async def main(): researcher = GPTResearcher( query="What are the latest AI developments?", report_type="research_report", # or detailed_report, deep, outline_report report_source="web", # or local, hybrid ) await researcher.conduct_research() report = await researcher.write_report() print(report)

asyncio.run(main())


### Run Servers

```bash
# Backend
python -m uvicorn backend.server.server:app --reload --port 8000

# Frontend
cd frontend/nextjs && npm install && npm run dev

Key File Locations

Need	Primary File	Key Classes
Main orchestrator	`gpt_researcher/agent.py`	`GPTResearcher`
Research logic	`gpt_researcher/skills/researcher.py`	`ResearchConductor`
Report writing	`gpt_researcher/skills/writer.py`	`ReportGenerator`
All prompts	`gpt_researcher/prompts.py`	`PromptFamily`
Configuration	`gpt_researcher/config/config.py`	`Config`
Config defaults	`gpt_researcher/config/variables/default.py`	`DEFAULT_CONFIG`
API server	`backend/server/app.py`	FastAPI `app`
Search engines	`gpt_researcher/retrievers/`	Various retrievers

Architecture Overview

User Query → GPTResearcher.__init__()
                │
                ▼
         choose_agent() → (agent_type, role_prompt)
                │
                ▼
         ResearchConductor.conduct_research()
           ├── plan_research() → sub_queries
           ├── For each sub_query:
           │     └── _process_sub_query() → context
           └── Aggregate contexts
                │
                ▼
         [Optional] ImageGenerator.plan_and_generate_images()
                │
                ▼
         ReportGenerator.write_report() → Markdown report

For detailed architecture diagrams: See references/architecture.md

Core Patterns

Adding a New Feature (8-Step Pattern)

Config → Add to gpt_researcher/config/variables/default.py
Provider → Create in gpt_researcher/llm_provider/my_feature/
Skill → Create in gpt_researcher/skills/my_feature.py
Agent → Integrate in gpt_researcher/agent.py
Prompts → Update gpt_researcher/prompts.py
WebSocket → Events via stream_output()
Frontend → Handle events in useWebSocket.ts
Docs → Create docs/docs/gpt-researcher/gptr/my_feature.md

For complete feature addition guide with Image Generation case study: See references/adding-features.md

Adding a New Retriever

# 1. Create: gpt_researcher/retrievers/my_retriever/my_retriever.py
class MyRetriever:
    def __init__(self, query: str, headers: dict = None):
        self.query = query
    
    async def search(self, max_results: int = 10) -> list[dict]:
        # Return: [{"title": str, "href": str, "body": str}]
        pass

# 2. Register in gpt_researcher/actions/retriever.py
case "my_retriever":
    from gpt_researcher.retrievers.my_retriever import MyRetriever
    return MyRetriever

# 3. Export in gpt_researcher/retrievers/__init__.py

For complete retriever documentation: See references/retrievers.md

Configuration

Config keys are lowercased when accessed:

# In default.py: "SMART_LLM": "gpt-4o"
# Access as: self.cfg.smart_llm  # lowercase!

Priority: Environment Variables → JSON Config File → Default Values

For complete configuration reference: See references/config-reference.md

Common Integration Points

WebSocket Streaming

class WebSocketHandler:
    async def send_json(self, data):
        print(f"[{data['type']}] {data.get('output', '')}")

researcher = GPTResearcher(query="...", websocket=WebSocketHandler())

MCP Data Sources

researcher = GPTResearcher(
    query="Open source AI projects",
    mcp_configs=[{
        "name": "github",
        "command": "npx",
        "args": ["-y", "@modelcontextprotocol/server-github"],
        "env": {"GITHUB_TOKEN": os.getenv("GITHUB_TOKEN")}
    }],
    mcp_strategy="deep",  # or "fast", "disabled"
)

For MCP integration details: See references/mcp.md

Deep Research Mode

researcher = GPTResearcher(
    query="Comprehensive analysis of quantum computing",
    report_type="deep",  # Triggers recursive tree-like exploration
)

For deep research configuration: See references/deep-research.md

Error Handling

Always use graceful degradation in skills:

async def execute(self, ...):
    if not self.is_enabled():
        return []  # Don't crash
    
    try:
        result = await self.provider.execute(...)
        return result
    except Exception as e:
        await stream_output("logs", "error", f"⚠️ {e}", self.websocket)
        return []  # Graceful degradation

Critical Gotchas

❌ Mistake	✅ Correct
`config.MY_VAR`	`config.my_var` (lowercased)
Editing pip-installed package	`pip install -e .`
Forgetting async/await	All research methods are async
`websocket.send_json()` on None	Check `if websocket:` first
Not registering retriever	Add to `retriever.py` match statement

Reference Documentation

Topic	File
System architecture & diagrams	references/architecture.md
Core components & signatures	references/components.md
Research flow & data flow	references/flows.md
Prompt system	references/prompts.md
Retriever system	references/retrievers.md
MCP integration	references/mcp.md
Deep research mode	references/deep-research.md
Multi-agent system	references/multi-agents.md
Adding features guide	references/adding-features.md
Advanced patterns	references/advanced-patterns.md
REST & WebSocket API	references/api-reference.md
Configuration variables	references/config-reference.md

gpt-researcher

GPT Researcher Development Skill

Quick Start

Basic Python Usage