gpt-researcherlisted

# gpt-researcher Autonomous multi-source research agent with a planner/executor architecture. Gathers information from multiple sources in parallel. **Upstream:** https://github.com/assafelovic/gpt-researcher **MCP Server:** https://github.com/assafelovic/gptr-mcp ## MCP Server Setup Add to your project's `.mcp.json`: ```json { "gpt-researcher": { "type": "stdio", "command": "uvx", "args": ["gptr-mcp"], "env": { "TAVILY_API_KEY": "<your-key>", "ANTHROPIC_API_KEY": "<your-key>", "FAST_LLM": "anthropic:<your-fast-model>", "SMART_LLM": "anthropic:<your-smart-model>" } } } ``` Replace `<your-fast-model>` and `<your-smart-model>` with your preferred Claude model IDs before use. ## MCP Tools | Tool | Purpose | |------|---------| | `deep_research` | Full multi-source research on a topic | | `quick_search` | Fast single-source lookup | | `write_report` | Generate structured report from research | | `get_research_sources` | List sources used in research | | `get_research_context` | Retrieve accumulated research context | ## API Keys Required 1. **TAVILY_API_KEY** — search engine (required) 2. **ANTHROPIC_API_KEY** — LLM provider (required) 3. **Embedding provider key** — for semantic search (required) ## How It Complements Loam - **`/researcher`** — quick single-pass web research - **GPT-Researcher** — autonomous multi-source deep research with structured reports ## Requirements - Python 3.11+ - Three API keys minimum - MCP