GPT Researcher Development Skill

GPT Researcher Development Skill

GPT Researcher is an LLM-based autonomous agent using a planner-executor-publisher pattern with parallelized agent work for speed and reliability.

Quick Start

Basic Python Usage

from gpt_researcher import GPTResearcher
import asyncio

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

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

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

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Key File Locations

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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

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Core Patterns

Adding a New Feature (8-Step Pattern)

1. Config → Add to gpt_researcher/​config/​variables/​default.py
2. Provider → Create in gpt_researcher/​llm_provider/​my_feature/
3. Skill → Create in gpt_researcher/​skills/​my_feature.py
4. Agent → Integrate in gpt_researcher/​agent.py
5. Prompts → Update gpt_researcher/​prompts.py
6. WebSocket → Events via stream_output()
7. Frontend → Handle events in useWebSocket.ts
8. 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

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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

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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

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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

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Critical Gotchas

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Reference Documentation