GPT Researcher vs modular-context-obsidian-plugin
GPT Researcher: GPT Researcher is an open-source LLM agent designed for deep, structured research on any topic using web and local sources. It produces detailed reports with citations by running parallel research agents, scraping relevant sources, filtering content, and synthesizing findings. Supports local document research, customizable report formats, and multiple LLM backends including OpenAI, Anthropic, and Ollama.; modular-context-obsidian-plugin: Modular Context is an Obsidian plugin that provides an LLM-native knowledge base structured as Sources, Wiki, and Schema, inspired by Karpathy's framing. It also includes a G-Suite MCP server that exposes multi-account Gmail and Calendar as 10 tools for Claude Code. The plugin is local-first, encrypted, and has no telemetry.
TL;DR
- You want an Apache-2.0 agent that plans sub-questions, searches the web and local documents in parallel, and writes 2,000+ word reports with citations.
- You want to use it as a Python library, standalone web app, or MCP-connected research skill with PDF/Word export.
- You want a general-purpose super-agent harness with a bash sandbox, persistent memory, and Feishu/WeCom/Slack bots; DeerFlow is built for that.
- You do not want per-report search API (e.g. Tavily) and multi-call LLM token costs.
Synthesise raw files (transcripts, notes, backlog) into vault modules
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