Open source · MIT
The meta harness for research queries.
Ask once. Librarium fans your query out to search engines, AI-grounded answers, and deep-research APIs in parallel, then merges everything into one structured output.
$ npm i -g librarium
Also available via , , , or .
zsh — librarium
$ librarium answer "how to get cited in ai answers"
fanning out to 5 providers
✓ gemini-grounded ai-grounded 1.1s 9 sources
✓ openrouter-online ai-grounded 1.9s 7 sources
✓ brave-answers ai-grounded 0.6s 20 sources
✓ exa ai-grounded 1.4s 10 sources
✓ kagi-fastgpt ai-grounded 4.7s 3 sources
AI engines cite pages they can parse and trust:
structured headings, direct answers high on the
page, and schema markup all raise citation rates
[1] [2]. Mentions on Reddit, reviews, and listicles
matter more than backlinks for LLM visibility [3].
Sources
[1] How AI search engines pick citations
[2] Schema markup and LLM visibility
[3] Reddit's outsized role in AI answers
5 succeeded, 0 failed in 4.7s
▸ 31 unique sources after dedupe (49 total citations)
▸ ./agents/librarium/1781136000-how-to-get-cited-in-ai/Features
Built for serious research workflows.
- Live fan-out table
- Every provider resolves in place with timing, source counts, and reported cost. Slow ones get highlighted; failures fall back to a configured backup.
- Grounded answers
- librarium answer fans out, then synthesizes one cited answer from what actually came back. Every claim maps to a real source.
- Reports for humans and machines
- A tabbed HTML report for reading, results.jsonl with full content for pipelines, and a browsable run directory for everything else.
- Tier-tuned queries
- --refine rewrites your query three ways with one LLM call: a brief for deep research, a question for AI answers, keywords for raw search.
- Async deep research
- Submit long-running jobs and walk away. status --wait --retrieve collects the reports when they land.
- Built for agents
- An agent skill, an MCP server, and an embeddable edge-safe core. Your agents fan out, browse, and cite without screen-scraping a terminal.
Plus provider groups, automatic fallbacks, and custom providers from npm or local scripts. Full command reference
librarium/core
A CLI for you. A library for your agents.
The same adapters and dispatcher behind the CLI ship as an embeddable core that returns structured results in memory. No filesystem, no Node-only dependencies; it runs in Cloudflare Workers and other edge runtimes. Wire it into agents, pipelines, or apps.
Read the library docsresearch.ts
import { dispatch } from 'librarium/core'
const run = await dispatch({
query: 'HTTP/3 adoption in 2026',
providers: ['perplexity-sonar-pro', 'exa'],
})
for (const result of run.results) {
console.log(result.provider, result.sources)
}Provider tiers
Depth when you need it, speed when you don't.
deep-research
minutes
Async jobs that run for minutes and come back with comprehensive multi-source reports. Submit, keep working, retrieve later.
- perplexity-sonar-deep
- openai-deep
- openai-deep-o3
- gemini-deep
- +2
ai-grounded
seconds
AI answers with inline citations. The middle ground between speed and depth.
- perplexity-sonar-pro
- gemini-grounded
- exa
- kagi-fastgpt
- +3
raw-search
sub-second
Classic engine results. Broad link discovery and fact checks, in under a second.
- brave-search
- tavily
- serpapi
- firecrawl-search
- +3
Twenty-plus built-in providers. See the full catalog
Stop searching one engine at a time.
Install librarium and fire off your first deep research in under a minute.
Paste this into your agent
Install librarium, the research fan-out CLI (npm i -g librarium), set it up with librarium init --auto and librarium install-skill, then use it to kick off a deep research on [a topic of my choosing].