# 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, currently we only support non-reasoning models, which means models like OpenAI's o1/o3 or DeepSeek's R1 are not supported yet, but we will add support for them in the future.

### Supported Models

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

> [!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
```

### 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 models?

DeerFlow supports the integration of Azure models. You can refer to [litellm Azure](https://docs.litellm.ai/docs/providers/azure). Configuration example of `conf.yaml`:
```yaml
BASIC_MODEL:
  model: "azure/gpt-4o-2024-08-06"
  api_base: $AZURE_API_BASE
  api_version: $AZURE_API_VERSION
  api_key: $AZURE_API_KEY
```
