Research Agent Setups: Perplexity, Elicit, Consensus, And Friends

A tour of the research-agent tool landscape and how to pick the right one per task. The meta-skill: knowing which tool for which question.

10 min · Reviewed 2026

The current landscape (2026)

Tool	Sweet spot	Not great at
Perplexity	Quick current-events research with citations	Deep scientific literature
Elicit	Literature review with structured extraction	Current events, non-English sources
Consensus	Claim-level 'what does research say' queries	Emerging questions with thin literature
Scite	Citation sentiment (supporting vs contradicting)	First-pass discovery
Semantic Scholar	Free API, good metadata, citation graph	UX, integrated summarization
GPT Deep Research	30-minute multi-hop web investigation	Paywalled academic content
Gemini Deep Research	Long-form research with Google's index	Niche academic databases
Claude Projects	Document-grounded research on YOUR corpus	Discovery outside your docs

The decision tree

Do you already have the source documents? → Claude Projects or NotebookLM
Is this academic literature? → Elicit or Consensus, then Semantic Scholar to verify
Is this current events or industry? → Perplexity or GPT Deep Research
Is this a claim-level question (X causes Y?)? → Consensus or Scite
Is this a full systematic review? → ASReview + Rayyan + Elicit as a pipeline

Stacking tools

The best workflows chain tools. Example: Elicit to map the literature → Consensus to validate specific claims → Semantic Scholar API to pull a citation graph → Claude Project to synthesize across everything → Obsidian for atomic note capture.

The big idea: no single research agent is good at everything. The researchers who get the most out of these tools are the ones who know exactly when to switch between them.

End-of-lesson check

12 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-research-agent-setups-creators

What is the main takeaway from "Research Agent Setups: Perplexity, Elicit, Consensus, And Friends — Quick Check"?
1. A tour of the research-agent tool landscape and how to pick the right one per task. The meta-skill: knowing which tool for which question.
2. Add novelty language that wasn't in the source.
3. Replace editorial judgment about field norms
4. Replace the PRISMA-required dual independent screening
Which choice best fits the situation in "Research Agent Setups: Perplexity, Elicit, Consensus, And Friends — Quick Check"?
1. Elicit
2. Perplexity
3. Consensus
4. Semantic Scholar
A learner studying Research Agent Setups: Perplexity, Elicit, Consensus, And Friends would need to understand which concept?
1. Perplexity
2. Consensus
3. Elicit
4. Semantic Scholar
Which of these is directly relevant to Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Perplexity
2. Elicit
3. Semantic Scholar
4. Consensus
Which of the following is a key point about Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Do you already have the source documents? → Claude Projects or NotebookLM
2. Is this academic literature? → Elicit or Consensus, then Semantic Scholar to verify
3. Is this current events or industry? → Perplexity or GPT Deep Research
4. Is this a claim-level question (X causes Y?)? → Consensus or Scite
Which of these does NOT belong in a discussion of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Is this current events or industry? → Perplexity or GPT Deep Research
2. Do you already have the source documents? → Claude Projects or NotebookLM
3. Add novelty language that wasn't in the source.
4. Is this academic literature? → Elicit or Consensus, then Semantic Scholar to verify
What is the key insight about "The meta-skill" in the context of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Add novelty language that wasn't in the source.
2. Replace editorial judgment about field norms
3. The biggest win in research-agent workflows is NOT mastering one tool — it's knowing which tool to reach for.
4. Replace the PRISMA-required dual independent screening
What is the key insight about "Tool budgets add up" in the context of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Add novelty language that wasn't in the source.
2. Replace editorial judgment about field norms
3. Replace the PRISMA-required dual independent screening
4. Power users frequently spend $100-200/month across research tools.
What is the key warning about "Maintain methodological rigour" in the context of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. AI-assisted research requires transparent disclosure of tools used, validation of outputs against primary sources, and p…
2. Add novelty language that wasn't in the source.
3. Replace editorial judgment about field norms
4. Replace the PRISMA-required dual independent screening
Which statement accurately describes an aspect of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Add novelty language that wasn't in the source.
2. The best workflows chain tools. Example: Elicit to map the literature → Consensus to validate specific claims → Semantic Scholar API to pull…
3. Replace editorial judgment about field norms
4. Replace the PRISMA-required dual independent screening
What does working with Research Agent Setups: Perplexity, Elicit, Consensus, And Friends typically involve?
1. Add novelty language that wasn't in the source.
2. Replace editorial judgment about field norms
3. The big idea: no single research agent is good at everything. The researchers who get the most out of these tools are the ones who know exac…
4. Replace the PRISMA-required dual independent screening
In "Research Agent Setups: Perplexity, Elicit, Consensus, And Friends — Quick Check", which idea is most important to apply carefully?
1. Elicit
2. Consensus
3. Perplexity
4. Semantic Scholar

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Tendril · Adults & Professionals · Research & Analysis

Research Agent Setups: Perplexity, Elicit, Consensus, And Friends

A tour of the research-agent tool landscape and how to pick the right one per task. The meta-skill: knowing which tool for which question.

10 min · Reviewed 2026

The current landscape (2026)

Tool	Sweet spot	Not great at
Perplexity	Quick current-events research with citations	Deep scientific literature
Elicit	Literature review with structured extraction	Current events, non-English sources
Consensus	Claim-level 'what does research say' queries	Emerging questions with thin literature
Scite	Citation sentiment (supporting vs contradicting)	First-pass discovery
Semantic Scholar	Free API, good metadata, citation graph	UX, integrated summarization
GPT Deep Research	30-minute multi-hop web investigation	Paywalled academic content
Gemini Deep Research	Long-form research with Google's index	Niche academic databases
Claude Projects	Document-grounded research on YOUR corpus	Discovery outside your docs

The decision tree

Do you already have the source documents? → Claude Projects or NotebookLM
Is this academic literature? → Elicit or Consensus, then Semantic Scholar to verify
Is this current events or industry? → Perplexity or GPT Deep Research
Is this a claim-level question (X causes Y?)? → Consensus or Scite
Is this a full systematic review? → ASReview + Rayyan + Elicit as a pipeline

Stacking tools

The big idea: no single research agent is good at everything. The researchers who get the most out of these tools are the ones who know exactly when to switch between them.

End-of-lesson check

12 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-research-agent-setups-creators

What is the main takeaway from "Research Agent Setups: Perplexity, Elicit, Consensus, And Friends — Quick Check"?
1. A tour of the research-agent tool landscape and how to pick the right one per task. The meta-skill: knowing which tool for which question.
2. Add novelty language that wasn't in the source.
3. Replace editorial judgment about field norms
4. Replace the PRISMA-required dual independent screening
Which choice best fits the situation in "Research Agent Setups: Perplexity, Elicit, Consensus, And Friends — Quick Check"?
1. Elicit
2. Perplexity
3. Consensus
4. Semantic Scholar
A learner studying Research Agent Setups: Perplexity, Elicit, Consensus, And Friends would need to understand which concept?
1. Perplexity
2. Consensus
3. Elicit
4. Semantic Scholar
Which of these is directly relevant to Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Perplexity
2. Elicit
3. Semantic Scholar
4. Consensus
Which of the following is a key point about Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Do you already have the source documents? → Claude Projects or NotebookLM
2. Is this academic literature? → Elicit or Consensus, then Semantic Scholar to verify
3. Is this current events or industry? → Perplexity or GPT Deep Research
4. Is this a claim-level question (X causes Y?)? → Consensus or Scite
Which of these does NOT belong in a discussion of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Is this current events or industry? → Perplexity or GPT Deep Research
2. Do you already have the source documents? → Claude Projects or NotebookLM
3. Add novelty language that wasn't in the source.
4. Is this academic literature? → Elicit or Consensus, then Semantic Scholar to verify
What is the key insight about "The meta-skill" in the context of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Add novelty language that wasn't in the source.
2. Replace editorial judgment about field norms
3. The biggest win in research-agent workflows is NOT mastering one tool — it's knowing which tool to reach for.
4. Replace the PRISMA-required dual independent screening
What is the key insight about "Tool budgets add up" in the context of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Add novelty language that wasn't in the source.
2. Replace editorial judgment about field norms
3. Replace the PRISMA-required dual independent screening
4. Power users frequently spend $100-200/month across research tools.
What is the key warning about "Maintain methodological rigour" in the context of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. AI-assisted research requires transparent disclosure of tools used, validation of outputs against primary sources, and p…
2. Add novelty language that wasn't in the source.
3. Replace editorial judgment about field norms
4. Replace the PRISMA-required dual independent screening
Which statement accurately describes an aspect of Research Agent Setups: Perplexity, Elicit, Consensus, And Friends?
1. Add novelty language that wasn't in the source.
2. The best workflows chain tools. Example: Elicit to map the literature → Consensus to validate specific claims → Semantic Scholar API to pull…
3. Replace editorial judgment about field norms
4. Replace the PRISMA-required dual independent screening
What does working with Research Agent Setups: Perplexity, Elicit, Consensus, And Friends typically involve?
1. Add novelty language that wasn't in the source.
2. Replace editorial judgment about field norms
3. The big idea: no single research agent is good at everything. The researchers who get the most out of these tools are the ones who know exac…
4. Replace the PRISMA-required dual independent screening
In "Research Agent Setups: Perplexity, Elicit, Consensus, And Friends — Quick Check", which idea is most important to apply carefully?
1. Elicit
2. Consensus
3. Perplexity
4. Semantic Scholar

← Back to interactive lesson