gpt-researcher

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

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

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