How to Build a Personal Research Workflow with OpenClaw

What You'll Build

A comprehensive research workflow that:

1. Searches the web using Tavily for real-time information and articles
2. Finds academic papers using the arXiv Research Assistant for peer-reviewed research
3. Explores documentation using DeepWiki for open-source project docs
4. Synthesizes findings using Summarize to generate concise research briefs

This workflow turns OpenClaw into a research assistant that can gather, filter, and synthesize information from multiple sources in minutes instead of hours.

Why Use AI for Research

Traditional research workflows are full of friction. You open dozens of browser tabs, switch between search engines, copy-paste snippets into scattered notes, and lose track of where you found what. By the time you sit down to write up your findings, half your tabs have gone stale and you can't remember which source supported which conclusion.

Here's what typically goes wrong:

• Tab overload — a single research question spawns 20+ tabs, each demanding attention and memory
• Context switching — jumping between Google, arXiv, GitHub docs, and your notes app fragments your focus
• Manual note-taking — copying quotes and URLs by hand is slow and error-prone
• Synthesis difficulty — pulling together insights from web articles, academic papers, and project documentation into a coherent picture is mentally exhausting
• Information decay — bookmarks go stale, articles get taken down, and your notes lose context over time

An AI-powered workflow solves these problems by centralizing the search, extraction, and synthesis steps into a single conversation. OpenClaw queries multiple sources in parallel, retains the full context of what it found, and can produce structured summaries on demand. You stay focused on the questions that matter instead of managing the mechanics of search.

Prerequisites

• OpenClaw installed and configured
• Tavily API key (free tier available at tavily.com)
• Node.js 18+

Step 1: Install the Required Skills

# 1. Web search
npx clawhub@latest install tavily

# 2. Academic paper search
npx clawhub@latest install arxiv

# 3. Open-source documentation search
npx clawhub@latest install deepwiki

# 4. AI summarization
npx clawhub@latest install summarize

Step 2: Configure API Keys

Tavily Web Search

1. Sign up at tavily.com — free tier includes 1,000 API credits/month (1 credit per basic search)
2. Copy your API key from the dashboard
3. Configure in OpenClaw:

clawhub inspect tavily

arXiv Research Assistant and DeepWiki

These skills work out of the box — no API keys needed. The arXiv skill queries the arXiv API directly, and DeepWiki uses public documentation sources.

Step 3: Research Workflow in Action

Example 1: Researching "RAG (Retrieval-Augmented Generation) Best Practices"

Phase 1: Web Search for Current State

Start with a broad web search to understand the current landscape:

Search for "RAG best practices 2026" and summarize the top results

Tavily returns recent articles, blog posts, and tutorials. The Summarize skill distills them into key takeaways.

Phase 2: Academic Papers

Dive deeper with peer-reviewed research:

Find recent arXiv papers on retrieval-augmented generation improvements

The arXiv skill returns relevant papers with titles, abstracts, authors, and links. You can then ask for summaries of specific papers:

Summarize the methodology section of paper [arXiv:2406.xxxxx]

Phase 3: Implementation Documentation

Check how popular frameworks implement RAG:

Search DeepWiki for LangChain RAG implementation guide
Search DeepWiki for LlamaIndex retrieval pipeline documentation

DeepWiki returns relevant documentation sections from open-source projects.

Phase 4: Synthesis

Combine everything into a research brief:

Based on all the research we've done, create a structured brief on RAG best practices covering:
1. Current state of the art
2. Key techniques (chunking, embedding, retrieval)
3. Common pitfalls and how to avoid them
4. Recommended frameworks and tools
5. Open research questions

Example 2: Evaluating a New Database for Your Project

Suppose you need to decide whether to adopt ScyllaDB for a high-throughput event pipeline. Here's how the four-phase workflow applies to a completely different domain.

Phase 1: Web Search for Real-World Adoption

Search for "ScyllaDB production experience 2025 2026" and summarize key findings

Tavily surfaces blog posts from engineering teams who have migrated, benchmark results, and community discussions about operational trade-offs. You get a picture of who is using it and what problems they ran into.

Phase 2: Academic and Technical Papers

Find arXiv papers on LSM-tree database performance and shard-per-core architecture

The arXiv skill returns papers on the underlying storage engine design, comparisons with Cassandra internals, and latency modeling under different workloads. Ask OpenClaw to summarize the key performance claims and whether they match real-world reports from Phase 1.

Phase 3: Documentation Deep Dive

Search DeepWiki for ScyllaDB data modeling best practices
Search DeepWiki for ScyllaDB driver compatibility and connection pooling

DeepWiki pulls up the official documentation on schema design, compaction strategies, and driver configuration. This is where you learn the practical constraints that blog posts gloss over — partition size limits, tombstone handling, and consistency level trade-offs.

Phase 4: Decision Synthesis

Based on all findings, create a decision brief for ScyllaDB adoption covering:
1. Performance characteristics vs. our current PostgreSQL setup
2. Operational complexity (deployment, monitoring, backups)
3. Data modeling constraints and migration effort
4. Community health and long-term viability
5. Recommendation: adopt, evaluate further, or pass

The result is a structured document you can share with your team or drop into a design review — built from real sources in a single research session.

Organizing Research Output

Raw research is only useful if you can find it later. Here are practical ways to structure and export your findings.

Structured Markdown Reports

Ask OpenClaw to format results into a consistent template:

Save the research brief as a Markdown file with sections for Summary, Key Findings, Sources, and Open Questions

This gives you a portable document that works in any editor, renders on GitHub, and can be version-controlled alongside your code.

Comparison Tables

For technology evaluations, ask for structured comparisons:

Create a Markdown comparison table of ScyllaDB vs. Cassandra vs. DynamoDB covering: latency, throughput, operational complexity, cost, and ecosystem maturity

Tables are easier to scan than prose and make it straightforward to present trade-offs to stakeholders.

Exporting to Notion or Obsidian

Since OpenClaw outputs Markdown natively, integration with note-taking tools is straightforward. Copy the Markdown output directly into a Notion page (paste as text, then convert to blocks) or save .md files into your Obsidian vault folder. For Obsidian users, you can ask OpenClaw to include [[wikilinks]] and YAML frontmatter to match your vault conventions. Over time, this builds a searchable personal knowledge base organized by topic and date.

Building a Personal Knowledge Base

Keep a consistent folder structure for research sessions:

research/
  2026-03-rag-best-practices/
    brief.md
    sources.md
    comparison-table.md
  2026-03-scylladb-evaluation/
    decision-brief.md
    benchmark-notes.md

Each session produces a self-contained folder. Link them together using cross-references, and you have a knowledge base that grows with every research task.

Advanced: Multi-Session Research

Not every research question can be answered in a single sitting. Complex topics benefit from multiple sessions spread across days or weeks, where each session builds on the previous one.

Continuing Research Across Sessions

OpenClaw does not automatically remember previous conversations, so you need to bring context forward. The simplest approach is to save a research state file at the end of each session:

Summarize our research progress so far into a file called research-state.md, including: questions answered, questions still open, key sources found, and next steps

At the start of your next session, provide this file as context and pick up where you left off. This is more reliable than trying to remember what you covered last time.

Tracking Research Threads

For broad topics, maintain a running list of sub-questions and their status. Ask OpenClaw to update it as you work:

Update the research tracker: mark "ScyllaDB compaction strategies" as done, add "test ScyllaDB with our schema" as next step

This turns your research from an ad-hoc activity into a structured process with clear progress markers.

Maintaining Context Between Conversations

If your research spans multiple tools and sessions, keep a sources.md file that logs every source you've consulted — URL, date accessed, and a one-line summary. When you start a new session, feed this file to OpenClaw so it knows what ground you've already covered and can focus on gaps instead of repeating searches.

Research Workflow Patterns

Pattern 1: Technology Evaluation

Evaluating a new tool or framework for your project:

1. Tavily — search for reviews, comparisons, and real-world usage reports
2. DeepWiki — read the official documentation and architecture overview
3. arXiv skill — find the underlying research papers (if applicable)
4. Summarize — generate a "buy vs. build" recommendation brief

Pattern 2: Competitive Analysis

Understanding how competitors solve a problem:

1. Tavily — search for competitor product announcements, blog posts, and changelog entries
2. DeepWiki — check their open-source repos (if any) for implementation details
3. Summarize — create a competitive landscape summary

Pattern 3: Learning a New Domain

Getting up to speed on an unfamiliar topic:

1. Tavily — search for "introduction to X" and "X explained simply"
2. arXiv skill — find survey papers that cover the field comprehensively
3. DeepWiki — find tutorial repos and documentation
4. Summarize — generate a "learning roadmap" with recommended reading order

Pattern 4: Bug Investigation

Researching a tricky technical issue:

1. Tavily — search for the error message or symptom
2. DeepWiki — check the library's documentation for known issues
3. Summarize — compile all findings into potential causes and solutions

Tips for Effective Research

1. Start broad, then narrow — use web search first, then drill into academic papers and docs
2. Use multiple search queries — rephrase your question 2-3 ways for better coverage
3. Verify across sources — cross-reference web articles with academic papers
4. Time-bound your searches — specify "2025-2026" for current information
5. Save as you go — ask OpenClaw to save key findings to a file for later reference

Troubleshooting

Tavily returns irrelevant results

• Refine your search query with more specific terms
• Use quotes for exact phrases
• Add site filters if you want results from specific domains

arXiv search finds no papers

• Try broader search terms — arXiv titles can be very specific
• Search by author name if you know who's working in the area
• Check if the topic is covered under a different name in academia

DeepWiki can't find documentation

• Verify the project name matches exactly
• Try the GitHub organization/repo format
• Some projects may not be indexed yet