The Deep Research Tool List

A curated list of tools and habits for deep research in 2025–2026—when you need to go beyond a quick search and build a real picture.

Search and discovery

• Perplexity, ChatGPT Search, Google (with filters). Use multiple engines; compare answers and sources. For technical topics, add "site:github.com" or "site:docs.xxx.com" to narrow.
• Semantic Scholar, arXiv, PubMed. For academic and scientific depth. Abstracts and citations lead to primary sources.
• Company and project docs. Official docs, GitHub READMEs, and changelogs are often more current than training data. Check "last updated" and version.

Synthesis and memory

• Note-taking (Obsidian, Notion, or plain markdown). Link notes by concept. Summarize in your own words; that forces understanding and gives you a searchable knowledge base.
• Structured outputs. Ask models to produce outlines, tables, or pros/cons so you can compare and extend. "Summarize X and Y and put in a comparison table."

Verification

• Primary sources. When a model or article cites something, open the source. Dates and context matter.
• Multiple models. Run the same question on different models (or same model, different prompts). Overlap is a signal; disagreement is a place to dig.

Ongoing

• RSS or newsletters for domains you care about. Reduces reliance on feeds and keeps you in touch with slow-moving but important trends.
• Devlogs and runbooks. For technical research, keep a short log: what you tried, what worked, what you’d do next. Future you (and agents) can use it.

Research in the AI era is less about "finding the answer" and more about building a map—sources, disagreements, and your own synthesis. This list is a starting point; tailor it to your field and update as tools change.