exa-cli

name	exa-cli
description	Provides three-tier web research via the Exa Search API. Triggers for current web information, API docs, vendor comparisons, landscape scans, and structured extraction from search results. Should be preferred over webfetch for multi-result research. For frontier-model agents, prefer `auto` — synthesis tiers mostly produce output you'd reason through anyway.

exa-cli

exa-cli is the default web research gateway for this stack. It is usually the right first step when a task needs current web information, and it now covers two distinct jobs: cheap lookup and fast synthesis.

Start Here

For frontier-model agents (Claude Opus/Sonnet, GPT-5, Gemini 2.5): default to auto. You reason better than what Exa's synthesis tiers produce. The value of Exa is sourcing — 10 ranked raw results with highlights — not summarization. Live comparison on an identical query showed auto returned the same top sources as deep / deep-reasoning at 2.1x less cost and 10-15x less latency, with synthesis you'd mostly ignore anyway. Worse, weak synthesis can actively mislead a downstream reasoning step into formatting the top source instead of doing real analysis.

Use synthesis tiers (deep, deep-reasoning) only when the synthesis itself is the deliverable — a user-facing writeup, a comparison table for a human, or explicit structured extraction via schema. Use a web-search subagent when you need claim-level citations, conflicts analysis, or iterative reasoning.

Tier	Use this	Cost	Latency	Reach for it when	Do not use it when
Quick lookup (default for agents)	exa-cli with auto (or fast / instant)	$0.007	1-2s	You want sources and highlights, and you will reason through them yourself. Frontier-model default.	You need a polished human-facing writeup and the synthesis itself is the output
Synthesized research	exa-cli with deep-reasoning or deep	$0.015 / $0.012	28s / 25s	You want a comparison or landscape scan as the output, or you need schema-driven structured extraction	A frontier model will read and reason over the results anyway — auto gets you there faster
Iterative research	web-search subagent	~$0.08	60-90s	You need claim-level citations, conflicts analysis, or follow-the-thread investigation	You only need a solid synthesis or structured extraction

Quick rule:

• Frontier-model agent doing research: auto.
• Need a human-facing writeup fast and the synthesis is the deliverable: deep-reasoning.
• Need source-by-source conflict analysis: web-search subagent.

Cost And Latency Cheat Sheet

Mode	Cost	Measured latency	What it is good at
auto / fast / instant	$0.007	1-2s	Cheap lookup, docs search, structured extraction on a budget
deep-lite	$0.012	~10s	A light middle option, but weak for synthesis
deep	$0.012	~25s	High-quality synthesis and markdown writeups
deep-reasoning	$0.015	~28s	Best default for synthesis and structured reports
web-search subagent	~$0.08	60-90s	Iterative reasoning, conflicts, uncertainty tracking

Pricing notes:

• First 10 results worth of contents are bundled, so do not cling to 5-result habits.
• Additional results beyond 10 add $0.001 per result.
• summary adds $0.001 per result.
• deep-reasoning is only $0.003 more than deep; prefer it over deep when synthesis is genuinely wanted.
• deep-lite tested poorly for synthesis. Treat it as optional completeness, not a recommended tier.
• For frontier-model agents specifically: auto is a better default than any synthesis tier. You save $0.005-0.008 per call and ~25 seconds of latency, and the frontier model's own reasoning over raw highlights typically beats Exa's synthesis anyway.

Best Defaults

• Prefer highlights unless you truly need long excerpts.
• Prefer maxAgeHours over fresh when you want "recent enough" instead of forced recrawls.
• Use additionalQueries on deep and deep-reasoning when the topic has multiple phrasings.
• Use outputSchema and systemPrompt freely on every search type. They are not deep-only anymore.
• Set synthOnly: true when a synthesis response is the real deliverable. It suppresses the raw result dump.

Useful Knobs

Input	Why it matters
content	Can be a single mode or an array like ["highlights","text"] when you want both snippets and long excerpts
maxAgeHours	Lets you say "use cache if newer than 24h, otherwise crawl"
additionalQueries	Gives Exa 2-3 alternate phrasings for better deep coverage
schema	Requests structured output on any tier
systemPrompt	Shapes tone, sections, and what to prioritize in the answer
synthOnly	CLI-only flag that drops the raw results list when synthesis is present
contentQuery	Tells highlights or summaries what to focus on inside each page

Worked Examples

Tier 1 - Quick lookup

Use this for facts, docs, or cheap structured extraction.

Factual lookup

exa-cli '{"query":"current Node.js LTS version","domains":["nodejs.org"]}'

Structured extraction on a cheap auto call

exa-cli '{"query":"latest Node.js LTS release line and support dates","type":"auto","domains":["nodejs.org"],"schema":{"type":"object","required":["line","codename","support_end"],"properties":{"line":{"type":"string"},"codename":{"type":"string"},"support_end":{"type":"string"}}}}'

Use this pattern when the user wants a clean data object, not a prose report.

Tier 2 - Synthesized research

Use this only when the synthesis itself is the deliverable — a user-facing markdown writeup, a comparison table to hand to a human, or schema-driven structured extraction. A frontier-model agent reasoning over the output will typically do better with auto + its own reasoning than with a synthesis tier.

Recommended synthesis pattern

exa-cli '{
  "query":"latest technical approaches to reducing hallucinations in production LLM applications in 2025-2026, with specific techniques and evidence",
  "type":"deep-reasoning",
  "additionalQueries":[
    "production LLM hallucination mitigation techniques 2026",
    "grounding verification benchmark hallucination reduction 2025 2026"
  ],
  "systemPrompt":"Write for an engineering lead. Focus on named techniques, specific evidence, tradeoffs, and when each approach fits.",
  "schema":{
    "type":"text",
    "description":"Markdown report with sections: intro, techniques, benchmarks, tradeoffs, conclusion."
  },
  "synthOnly":true
}'

Why this works:

• deep-reasoning is the best default middle tier.
• additionalQueries improves coverage for broad research topics.
• systemPrompt shapes the answer.
• outputSchema turns the response into a clean deliverable.
• synthOnly: true avoids dumping 10 raw results after the synthesis.

When to choose deep instead

Use deep when you want a solid synthesis but do not need the extra tightness of deep-reasoning.

exa-cli '{"query":"compare hosted browser automation APIs for AI agents","type":"deep","systemPrompt":"Return a concise markdown comparison with strengths, weaknesses, and best fit.","schema":{"type":"text","description":"Markdown comparison"},"synthOnly":true}'

Do not default to deep-lite for synthesis

deep-lite is valid, but the live spike test showed that it often formats the top source instead of producing a true synthesis. If the user wants a writeup, pick deep or deep-reasoning.

Tier 3 - Iterative research

Use a web-search subagent when the job is not just "summarize this topic" but "show me where the evidence agrees, disagrees, and gets shaky."

Example escalation

User asks: "Which sources disagree about the benchmark gains from speculative decoding, and which claims look weakest?"

Do not use exa-cli alone. Spawn a web-search subagent and ask for:

• primary-source citations for each major claim
• explicit conflicts between sources
• quoted evidence where possible
• uncertainties or weak spots in the public record

That is the line between middle-tier synthesis and full iterative research.

Tool Choice Boundaries

• Prefer exa-cli over webfetch for multi-result web research.
• Use webfetch only when the URL is already known and you need that exact page.
• Use deep-reasoning before spawning web-search when the user wants a good report but not meta-analysis.
• Use local code tools, not exa-cli, for repo inspection.

Gotchas

• outputSchema and systemPrompt work on all search types.
• tweet is not a valid category.
• highlightScores is deprecated and should not appear in output.
• synthOnly is a CLI formatter flag, not an Exa API field.
• fresh: true is just shorthand for maxAgeHours: 0.

Environment

Requires EXA_API_KEY. Get one at https://dashboard.exa.ai/api-keys. If not set, the CLI exits with a clear error pointing to the dashboard.