The mistake most people make with AI research: they treat it like a smarter Google. One query, one answer, done. That's not research — that's lookup. Real research is iterative. It synthesizes conflicting perspectives, identifies gaps in current sources, and pursues those gaps with follow-up queries. OpenClaw's agentic loop enables exactly that process at speed.
How OpenClaw Deep Research Actually Works
A shallow AI research run looks like this: search query → top 3 results → summarize. That process takes 30 seconds and produces a Wikipedia-level overview. It's the approach most AI tools use by default.
OpenClaw deep research runs a fundamentally different loop. The agent searches, extracts full content from each source, synthesizes across all sources, then explicitly identifies gaps and contradictions in what it found. It generates follow-up queries to address those gaps, searches again, and repeats until it reaches your defined stopping criteria — typically a minimum source count or a defined research depth level.
The difference in output quality is not marginal. A 15-source iterative research run consistently surfaces perspectives, data points, and contradictions that a 3-source single-pass run misses entirely. The missed content is usually the most valuable part — the minority view that challenges consensus, the data point that contradicts the headline finding, the limitation that changes the practical implication.
Configuring Your OpenClaw Research Agent
A research agent configuration has four key components: the research brief, the source requirements, the synthesis instructions, and the output format. Each needs to be precise.
# Research agent configuration
system: |
You are a deep research agent. Your task: {{RESEARCH_TOPIC}}
RESEARCH BRIEF:
Primary questions to answer:
1. {{QUESTION_1}}
2. {{QUESTION_2}}
3. {{QUESTION_3}}
SOURCE REQUIREMENTS:
- Consult minimum 12 sources before writing
- Include at least 3 sources published in the last 6 months
- Include at least 2 sources that present contrary or critical views
- Avoid over-relying on any single source (max 20% of content from one source)
RESEARCH PROCESS:
Round 1: 3 initial search queries → extract full content → synthesize
Round 2: Identify 3 gaps from round 1 → targeted queries → extract → update synthesis
Round 3: Verify contradictions → find resolution or document conflicting views
OUTPUT FORMAT:
- Executive summary (200 words)
- Findings by question (200-400 words per question)
- Contradictions and open questions
- Source list with one-line description per source
skills:
- web_search
- firecrawl
- file_write
model: claude-3-5-sonnet-20241022The source requirement to include contrary views is critical. Without it, the agent satisfies the query with consensus sources only. The most useful research almost always includes at least one source challenging the dominant perspective.
We'll get to the researcher-writer pattern in a moment — but this single-agent configuration already produces research quality that outperforms most manual processes. Start here before adding the second agent.
The Researcher-Writer Multi-Agent Pattern
Single-agent research has a fundamental problem: the same context window that holds all the source material also has to produce polished writing. These are cognitively different tasks, and mixing them in one agent's context produces output that's neither as well-researched as it could be nor as well-written as it could be.
The researcher-writer pattern separates them. The researcher agent collects, extracts, and structures source material into a research brief. The writer agent receives that brief — not the raw sources — and produces the final output. Each agent operates in its strength domain without interference.
# Two-agent research pipeline
system: |
RESEARCHER AGENT instructions:
Research {{TOPIC}} thoroughly using minimum 12 sources.
Produce a research brief in this exact format:
- Key findings (bullet list, one finding per source)
- Contradictions found (list conflicting claims with sources)
- Data points (specific numbers, dates, statistics with attribution)
- Gaps remaining (questions unanswered)
- Source list (URL + one-line summary per source)
Save the research brief to: output/research-brief-{{DATE}}.md
Do NOT write prose or narrative. Data and structure only.
---
# WRITER AGENT (separate run, reads brief output)
system: |
You are a professional writer. Read the research brief at:
output/research-brief-{{DATE}}.md
Write a {{FORMAT}} on {{TOPIC}} using only the information
in the research brief. Do not search for additional sources.
Cite sources by number matching the brief's source list.
Target: {{WORD_COUNT}} words
Audience: {{AUDIENCE}}
Tone: {{TONE}}
skills:
- file_read
- file_writeThe writer agent's instruction to use only the research brief — no additional searching — is the key constraint. It forces the writer to work from curated, verified source material rather than generating content from model knowledge, which reduces hallucination risk significantly.
Quality Control for Research Outputs
Research agent outputs contain hallucinations. This is not a theoretical concern — it's a consistent, documented behavior of large language models operating in agentic loops. The question is not whether your research agent will hallucinate; it's whether you have a system to catch it.
Add a verification pass as the third step in every research pipeline:
# Verification agent — runs after writer completes
system: |
You are a fact-checker. Review the document at:
output/draft-{{DATE}}.md
For each factual claim in the document:
1. Find the supporting source in output/research-brief-{{DATE}}.md
2. Verify the claim matches what the source actually states
3. Flag any claim that: (a) has no source, (b) misrepresents the source,
or (c) adds specificity not in the source
Output: output/verification-report-{{DATE}}.md
Format: table with columns: Claim | Source Found | Accurate | Notes
skills:
- file_read
- file_writeThe verification report gives you a concrete, reviewable record of every claim and its source support. As of early 2025, this approach catches unsupported assertions in roughly 15-20% of research outputs in our testing — a high enough rate to justify the extra pipeline step on any research that matters.
Real Research Output Examples
Concrete examples clarify what "good" looks like for OpenClaw research outputs. Here are three use cases with typical output characteristics.
Competitive landscape analysis. A 12-source research run on a SaaS market segment produces: executive summary, 5-7 competitor profiles with feature/pricing comparison, 3-4 market trends with supporting data, 2-3 gaps in current competitive offerings, and a source list. Total output: 2,500–3,500 words. Time: 25–35 minutes.
Technology evaluation brief. A 10-source research run comparing two technical approaches produces: decision criteria (5-7 dimensions), performance data from benchmarks or case studies, known limitations and failure modes for each approach, community and ecosystem status, and a recommendation with reasoning. Total output: 1,800–2,500 words. Time: 20–28 minutes.
Literature review on a specific question. A 15-source research run across academic and industry sources produces: current consensus view with supporting evidence, minority or dissenting views with their evidence, methodology notes (where source quality affects reliability), and open research questions. Total output: 3,000–4,500 words. Time: 35–45 minutes.
Common Deep Research Configuration Mistakes
- No minimum source count defined. Without a floor, the agent stops at 3-4 sources. Set a minimum of 10-12 for any substantive research task.
- No contrary view requirement. Consensus-only research produces one-sided outputs. Explicitly require sources presenting opposing or critical perspectives.
- Mixing research and writing in one pass. Single-agent research-and-write produces worse output on both dimensions than the two-agent separated approach.
- Skipping the verification pass. Unverified research outputs contain unsupported claims at a rate that makes them unreliable for any decision-making purpose.
- Using research agent outputs without human review. OpenClaw research is a first-draft tool, not a final-product tool. Human review of the verification report before any external use of the research is non-negotiable.
- Running long research tasks synchronously. A 20-source research pipeline blocks for 40+ minutes. Schedule as a cron job and work on something else while it runs.
Frequently Asked Questions
How does OpenClaw deep research work?
OpenClaw deep research runs a multi-step agent loop: search for sources, extract full content from each source, synthesize findings across sources, identify gaps, search for gap-filling sources, and produce a structured output. The agent iterates until it reaches a defined source count or quality threshold — typically 8-15 sources per research task.
How many sources does OpenClaw research typically cover?
A well-configured OpenClaw research pipeline covers 8-20 sources per task depending on your configuration. Setting a minimum source count in your system prompt (e.g., 'consult at least 10 sources before writing') prevents shallow single-source summaries that miss important perspectives and contradictions.
What is the researcher-writer multi-agent pattern?
The researcher-writer pattern separates fact-gathering from writing into two distinct agents. The researcher agent collects and structures source material. The writer agent receives that structured brief and produces the final output. This separation improves quality because each agent operates within its core strength without context pollution from the other task.
How do I ensure research quality with OpenClaw?
Add a quality control step to your pipeline: after the first draft, run a separate verification pass that checks claims against the source material. Instruct the agent to flag any claim that cannot be traced to a specific source. This catches hallucinations and unsupported assertions before they reach your final output.
What types of research tasks work best with OpenClaw?
Information synthesis tasks work best — competitive landscape analysis, technology comparisons, literature reviews, market research, and topic overviews. Tasks requiring primary research, expert interviews, or proprietary data are outside OpenClaw's scope. It excels at aggregating and synthesizing publicly available information at speed.
How long does an OpenClaw deep research run take?
A 10-source research task typically completes in 15-25 minutes. A 20-source task with a researcher-writer pattern takes 35-50 minutes. The time scales with source count and synthesis complexity, not with topic complexity. Schedule long research tasks as overnight cron jobs rather than waiting for them to complete in real time.
You now have the complete deep research system: iterative pipeline configuration, researcher-writer multi-agent pattern, and a verification pass that catches hallucinations before they matter. Configure the single-agent pipeline first and run it on a research topic you know well — that gives you a quality benchmark. Add the writer and verification agents once the research brief quality meets your standard. The full three-agent pipeline is the most reliable research system you can build with OpenClaw today.