# Configuration Guide

## Quick Settings

Copy the `conf.yaml.example` file to `conf.yaml` and modify the configurations to match your specific settings and requirements.

```bash
cd deer-flow
cp conf.yaml.example conf.yaml
```

## Which models does DeerFlow support?

In DeerFlow, we currently only support non-reasoning models. This means models like OpenAI's o1/o3 or DeepSeek's R1 are not supported yet, but we plan to add support for them in the future. Additionally, all Gemma-3 models are currently unsupported due to the lack of tool usage capabilities.

### Supported Models

`doubao-1.5-pro-32k-250115`, `gpt-4o`, `qwen-max-latest`,`qwen3-235b-a22b`,`qwen3-coder`, `gemini-2.0-flash`, `deepseek-v3`, and theoretically any other non-reasoning chat models that implement the OpenAI API specification.

### Local Model Support

DeerFlow supports local models through OpenAI-compatible APIs:

- **Ollama**: `http://localhost:11434/v1` (tested and supported for local development)

See the `conf.yaml.example` file for detailed configuration examples.

> [!NOTE]
> The Deep Research process requires the model to have a **longer context window**, which is not supported by all models.
> A work-around is to set the `Max steps of a research plan` to `2` in the settings dialog located on the top right corner of the web page,
> or set `max_step_num` to `2` when invoking the API.

### How to switch models?
You can switch the model in use by modifying the `conf.yaml` file in the root directory of the project, using the configuration in the [litellm format](https://docs.litellm.ai/docs/providers/openai_compatible).

---

### How to use OpenAI-Compatible models?

DeerFlow supports integration with OpenAI-Compatible models, which are models that implement the OpenAI API specification. This includes various open-source and commercial models that provide API endpoints compatible with the OpenAI format. You can refer to [litellm OpenAI-Compatible](https://docs.litellm.ai/docs/providers/openai_compatible) for detailed documentation.
The following is a configuration example of `conf.yaml` for using OpenAI-Compatible models:

```yaml
# An example of Doubao models served by VolcEngine
BASIC_MODEL:
  base_url: "https://ark.cn-beijing.volces.com/api/v3"
  model: "doubao-1.5-pro-32k-250115"
  api_key: YOUR_API_KEY

# An example of Aliyun models
BASIC_MODEL:
  base_url: "https://dashscope.aliyuncs.com/compatible-mode/v1"
  model: "qwen-max-latest"
  api_key: YOUR_API_KEY

# An example of deepseek official models
BASIC_MODEL:
  base_url: "https://api.deepseek.com"
  model: "deepseek-chat"
  api_key: YOUR_API_KEY

# An example of Google Gemini models using OpenAI-Compatible interface
BASIC_MODEL:
  base_url: "https://generativelanguage.googleapis.com/v1beta/openai/"
  model: "gemini-2.0-flash"
  api_key: YOUR_API_KEY
```
The following is a configuration example of `conf.yaml` for using best opensource OpenAI-Compatible models:
```yaml
# Use latest deepseek-v3 to handle basic tasks, the open source SOTA model for basic tasks
BASIC_MODEL:
  base_url: https://api.deepseek.com
  model: "deepseek-v3"
  api_key: YOUR_API_KEY
  temperature: 0.6
  top_p: 0.90
# Use qwen3-235b-a22b to handle reasoning tasks, the open source SOTA model for reasoning
REASONING_MODEL:
  base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
  model: "qwen3-235b-a22b-thinking-2507"
  api_key: YOUR_API_KEY
  temperature: 0.6
  top_p: 0.90
# Use qwen3-coder-480b-a35b-instruct to handle coding tasks, the open source SOTA model for coding
CODE_MODEL:
  base_url: https://dashscope.aliyuncs.com/compatible-mode/v1
  model: "qwen3-coder-480b-a35b-instruct"
  api_key: YOUR_API_KEY
  temperature: 0.6
  top_p: 0.90
```
In addition, you need to set the `AGENT_LLM_MAP` in `src/config/agents.py` to use the correct model for each agent. For example:

```python
# Define agent-LLM mapping
AGENT_LLM_MAP: dict[str, LLMType] = {
    "coordinator": "reasoning",
    "planner": "reasoning",
    "researcher": "reasoning",
    "coder": "basic",
    "reporter": "basic",
    "podcast_script_writer": "basic",
    "ppt_composer": "basic",
    "prose_writer": "basic",
    "prompt_enhancer": "basic",
}


### How to use Google AI Studio models?

DeerFlow supports native integration with Google AI Studio (formerly Google Generative AI) API. This provides direct access to Google's Gemini models with their full feature set and optimized performance.

To use Google AI Studio models, you need to:
1. Get your API key from [Google AI Studio](https://aistudio.google.com/app/apikey)
2. Set the `platform` field to `"google_aistudio"` in your configuration
3. Configure your model and API key

The following is a configuration example for using Google AI Studio models:

```yaml
# Google AI Studio native API (recommended for Google models)
BASIC_MODEL:
  platform: "google_aistudio"
  model: "gemini-2.5-flash"  # or "gemini-1.5-pro" ,...
  api_key: YOUR_GOOGLE_API_KEY # Get from https://aistudio.google.com/app/apikey

```

**Note:** The `platform: "google_aistudio"` field is required to distinguish from other providers that may offer Gemini models through OpenAI-compatible APIs.
```

### How to use models with self-signed SSL certificates?

If your LLM server uses self-signed SSL certificates, you can disable SSL certificate verification by adding the `verify_ssl: false` parameter to your model configuration:

```yaml
BASIC_MODEL:
  base_url: "https://your-llm-server.com/api/v1"
  model: "your-model-name"
  api_key: YOUR_API_KEY
  verify_ssl: false  # Disable SSL certificate verification for self-signed certificates
```

> [!WARNING]
> Disabling SSL certificate verification reduces security and should only be used in development environments or when you trust the LLM server. In production environments, it's recommended to use properly signed SSL certificates.

### How to use Ollama models?

DeerFlow supports the integration of Ollama models. You can refer to [litellm Ollama](https://docs.litellm.ai/docs/providers/ollama). <br>
The following is a configuration example of `conf.yaml` for using Ollama models(you might need to run the 'ollama serve' first):

```yaml
BASIC_MODEL:
  model: "model-name"  # Model name, which supports the completions API(important), such as: qwen3:8b, mistral-small3.1:24b, qwen2.5:3b
  base_url: "http://localhost:11434/v1" # Local service address of Ollama, which can be started/viewed via ollama serve
  api_key: "whatever"  # Mandatory, fake api_key with a random string you like :-)
```

### How to use OpenRouter models?

DeerFlow supports the integration of OpenRouter models. You can refer to [litellm OpenRouter](https://docs.litellm.ai/docs/providers/openrouter). To use OpenRouter models, you need to:
1. Obtain the OPENROUTER_API_KEY from OpenRouter (https://openrouter.ai/) and set it in the environment variable.
2. Add the `openrouter/` prefix before the model name.
3. Configure the correct OpenRouter base URL.

The following is a configuration example for using OpenRouter models:
1. Configure OPENROUTER_API_KEY in the environment variable (such as the `.env` file)
```ini
OPENROUTER_API_KEY=""
```
2. Set the model name in `conf.yaml`
```yaml
BASIC_MODEL:
  model: "openrouter/google/palm-2-chat-bison"
```

Note: The available models and their exact names may change over time. Please verify the currently available models and their correct identifiers in [OpenRouter's official documentation](https://openrouter.ai/docs).


### How to use Azure OpenAI chat models?

DeerFlow supports the integration of Azure OpenAI chat models. You can refer to [AzureChatOpenAI](https://python.langchain.com/api_reference/openai/chat_models/langchain_openai.chat_models.azure.AzureChatOpenAI.html). Configuration example of `conf.yaml`:
```yaml
BASIC_MODEL:
  model: "azure/gpt-4o-2024-08-06"
  azure_endpoint: $AZURE_OPENAI_ENDPOINT
  api_version: $OPENAI_API_VERSION
  api_key: $AZURE_OPENAI_API_KEY
```

### How to configure context length for different models

Different models have different context length limitations. DeerFlow provides a method to control the context length between different models. You can configure the context length between different models in the `conf.yaml` file. For example:
```yaml
BASIC_MODEL:
  base_url: https://ark.cn-beijing.volces.com/api/v3
  model: "doubao-1-5-pro-32k-250115"
  api_key: ""
  token_limit: 128000
```
This means that the context length limit using this model is 128k. 

The context management doesn't work if the token_limit is not set.

## About Search Engine

### Supported Search Engines
DeerFlow supports the following search engines:
- Tavily
- InfoQuest
- DuckDuckGo
- Brave Search
- Arxiv
- Searx
- Serper
- Wikipedia

### How to use Serper Search?

To use Serper as your search engine, you need to:
1. Get your API key from [Serper](https://serper.dev/)
2. Set `SEARCH_API=serper` in your `.env` file
3. Set `SERPER_API_KEY=your_api_key` in your `.env` file

### How to control search domains for Tavily?

DeerFlow allows you to control which domains are included or excluded in Tavily search results through the configuration file. This helps improve search result quality and reduce hallucinations by focusing on trusted sources.

`Tips`: it only supports Tavily currently. 

You can configure domain filtering and search results in your `conf.yaml` file as follows:

```yaml
SEARCH_ENGINE:
  engine: tavily
  # Only include results from these domains (whitelist)
  include_domains:
    - trusted-news.com
    - gov.org
    - reliable-source.edu
  # Exclude results from these domains (blacklist)
  exclude_domains:
    - unreliable-site.com
    - spam-domain.net
  # Include images in search results, default: true
  include_images: false
  # Include image descriptions in search results, default: true
  include_image_descriptions: false
  # Include raw content in search results, default: true
  include_raw_content: false
```

### How to post-process Tavily search results

DeerFlow can post-process Tavily search results:
* Remove duplicate content
* Filter low-quality content: Filter out results with low relevance scores
* Clear base64 encoded images
* Length truncation: Truncate each search result according to the user-configured length

The filtering of low-quality content and length truncation depend on user configuration, providing two configurable parameters:
* min_score_threshold: Minimum relevance score threshold, search results below this threshold will be filtered. If not set, no filtering will be performed;
* max_content_length_per_page: Maximum length limit for each search result content, parts exceeding this length will be truncated. If not set, no truncation will be performed;

These two parameters can be configured in `conf.yaml` as shown below:
```yaml
SEARCH_ENGINE:
  engine: tavily
  include_images: true
  min_score_threshold: 0.4
  max_content_length_per_page: 5000
```
That's meaning that the search results will be filtered based on the minimum relevance score threshold and truncated to the maximum length limit for each search result content.

## Web Search Toggle

DeerFlow allows you to disable web search functionality, which is useful for environments without internet access or when you want to use only local RAG knowledge bases.

### Configuration

You can disable web search in your `conf.yaml` file:

```yaml
# Disable web search (use only local RAG)
ENABLE_WEB_SEARCH: false
```

Or via API request parameter:

```json
{
  "messages": [{"role": "user", "content": "Research topic"}],
  "enable_web_search": false
}
```

> [!WARNING]
> If you disable web search, make sure to configure local RAG resources; otherwise, the researcher will operate in pure LLM reasoning mode without external data sources.

### Behavior When Web Search is Disabled

- **Background investigation**: Skipped entirely (relies on web search)
- **Researcher node**: Will use only RAG retriever tools if configured
- **Pure reasoning mode**: If no RAG resources are available, the researcher will rely solely on LLM reasoning

---

## RAG (Retrieval-Augmented Generation) Configuration

DeerFlow supports multiple RAG providers for document retrieval. Configure the RAG provider by setting environment variables.

### Supported RAG Providers

- **RAGFlow**: Document retrieval using RAGFlow API
- **VikingDB Knowledge Base**: ByteDance's VikingDB knowledge base service
- **Milvus**: Open-source vector database for similarity search
- **Qdrant**: Open-source vector search engine with cloud and self-hosted options
- **MOI**: Hybrid database for enterprise users
- **Dify**: AI application platform with RAG capabilities

### Qdrant Configuration

To use Qdrant as your RAG provider, set the following environment variables:

```bash
# RAG_PROVIDER: qdrant (using Qdrant Cloud or self-hosted)
RAG_PROVIDER=qdrant
QDRANT_LOCATION=https://xyz-example.eu-central.aws.cloud.qdrant.io:6333
QDRANT_API_KEY=<your_qdrant_api_key>
QDRANT_COLLECTION=documents
QDRANT_EMBEDDING_PROVIDER=openai  # support openai, dashscope
QDRANT_EMBEDDING_BASE_URL=
QDRANT_EMBEDDING_MODEL=text-embedding-ada-002
QDRANT_EMBEDDING_API_KEY=<your_embedding_api_key>
QDRANT_AUTO_LOAD_EXAMPLES=true  # automatically load example markdown files
```

### Milvus Configuration

To use Milvus as your RAG provider, set the following environment variables:

```bash
# RAG_PROVIDER: milvus  (using free milvus instance on zilliz cloud: https://docs.zilliz.com/docs/quick-start )
RAG_PROVIDER=milvus
MILVUS_URI=<endpoint_of_self_hosted_milvus_or_zilliz_cloud>
MILVUS_USER=<username_of_self_hosted_milvus_or_zilliz_cloud>
MILVUS_PASSWORD=<password_of_self_hosted_milvus_or_zilliz_cloud>
MILVUS_COLLECTION=documents
MILVUS_EMBEDDING_PROVIDER=openai
MILVUS_EMBEDDING_BASE_URL=
MILVUS_EMBEDDING_MODEL=
MILVUS_EMBEDDING_API_KEY=

# RAG_PROVIDER: milvus  (using milvus lite on Mac or Linux)
RAG_PROVIDER=milvus
MILVUS_URI=./milvus_demo.db
MILVUS_COLLECTION=documents
MILVUS_EMBEDDING_PROVIDER=openai
MILVUS_EMBEDDING_BASE_URL=
MILVUS_EMBEDDING_MODEL=
MILVUS_EMBEDDING_API_KEY=
```

---

## Multi-Turn Clarification (Optional)

An optional feature that helps clarify vague research questions through conversation. **Disabled by default.**

### Enable via Command Line

```bash
# Enable clarification for vague questions
uv run main.py "Research AI" --enable-clarification

# Set custom maximum clarification rounds
uv run main.py "Research AI" --enable-clarification --max-clarification-rounds 3

# Interactive mode with clarification
uv run main.py --interactive --enable-clarification --max-clarification-rounds 3
```

### Enable via API

```json
{
  "messages": [{"role": "user", "content": "Research AI"}],
  "enable_clarification": true,
  "max_clarification_rounds": 3
}
```

### Enable via UI Settings

1. Open DeerFlow web interface
2. Navigate to **Settings** → **General** tab
3. Find **"Enable Clarification"** toggle
4. Turn it **ON** to enable multi-turn clarification. Clarification is **disabled** by default. You need to manually enable it through any of the above methods. When clarification is enabled, you'll see **"Max Clarification Rounds"** field appear below the toggle
6. Set the maximum number of clarification rounds (default: 3, minimum: 1)
7. Click **Save** to apply changes

**When enabled**, the Coordinator will ask up to the specified number of clarifying questions for vague topics before starting research, improving report relevance and depth. The `max_clarification_rounds` parameter controls how many rounds of clarification are allowed.


**Note**: The `max_clarification_rounds` parameter only takes effect when `enable_clarification` is set to `true`. If clarification is disabled, this parameter is ignored.
