Searlo

Built for RAG Pipelines

The Search API Built for Retrieval Augmented Generation

Ground your LLMs with real-time web data. TOON format reduces token consumption by 60%. Sub-second responses. Works with LangChain, LlamaIndex, CrewAI, and any framework that needs web search.

Get 3,000 Free CreditsRead the Docs
GET/api/v1/search/web?q=postgres+connection+pooling&num=3
200 OK0.17s1 credit85,000 results
{  "searchTime": 0.17,  "totalResults": 85000,  "page": 1,  "organic": [    {      "position": 1,      "title": "31.10. Connection Pools and Data Sources",      "link": "www.postgresql.org/docs/…",      "domain": "postgresql.org",      "snippet": "For an environment without an application se…"    },    { "position": 2, "domain": "medium.com", … },    { "position": 3, "domain": "stackoverflow.blog", … }  ],  "credits": 1}

Why RAG Engineers Choose Searlo

Every feature is optimized for the RAG workflow: search → retrieve → chunk → embed → generate.

Features

  • TOON Format — 60% Fewer Tokens

    Our Token-Optimized Output Notation strips HTML noise and delivers clean, structured text that reduces LLM token consumption by 60% compared to raw HTML.

  • Sub-Second Grounding

    Average response time of ~300ms means your RAG pipeline gets fresh web data without bottlenecking the user experience. Real-time, not cached.

  • Structured JSON Output

    Every response includes title, snippet, URL, and full content in clean JSON. Ready for chunking, embedding, and vector store ingestion.

  • Flexible Credit Validity

    Pay-once model starting at $3.99. No monthly fees eating into your RAG infrastructure budget. Scale at your own pace.

Drop-In Framework Integration

Works with every major AI framework out of the box.

LangChain Drop-in WebSearchTool compatible with LangChain agents and chains from langchain_community.utilities import SearloSearchAPIWrapper search = SearloSearchAPIWrapper(api_key="your_key") results = search.results("latest AI research", num_results=5)

Features

  • LangChain

    Drop-in WebSearchTool compatible with LangChain agents and chains

Drop-In Framework Integration

from langchain_community.utilities import SearloSearchAPIWrapper
search = SearloSearchAPIWrapper(api_key="your_key")
results = search.results("latest AI research", num_results=5)

LlamaIndex Web search data loader for LlamaIndex RAG pipelines from llama_index.tools import SearloSearchTool tool = SearloSearchTool(api_key="your_key") docs = tool.load_data("your search query")

Features

  • LlamaIndex

    Web search data loader for LlamaIndex RAG pipelines

Get started in one request

from llama_index.tools import SearloSearchTool
tool = SearloSearchTool(api_key="your_key")
docs = tool.load_data("your search query")

CrewAI Search tool for CrewAI multi-agent research workflows from crewai_tools import SearloSearchTool search_tool = SearloSearchTool(api_key="your_key") # Add to your CrewAI agent's tool list

Features

  • CrewAI

    Search tool for CrewAI multi-agent research workflows

Get started in one request

from crewai_tools import SearloSearchTool
search_tool = SearloSearchTool(api_key="your_key")
# Add to your CrewAI agent's tool list

How Searlo Fits Your RAG Pipeline

Features

  • User Query

    User asks your AI application a question requiring current information

  • Searlo Web Search

    Your app calls Searlo API — gets relevant web results in ~300ms with TOON format

  • Chunk & Embed

    Clean TOON output is ready for chunking — no HTML parsing needed. 60% fewer tokens.

  • Context Injection

    Retrieved web context is injected into LLM prompt alongside your domain knowledge

  • Generate Answer

    LLM generates a grounded, accurate, up-to-date response with real source citations

RAG-Optimized Pricing

Most RAG applications make 5-20 search calls per user query. At Searlo's pricing, that's $0.001-0.006 per user interaction — 10-50x cheaper than competitors.

Features

  • Tavily

    $5.00/1K

  • SerpAPI

    $5.00/1K

  • Searlo

    $0.30/1K

Related

Ground Your AI with Real-Time Web Data Join AI engineers building production RAG systems with Searlo. 3,000 free credits, no credit card required. Start Building Your RAG Pipeline

Features

Ground Your AI with Real-Time Web Data

Join AI engineers building production RAG systems with Searlo. 3,000 free credits, no credit card required.

FAQ

Frequently Asked Questions

What is the best search API for RAG pipelines?

Searlo is purpose-built for RAG. The TOON output format reduces LLM token consumption by 60% compared to parsing raw HTML. Responses arrive in ~300ms, and structured JSON output is ready for chunking and embedding without extra preprocessing.

How does TOON format help with RAG?

TOON (Token-Optimized Output Notation) strips HTML tags, navigation chrome, ads, and boilerplate from search results. You get clean, structured text that's ready for embedding — saving 60% on LLM tokens and improving retrieval quality by removing noise.

Can I use Searlo with LangChain and LlamaIndex?

Yes. Searlo works as a custom tool in LangChain agents, a FunctionTool in LlamaIndex, and a BaseTool in CrewAI. The REST API is framework-agnostic — any HTTP client works. See our Integrations page for code examples.

How much does a RAG search pipeline cost with Searlo?

Most RAG apps make 5-20 searches per user query. At Searlo's Builder plan ($0.30/1K), that's $0.0015-$0.006 per interaction. The free tier includes 3,000 credits — enough to build and test your pipeline (new users: 90-day validity, legacy users: unlimited).

Does Searlo support real-time search for RAG?

Yes. Every Searlo search returns live Google results, not cached data. This is critical for RAG applications that need current information — news, stock data, recent events, or updated documentation.

From the team behind Searlo

We also build RenderKit — the browser-rendering API for screenshots, PDFs, and LLM-ready markdown. Same billing model, same developer-first API design.

URL to Markdown APIClean markdown plus token-aware chunks for RAG ingestion.MCP web scraping serverGive Claude or Cursor a page-to-markdown tool over MCP.MCP screenshot serverLet an agent see the page it is reasoning about.Token-aware chunkingWhy naive character splits wreck retrieval quality.

Explore the full RenderKit API →

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