Skip to main content
Brime

Real-world data.
At agent speed.

One API for search, extract, and research — built so your agent gets clean, cited, structured data from the open web in seconds.

Get started freeRead the docs

No credit card · 1,000 free credits every month

Lionel Messi Facts

https://www.britannica.com/facts/Lionel-Messi
score: 0.95

Lionel Messi, born in 1987, is an Argentine footballer widely regarded as one of the greatest players of his generation. He spent the majority of his career playing for FC Barcelona, where he won numerous domestic league titles and UEFA Champions League titles.

Works with your stack

Use it from any model.
REST, SDKs, LangChain, MCP.

OpenAI
Claude
Gemini
Meta
LlamaIndex
LangChain
LangGraph
CrewAI
Mastra
Mistral
The toolkit

Three primitives. One platform.

Same key. Same SDK. Same billing — pick the one your agent needs, or compose all three.

transformer architecture
Attention Is All You Need0.95
Introduces the Transformer — a model based solely on attention, no recurrence or convolutions.arxiv.org
Transformer (deep learning)0.87
A deep-learning architecture built on the multi-head self-attention mechanism, proposed in 2017.en.wikipedia.org
Transformers (Hugging Face)0.71
State-of-the-art pretrained models for text, vision, and audio — inference and training in a few lines.huggingface.co
The Annotated Transformer0.64
A line-by-line PyTorch implementation of the paper, annotating each component.nlp.seas.harvard.edu
/search

Search

Real-time access to the open web — ranked, scored, and citation-aware so your agent gets the most relevant source first.

Real-time freshnessRelevance scoringSource attribution
en.wikipedia.org/wiki/Large_language_model
Rawhtml
<div class="mw-body" id="bodyContent"><nav id="p-search" class="vector-menu-content"><aside class="ad-banner sticky sidebar-unit"><ins class="adsbygoogle" data-ad-slot="447"><table class="infobox vcard" cellspacing="3"><p><b>A large language model</b> (LLM) is a type of machine-learning model trained on…<sup class="reference" id="cite_ref-1">[1]</sup><span class="mw-editsection">[edit source]</span><h2 id="Capabilities">Capabilities</h2><p>They generate text, translate languages, and summarize long documents…<!-- sponsored ad unit · doubleclick.net --><footer id="footer" role="contentinfo">…
Extractedmarkdown

# Large language model

A language model with billions of parameters, trained on web-scale text to generate and understand natural language.

## Capabilities

Text generation, translation, summarization, and step-by-step reasoning.

## Architecture

A decoder-only transformer built on multi-head self-attention.

## Training

Pretrained on a large corpus, then aligned with RLHF.

/extract

Extract

Send a URL or document and get clean text, structured markdown, or LLM-ready chunks back — no scraping, no preprocessing.

Multi-format outputPDF & HTMLLayout-aware
Compare RAG vs fine-tuning for agents
Sources
arxiv.organthropic.comopenai.comhuggingface.codeepmind.google+76 more

Comparing RAG and Fine-Tuning

8 sections · 4,200 words · 18 citations
Key findings
RAG leads on freshness and cost-to-update[1]
Fine-tuning wins on latency and format control[4]
Hybrid retrieval + light tuning tops accuracy[9]
RAG is the stronger default for agent workloads[12]
Open report
/research

Research

Plans, reads, and synthesizes across multiple sources — returns a cited, structured report your agent can act on.

Auto-planningCross-source synthesisInline citations
/search

Built for production agents.
Six guarantees, every call.

Drop-in integration.

One SDK call from Python, Node, or curl. Plugs into any agent loop in three lines, no infra to provision.

Live web results.

Every query goes out to live search engines. Results are cached for a few minutes at most, so your agent reads today's web, not a stale snapshot.

Cited by default.

Source URL, title, content, relevance score, and published date (when available) on every result — built for agents that have to show their work.

No training on your data.

Your queries are never used to train models. Results are cached for minutes and request logs are kept for up to 30 days — see the privacy policy.

Built for production.

Retry-safe with an Idempotency-Key header. Every response carries an x-request-id you can log and hand to support.

Any-language queries.

Ask in your own language. Queries go to the search engines as written, and synthesized answers come back in the language you asked in.

Same SDK·same key·shared with /extract and /researchRead the /search docs
Frequently asked

Asked.
Answered.

Tavily, Exa, and Perplexity each cover part of this. Brime puts /search, /extract, and /research behind one SDK, one key, one bill — with document parsing (PDFs and other files, not just HTML) built into /extract. We ship in public; the changelog shows what landed and what didn't.

Still curious?·we read every emailTalk to founders

Build agents
that read.

Drop-in for any agent loop. Three lines of SDK and your agent reads the open web — scored, cited, and ready.

Get started freeRead the docs
No card needed · 1,000 free credits every month