gpt-researcher

name	gpt-researcher
description	GPT Researcher is an autonomous deep research agent that conducts web and local research, producing detailed reports with citations. Use this skill when helping developers understand, extend, debug, or integrate with GPT Researcher - including adding features, understanding the architecture, working with the API, customizing research workflows, adding new retrievers, integrating MCP data sources, or troubleshooting research pipelines. Use when this capability is needed.
metadata	{"author":"a-yeyang"}

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)

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

---

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

---

Source: a-yeyang/AI-researcher — distributed by TomeVault.