagmire of version conflicts. This initial environment setup difficulty undoubtedly consumes developers' valuable energy and can even become a "deterrent" to exploring new technologies.

ServBay: The Elegant Way to Deploy DeerFlow on macOS

Fortunately, for the vast number of macOS users, we have a more efficient and elegant option—ServBay. As a one-stop local development environment designed specifically for macOS, ServBay can perfectly resolve the environment challenges during the DeerFlow deployment process, allowing developers to focus their energy on innovation itself.

The Core Advantages of Choosing ServBay to Deploy DeerFlow Are:

1. Fine-grained Multi-Version Management and One-Click Switching: This is precisely ServBay's "killer feature" for meeting DeerFlow's specific version requirements. ServBay allows users to easily install and manage multiple different versions of Python (such as Python 3.11, 3.12, 3.14, etc.) and Node.js (such as Node.js 18, 20, 22, 23, etc.) through an intuitive graphical interface. Need to enable the required Python 3.12+ and Node.js 22+ for DeerFlow? Just a few clicks in the ServBay interface are enough to complete version activation and switching. ServBay automatically handles complex paths and dependencies at the underlying level, ensuring a clean and accurate environment.

2. A True One-Stop Integrated Development Platform: ServBay's capabilities extend far beyond Python and Node.js management. It natively integrates PHP (multiple versions), MySQL, MariaDB, PostgreSQL, Redis, Memcached, as well as mainstream web servers like Caddy, Nginx, and Apache. This means that if your DeerFlow project requires database support or other backend services in the future, ServBay can still provide a one-stop solution without needing to configure additional tools.

3. Perfect Environment Isolation Mechanism: ServBay provides excellent isolation for each enabled service and version. The Python 3.12 and Node.js 22 environment you configure for DeerFlow will be completely isolated from other projects or other version environments in the system, effectively avoiding potential conflicts while also ensuring the cleanliness and stability of the macOS system environment.

4. Ultimate Ease of Use and Intuitive Operation: ServBay completely bypasses cumbersome command-line configuration and obscure configuration file editing. Its clean and intuitive graphical user interface makes operations such as service installation, version switching, log viewing, and configuration modification unprecedentedly simple and efficient.

Practical Exercise: Quickly Set Up the DeerFlow Running Environment Using ServBay

Before starting, please ensure that ServBay is successfully installed on your macOS system. If not already installed, please visit the official ServBay website (https://www.servbay.com) to download the latest version and complete the installation. The installation process is the same as for conventional macOS applications, simple and straightforward.

Step One: Install Python 3.12+ via ServBay

• Launch the ServBay application.
• In the left navigation bar, select the "Packages" option.
• Find Python in the service list. You will see multiple Python versions supported by ServBay.
• Select Python 3.12 or any newer version and click the download or install button on the right. ServBay will automatically complete the download and installation process.

Step Two: Install Node.js 22+ via ServBay

Similarly, in ServBay's "Packages" management interface, find Node.js and select Node.js 22 or any newer version to download.

Step Three: Verify Environment Configuration

After configuration, verification is a crucial step to ensure everything is ready.

You should see output showing Python 3.13.3 and Node.js 22.14.0 respectively, which indicates that ServBay has successfully configured the running environment that meets DeerFlow's requirements.

Obtain and Install the DeerFlow Project

Basic Configuration

With the environment fully prepared, the next step is the installation and configuration of the DeerFlow project itself.

# Clone the repository
git clone https://github.com/bytedance/deer-flow.git
cd deer-flow

# Install dependencies, uv will take care of the python interpreter and venv creation, and install the required packages
uv sync

# Configure .env with your API keys
# Tavily: https://app.tavily.com/home
# Brave_SEARCH: https://brave.com/search/api/
# volcengine TTS: Add your TTS credentials if you have them
cp .env.example .env

# See the 'Supported Search Engines' and 'Text-to-Speech Integration' sections below for all available options

# Configure conf.yaml for your LLM model and API keys
# Please refer to 'docs/configuration_guide.md' for more details
cp conf.yaml.example conf.yaml

# Install marp for ppt generation
# https://github.com/marp-team/marp-cli?tab=readme-ov-file#use-package-manager
brew install marp-cli

Highlight! Don't forget to modify the .env and conf.yaml configurations, otherwise you'll get a 401 error.

The official default search engine is Tavily (https://app.tavily.com/home), which is a dedicated search API for AI applications. VolcEngine is not needed for now, so it's left blank. The modified commands look like this:

# Search Engine
SEARCH_API=tavily
TAVILY_API_KEY=tvly-xxx
# JINA_API_KEY=jina_xxx # Optional, default is None

# Optional, volcengine TTS for generating podcast
VOLCENGINE_TTS_APPID=xxx
VOLCENGINE_TTS_ACCESS_TOKEN=xxx
# VOLCENGINE_TTS_CLUSTER=volcano_tts # Optional, default is volcano_tts
# VOLCENGINE_TTS_VOICE_TYPE=BV700_V2_streaming # Optional, default is BV700_V2_streaming

Similarly, we need to modify conf.yaml. This mainly involves the base model used. The official recommendation is ChatGPT, but I think Gemini is better and the API is free.

BASIC_MODEL:
  base_url: "https://generativelanguage.googleapis.com/v1beta/openai/"
  model: "gemini-2.0-flash"
  api_key: YOUR_API_KEY

Once installation and basic configuration are complete, you can run DeerFlow.

# Run the project in a bash-like shell
uv run main.py

Web UI Interaction

However, this dark terminal is really unsightly. So, you can install a Web UI for interaction.
The official method is to open this Web UI using localhost, but I highly recommend using ServBay. By creating a reverse proxy through ServBay with a custom domain, it's much more convenient to use.

• In the left navigation menu of ServBay, find "Websites", and click the "+" button below to add a new website.
• Name: Give it a name you like. Like deer-servbay.test
• Domain: Customize an easy-to-remember domain name.
• Website Type: Select Reverse Proxy.
• IP Address: Enter 127.0.0.1.
• Port: 3000.

Enter the command to start bootstrap.sh

# Run both the backend and frontend servers in development mode
# On macOS/Linux
./bootstrap.sh -d

Enter your custom domain (deer-servbay.test) in the browser, and you can interact with DeerFlow.

Isn't it incredibly convenient?

Advanced Play

Just when I thought DeerFlow could only run online, I saw that it actually supports Ollama, which means it can also be deployed locally!

Time to pull out the ultimate tool, ServBay.
ServBay has long supported Ollama. After installing it, you can deploy Qwen3 locally (What? You said DeepSeek? It's no longer popular, Qwen3 is the king now) and run it happily on your local machine.

The Broad Application Prospects of DeerFlow: Empowering Deep Exploration in Diverse Scenarios

Having mastered how to efficiently deploy DeerFlow through ServBay, we can't help but wonder: in which areas can this powerful deep research platform shine? From its core functions—deep research assistance, multimodal content processing, AI-enhanced report editing, and presentation generation, among others—we can foresee its huge potential in multiple key scenarios:

Academic Research and Scientific Exploration

• Literature Review and Knowledge Discovery: DeerFlow can assist researchers in quickly collecting, filtering, and analyzing massive amounts of academic literature, patent data, and industry reports, automatically extracting key information, and generating structured knowledge graphs or review summaries, significantly improving literature research efficiency.
• Experimental Data Analysis and Interpretation: Combining its data processing capabilities, researchers can use DeerFlow for in-depth analysis of experimental data, perform pattern recognition and trend prediction with AI models, and assist in generating preliminary analysis reports.
• Research Output Presentation and Dissemination: Through AI-enhanced report editing and automatic PPT generation, researchers can write research papers, create academic reports and presentation materials more efficiently and professionally, facilitating the rapid dissemination and exchange of research findings.

Business Intelligence and Market Analysis

• Industry Trends and Competitive Intelligence: Business analysts can use DeerFlow to continuously monitor industry news, social media dynamics, and competitive intelligence, perform sentiment analysis and trend identification through AI, form in-depth insights reports, and provide basis for decision-making.
• User Research and Product Feedback: Integrating multi-source information such as user survey data, product usage logs, and customer service records, DeerFlow can help product teams deeply understand user needs and pain points, and optimize product design and operational strategies.
• Content Marketing and Knowledge Sharing: Marketing teams can leverage DeerFlow to quickly generate industry white papers, technical interpretation articles, online seminar PPTs, and even podcast scripts, improving content production efficiency and professionalism.

Education and Knowledge Services

• Personalized Learning Resource Generation: Teachers or educational content developers can use DeerFlow to quickly integrate relevant knowledge for specific topics or courses, generating customized learning materials, courseware, and quizzes.
• Complex Concept Interpretation and Visualization: For complex scientific principles or technical concepts, DeerFlow can assist in generating easy-to-understand graphical reports or presentations, helping students better absorb knowledge.

Finance and Investment Analysis

• Financial Report Analysis and Risk Assessment: Analysts can use DeerFlow for in-depth mining of listed companies' financial statements and news announcements, combined with macroeconomic data, to perform more comprehensive risk assessments and investment value analyses.
• Market Sentiment and Public Opinion Monitoring: Real-time tracking of news and social media discussions related to the financial market, analyzing market sentiment through AI, and providing reference for trading decisions.

Legal and Compliance Fields

• Case Studies and Legal Literature Retrieval: Lawyers and legal personnel can use DeerFlow to efficiently retrieve and analyze large amounts of legal documents, precedents, and regulations, assisting in case preparation and legal consultation.
• Compliance Review Report Generation: Assisting in the generation of preliminary compliance review reports, sorting out relevant clauses and potential risk points.

Conclusion: Empowered by ServBay, Explore More Ways to Play with DeerFlow

With the strong support of ServBay, the process of setting up the running environment for cutting-edge projects like DeerFlow with specific environment dependencies has been greatly simplified. The problems of version management, environment isolation, etc., which might have been challenging, are easily solved by ServBay's intuitive graphical interface and automated processing mechanism. It can also work perfectly with Ollama.

The emergence of DeerFlow represents a new direction for AI technology to empower deep information processing and knowledge creation. With the continuous improvement of its functions and the sustained development of its community ecosystem, we have reason to believe that it will play a key role in more areas requiring deep thinking, efficient information processing, and professional content generation. And efficient development environments like ServBay pave the way for every developer eager to explore and apply these cutting-edge technologies, making the innovation journey smoother.