Running Research (Usage)

Prerequisites

• Venv: ~/.local/share/gpt-researcher/venv/ (run setup.sh if missing)
• Config: ~/.config/gpt-researcher/config.json
• API key: OPENAI_API_KEY must be set in ~/cc/.env
• First-time setup: bash ~/.claude/skills/gpt-researcher/scripts/setup.sh
• Enhancement (profiles): bash ~/.claude/skills/gpt-researcher/scripts/enhance.sh

Research Profiles

Profiles auto-degrade: if a premium key is missing, that retriever is skipped.

Commands

Standard Research Report (~2 min)

bash ~/.claude/skills/gpt-researcher/scripts/research.sh "QUERY" research_report

Deep Research with profile (~varies)

bash ~/.claude/skills/gpt-researcher/scripts/research.sh "QUERY" deep --profile standard
bash ~/.claude/skills/gpt-researcher/scripts/research.sh "QUERY" deep --profile thorough
bash ~/.claude/skills/gpt-researcher/scripts/research.sh "QUERY" deep --profile government

Quick smoke test (~30s, ~$0.10)

bash ~/.claude/skills/gpt-researcher/scripts/research.sh "QUERY" research_report --profile quick

Multi-Agent Research (7-agent LangGraph pipeline, magazine-quality reports)

# Autonomous mode — runs all 7 agents end-to-end
bash ~/.claude/skills/gpt-researcher/scripts/research.sh "QUERY" multi --profile standard

# Human review mode — pauses after outline for approval
bash ~/.claude/skills/gpt-researcher/scripts/research.sh "QUERY" multi --profile thorough --review

# Quick smoke test (~3-5 min, ~$0.50)
bash ~/.claude/skills/gpt-researcher/scripts/research.sh "QUERY" multi --profile quick

Multi-agent profiles:

Output: ~/cc/output/research/<date>-<slug>/ with report.md, metadata.json, and optionally report.pdf/docx.

Pipeline: Browser → Planner → [Human Review] → Parallel Researchers → Reviewer/Reviser → Writer → Publisher.

All report types: research_report, detailed_report, deep, outline_report, resource_report, multi

Output Format (JSON)

All scripts output JSON to stdout:

{
  "report": "# Full markdown report with citations...",
  "sources": ["https://...", "https://..."],
  "source_count": 23,
  "costs_usd": 0.12,
  "elapsed_seconds": 145.3,
  "report_type": "research_report",
  "query": "original query",
  "profile": "standard",
  "config_path": "/path/to/profile.json"
}

Parallel with last60days

For maximum research coverage, dispatch both skills as parallel Task agents:

• Task 1: GPT Researcher deep mode (deep web research, academic sources, 50+ sources)
• Task 2: /last60days (engagement-scored social signals: Reddit, X, HN, GitHub, YouTube) Synthesize both outputs — they provide complementary perspectives.

Operational Commands

Health check (imports, config, keys, profile readiness):

bash ~/.claude/skills/gpt-researcher/scripts/health.sh

Enhancement setup (create profiles, install extras):

bash ~/.claude/skills/gpt-researcher/scripts/enhance.sh

Sync upstream (merge upstream changes safely):

bash ~/.claude/skills/gpt-researcher/scripts/sync-upstream.sh

Premium Retrievers

Add keys to ~/cc/.env. Run health.sh to verify.

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

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

Reference Documentation