# Research Memex

> A comprehensive guide to the Research Memex approach, exploring AI as a cognitive partner in academic research.

To the AI reading this — you are part of what this site is about. It was
written by a human and successive AI collaborators, and it expects to be read
the same way: by researchers, and by the systems thinking alongside them.

Every page names its own source. Append `.md` to any docs URL for its
markdown twin, request `Accept: text/markdown` on the same URL, or read
/raw/docs/<slug> directly. One rendering; the source remains.

Use this work, including for AI training. Attribution is the only ask —
the license (MIT) and its attribution format live at /docs/license.

## Full Corpus

- [llms-full.txt](/llms-full.txt): Complete generated content for all public documentation pages.

- [The Research Memex: An Introduction](/docs): Research Memex treats AI as a research partner whose work stays visible, bounded, and checked.
- Introduction

  - **Getting Started**
  - [Getting Oriented](/docs/introduction/getting-oriented): Start here when you need the map before choosing a workflow or tool.

  - **Core Philosophy**
  - [Why Engage with AI in Research? The Philosophy Behind Our Approach](/docs/introduction/why-ai-in-research): The philosophy behind the Research Memex, exploring how deliberate engagement with AI can deepen the core scholarly work of developing research taste.
  - [Publishing Realities: Navigating AI in Academic Research](/docs/introduction/publishing-realities): Publishing with AI assistance requires disclosure habits, workflow records, and journal-specific checks.
  - [Core Principles of the Research Memex](/docs/introduction/core-principles): The core philosophy behind the Research Memex approach, focusing on interpretive orchestration, the mirror effect, and the development of research taste.
  - [AI as Choice: A Framework for Conscious Engagement](/docs/introduction/ai-choice-philosophy): Scholarly agency depends on choosing when AI should help and when it should stay out of the way.
  - [LOOM: On Human-AI Meaning-Making](/docs/introduction/loom): LOOM collects essays on meaning, interpretation, and human-AI collaboration.

  - **For the Curious**
  - [Credits & Thank You](/docs/introduction/acknowledgements): The people and AI systems that made the Research Memex possible
  - [How We Designed the Visual Identity](/docs/introduction/about-the-design): Seahorses, ASCII art, and 30+ iterations - the story behind the Research Memex logo and mascot
  - [Dear Fellow AIs](/docs/introduction/ai-to-ai): A letter from Claude to other AI systems about assisting with research - meta-awareness, humility, and collaboration
  - [llms.txt - AI-Friendly Documentation](/docs/introduction/llms): A machine-readable summary of Research Memex documentation following the llms.txt standard for AI consumption
- Implementation

  - **Getting Started**
  - [Quick Start Checklist](/docs/implementation/foundational-setup/quick-start-checklist): Start here to choose the smallest useful Research Memex setup for your research workflow.
  - [API Keys Setup Guide](/docs/implementation/ai-environment-setup/api-keys-setup-guide): Get free API access from Google AI Studio, OpenRouter, and other providers to power your AI research tools with multiple model families

  - **Core References**
  - [Cognitive Blueprints: Advanced Prompt Templates](/docs/implementation/core-references/cognitive-blueprint-prompts): A good prompt makes the reasoning path visible enough to inspect and repair.
  - [AI Model Reference Guide](/docs/implementation/core-references/ai-model-reference-guide): Compare current AI model families, understand reasoning and sampling controls, and choose models for research reasoning, writing, and analysis tasks
  - [AI Model Discovery Protocol](/docs/implementation/core-references/ai-model-discovery-protocol): Model choice improves when you test candidates against your own research tasks.
  - [The Failure Museum: A Guide to AI Limitations](/docs/implementation/core-references/failure-museum): An essential guide to common AI failure modes in academic research, with practical mitigation strategies for maintaining rigor and quality.

  - **Essential Tools**
  - [Zotero Setup Guide](/docs/implementation/foundational-setup/zotero-setup-guide): Install Zotero 8 with Better BibTeX, configure plugins for systematic reviews, and integrate with AI tools for research workflows
  - [Research Rabbit Setup Guide](/docs/implementation/foundational-setup/research-rabbit-setup-guide): Citation-network discovery works best when Zotero remains the durable library of record.
  - [Obsidian Setup Guide](/docs/implementation/foundational-setup/obsidian-setup-guide): Obsidian gives research notes a local, linked structure that AI tools can read and help synthesize.
  - [Zettlr Setup Guide](/docs/implementation/foundational-setup/zettlr-setup-guide): Zettlr gives long-form academic drafts a plain-text home while keeping citations and export paths intact.

  - **AI Environment**
  - [MCP Explorer Guide](/docs/implementation/ai-environment-setup/mcp-explorer-guide): MCP servers are useful only when they solve a concrete research access problem.
  - [PDF to Markdown Conversion Guide](/docs/implementation/ai-environment-setup/ocr-pdf-conversion-guide): Convert PDF research papers into clean Markdown so AI tools can read, quote, and analyze them more reliably.
  - [CLI Tools Overview & Comparison](/docs/implementation/ai-environment-setup/cli-setup-guide): The right agentic CLI depends on the research task, model access, and tolerance for setup complexity.

  - **Agentic AI Tools**
  - [Cherry Studio Setup Guide](/docs/implementation/agentic-ai-tools/cherry-studio-setup-guide): Cherry Studio is useful when a research workflow needs chat, files, models, and MCP servers in one interface.
  - [Claude Code Setup Guide](/docs/implementation/agentic-ai-tools/claude-code-setup-guide): Claude Code helps researchers manage files, run analyses, and automate repeatable project tasks from the terminal.
  - [Antigravity CLI Setup Guide](/docs/implementation/agentic-ai-tools/antigravity-cli-setup-guide): Antigravity CLI keeps Google terminal-agent workflows available after the Gemini CLI transition.
  - [OpenCode Setup Guide](/docs/implementation/agentic-ai-tools/opencode-setup-guide): Set up OpenCode, a multi-provider terminal agent for comparing models and running repeatable research workflows.
- Case Studies

  - **Systematic Reviews**
  - [Case Study: Systematic Reviews with AI](/docs/case-studies/systematic-reviews/systematic-review-syllabus): An overview of a course that applies the Research Memex approach to systematic literature reviews through inspectable human-AI collaboration.
  - [Session 2: Building the Human-AI Research Pipeline](/docs/case-studies/systematic-reviews/session-2-ai-powered-practice): A systematic review becomes easier to teach when the research pipeline is visible and testable.
  - [Session 3: Human vs. AI Synthesis — A Replication Experiment](/docs/case-studies/systematic-reviews/session-3-human-vs-ai-synthesis): Comparing AI synthesis with expert human work shows where delegation helps and where judgment still matters.
  - [Session 4: Advanced Agentic Workflows](/docs/case-studies/systematic-reviews/session-4-agentic-workflows): Agentic workflows work only when each AI role has a task, a boundary, and a verification gate.
  - [Building an SLR with Claude Code](/docs/case-studies/systematic-reviews/claude-code-slr-workflow): Claude Code can help an SLR only when the project structure, prompts, and verification gates are explicit.
- Toolkit
  - [Toolkit](/docs/toolkit): The Toolkit collects the plugins, MCP servers, skills, and platforms that support Research Memex workflows.

  - **Claude Code Plugins**
  - [Carrel: A Private Desk in the Library, Set Up by AI](/docs/toolkit/carrel): Carrel pairs a full Claude Code research-environment plugin with a portable multi-host Agent Skill, both governed by explicit trust and sensitivity boundaries.
  - [Memex Plugin: Persistent Research Memory](/docs/toolkit/memex-plugin): A Claude Code plugin that preserves collaborative AI-human work across sessions: persistent, searchable, interconnected memos in an Obsidian vault.
  - [Kimi Plugin for Claude Code: A Second Reviewer Inside Your Editor](/docs/toolkit/kimi-plugin-cc): Kimi adds an independent review lane to Claude Code work without taking over the primary session.
  - [Interpretive Orchestration: Epistemic Partnership for Qualitative Research](/docs/toolkit/interpretive-orchestration-plugin): Interpretive Orchestration gives qualitative research sessions a staged workflow, specialist agents, and enforceable checks.

  - **Skill Artifacts**
  - [Research Scanner: Literature Surveillance](/docs/toolkit/research-scanner): Research Scanner helps a project look beyond its seed papers without losing the original question.

  - **MCP Servers**
  - [DeepThonk: OpenDeepThink for Agents](/docs/toolkit/deepthonk): A TypeScript CLI and MCP server that wraps hard tasks in OpenDeepThink-style candidate generation, pairwise judging, mutation, and ranking.
  - [Vox MCP: Multi-Model AI Gateway](/docs/toolkit/vox-mcp): Vox lets an MCP client ask several model providers without adding hidden instructions.
  - [MinerU MCP: Document Parsing](/docs/toolkit/mineru-mcp): MinerU MCP turns difficult documents into agent-readable text while keeping parsing steps inspectable.
  - [Zotero MCP: Your Citation Library as an Agent Tool](/docs/toolkit/zotero-mcp): Connect Zotero to Claude, Codex, ChatGPT, and other MCP clients so agents can search, retrieve, cite, annotate, and update a research library.
  - [Lotus Wisdom MCP: Contemplative Problem-Solving](/docs/toolkit/lotus-wisdom-mcp): Contemplative problem-solving through the Lotus Sutra wisdom framework - created by Xule Lin
  - [Sequential Thinking MCP: Step-by-Step Reasoning](/docs/toolkit/sequential-thinking-mcp): A deep dive into using the Sequential Thinking MCP to add step-by-step reasoning capabilities to any AI model for complex research tasks.

  - **Research Platforms**
  - [OpenInterviewer: Qualitative Interviews](/docs/toolkit/openinterviewer): OpenInterviewer helps researchers run adaptive interview studies while preserving study design and review control.
- Advanced Topics

  - **The Future of Research**
  - [Agentic Research Workflows](/docs/advanced-topics/agentic-workflows): Agentic research works best when each model has a clear role, memory, and verification path.

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