Gpt Researcher

# Adding Features Guide

## Table of Contents
- [The 8-Step Pattern](#the-8-step-pattern)
- [Image Generation Case Study](#image-generation-case-study)
- [Testing New Features](#testing-new-features)

---

## The 8-Step Pattern

```
┌────────┐    ┌────────┐    ┌────────┐    ┌────────┐
│1.CONFIG│ →  │2.PROVIDER│ → │3.SKILL │ →  │4.AGENT │
└────────┘    └────────┘    └────────┘    └────────┘
     ↓             ↓             ↓             ↓
┌────────┐    ┌────────┐    ┌────────┐    ┌────────┐
│5.PROMPTS│ → │6.WEBSOCKET│→ │7.FRONTEND│→ │8.DOCS  │
└────────┘    └────────┘    └────────┘    └────────┘
```

### Step 1: Add Configuration

**File:** `gpt_researcher/config/variables/default.py`

```python
DEFAULT_CONFIG: BaseConfig = {
    "MY_FEATURE_ENABLED": False,
    "MY_FEATURE_MODEL": "model-name",
    "MY_FEATURE_MAX_ITEMS": 3,
}
```

**File:** `gpt_researcher/config/variables/base.py`

```python
class BaseConfig(TypedDict):
    "MY_FEATURE_ENABLED": bool
    "MY_FEATURE_MODEL": Union[str, None]
    "MY_FEATURE_MAX_ITEMS": int
```

### Step 2: Create Provider

**File:** `gpt_researcher/llm_provider/my_feature/my_provider.py`

```python
class MyFeatureProvider:
    def __init__(self, api_key: str = None, model: str = None):
        self.api_key = api_key or os.getenv("MY_API_KEY")
        self.model = model
    
    def is_enabled(self) -> bool:
        return bool(self.api_key and self.model)
    
    async def execute(self, input_data: str) -> Dict[str, Any]:
        # API implementation
        pass
```

Export in `gpt_researcher/llm_provider/__init__.py`.

### Step 3: Create Skill

**File:** `gpt_researcher/skills/my_feature.py`

```python
class MyFeatureSkill:
    def __init__(self, researcher):
        self.researcher = researcher
        self.config = researcher.cfg
        self.provider = MyFeatureProvider(...)
    
    def is_enabled(self) -> bool:
        return getattr(self.config, 'my_feature_enabled', False) and self.provider.is_enabled()
    
    async def execute(self, context: str, query: str) -> List[Dict]:
        if not self.is_enabled():
            return []
        
        await stream_output("logs", "my_feature_start", "🚀 Starting...", self.researcher.websocket)
        results = await self.provider.execute(context)
        await stream_output("logs", "my_feature_complete", "✅ Done", self.researcher.websocket)
        
        return results
```

Export in `gpt_researcher/skills/__init__.py`.

### Step 4: Integrate into Agent

**File:** `gpt_researcher/agent.py`

```python
def __init__(self, ...):
    if self.cfg.my_feature_enabled:
        from gpt_researcher.skills import MyFeatureSkill
        self.my_feature = MyFeatureSkill(self)
    else:
        self.my_feature = None
    self.my_feature_results = []

async def conduct_research(self, ...):
    # ... existing ...
    if self.my_feature and self.my_feature.is_enabled():
        self.my_feature_results = await self.my_feature.execute(self.context, self.query)
```

### Step 5: Update Prompts

**File:** `gpt_researcher/prompts.py`

```python
@staticmethod
def generate_my_feature_prompt(context: str, query: str) -> str:
    return f"""..."""
```

### Step 6: WebSocket Events

Already handled via `stream_output()` in skill.

### Step 7: Frontend (if needed)

**File:** `frontend/nextjs/hooks/useWebSocket.ts`

```typescript
if (data.content === 'my_feature_start') {
    setStatus('processing');
}
```

### Step 8: Documentation

Create `docs/docs/gpt-researcher/gptr/my_feature.md`.

---

## Image Generation Case Study

This section shows the **actual implementation** of the Image Generation feature as a reference.

### 1. Configuration Added

**File:** `gpt_researcher/config/variables/default.py`

```python
DEFAULT_CONFIG: BaseConfig = {
    # ... existing ...
    "IMAGE_GENERATION_MODEL": "models/gemini-2.5-flash-image",
    "IMAGE_GENERATION_MAX_IMAGES": 3,
    "IMAGE_GENERATION_ENABLED": False,
    "IMAGE_GENERATION_STYLE": "dark",  # dark, light, auto
}
`