01
Official implementation guide
docs.example.com/agent-memory
Documentation retrieved from a general web search engine.
AI Search API · Multi-source web retrieval
AI Search API for Agents, RAG, and LLM Apps
Use one AI search API to search Google, Bing, Brave, Yandex, Baidu, DuckDuckGo, X, Reddit, and public Discourse communities—then fetch, deduplicate, rerank, and structure the results.
Test a request in the browser, then use the same model in your code.
api.websearchapi.ai / ai-search
Request
POSTweb search API for AI agents
SourcesWeb + Community
ContentFull page
FusionDedupe + Rerank
OutputStructured
Response preview
Structured JSON02
Production lessons from practitioners
reddit.com/r/LocalLLaMA/...
A public community discussion with practical tradeoffs.
03
Framework support thread
community.example.org/t/...
A public Discourse thread with troubleshooting evidence.
Illustrative response using public WebSearchAPI.ai pages.
AI search explained
What is an AI search API?
Live web discovery and retrieval designed for software that needs current, grounded information.
An AI search API is a developer interface that gives AI systems current, grounded information from the live web. It discovers relevant sources, retrieves useful content, and prepares evidence for AI agents, retrieval-augmented generation (RAG), and LLM apps.
Unlike a traditional web search API that stops at ranked links and snippets, WebSearchAPI.ai searches multiple indexes and public communities. Search Fusion fetches pages, consolidates duplicates, preserves source provenance, and reranks the evidence for your request. Keep the results, extract structured fields, or ask for a grounded response.
Capabilities
AI search API capabilities for grounded answers
Use the complete retrieval pipeline or select only the stages your application needs.
Multi-engine AI search
Run AI search across Google, Bing, Brave, Yandex, Baidu, and DuckDuckGo without maintaining six integrations.
Public community search
Search public X posts, Reddit discussions, and Discourse-powered communities alongside the web.
Full-result fetching
Use Unstoppable Web Scraping to turn useful result pages into clean content for AI.
Cross-source deduplication
Consolidate repeated URLs so more of the context window is available for distinct evidence.
Request-aware reranking
Order the combined evidence for the actual question instead of inheriting one engine’s ranking.
Flexible AI output
Return raw results, extracted context, structured fields, or a response grounded in retrieved sources.
Implementation
How to integrate the AI search API
Configure the retrieval objective, inspect the workflow in the playground, and call the AI search API from your server.
- 01
Create an API key
Sign up, open the developer settings in your dashboard, and create the key your server will use.
- 02
Choose the retrieval objective
Set the query, source mix, result depth, extraction preference, and any market or freshness controls.
- 03
Run Search Fusion
Send the request so WebSearchAPI.ai can retrieve, fetch, deduplicate, and rerank the evidence.
- 04
Use the output you need
Pass normalized results, full context, structured fields, or a grounded response into your application.
Use cases
AI search API use cases
Add current, attributable web evidence to agents, RAG pipelines, research tools, and data workflows.
AI agent search
Give agents current, attributable evidence without trusting one index as the whole web.
RAG search API
Build context from broader discovery, full-page content, and fewer duplicate results.
Research and intelligence
Combine published information, public conversation, and regional sources in one evidence set.
Structured extraction
Turn search-discovered pages into machine-readable fields with source provenance.
FAQ
AI Search API frequently asked questions
Understand source coverage, Search Fusion, and output control before your first request.
What is an AI search API?
An AI search API gives AI systems current information from the live web. It discovers relevant sources, retrieves useful content, and returns evidence that agents, RAG pipelines, and LLM applications can use for grounded outputs.
How is an AI search API different from a traditional web search API?
Traditional search APIs often stop at ranked links and snippets from one index. WebSearchAPI.ai can search multiple web engines and public communities, fetch full-page content, remove duplicate results, rerank evidence, and return structured or grounded AI output.
Which search and community sources are supported?
WebSearchAPI.ai supports Google, Bing, Brave, Yandex, Baidu, and DuckDuckGo, plus public content from X, Reddit, and Discourse-powered community websites. Availability may vary by configuration, plan, or region.
What is Search Fusion?
Search Fusion turns source-specific results into one evidence set by fetching useful pages, normalizing records, consolidating duplicates, preserving provenance, and reranking for the request.
Can I use the AI search API in a RAG pipeline?
Yes. You can return clean page content as model-ready context, keep the underlying sources visible, and pass the retrieved evidence into your own model and generation logic.
Does the AI search API return source URLs?
Yes. Normalized results preserve source provenance so your application can inspect and attribute the evidence used downstream.
Do I have to use an AI-generated answer?
No. AI transformation is optional. You can consume normalized results or extracted content directly, then use only the mapping, structured extraction, or response layer your workflow needs.
Next
Keep exploring
Test the API, read the full reference, or pair search with direct content extraction.
Try the API playground
Run a search and inspect the response in your browser.
Read the Search API reference
Review every request parameter and response field.
Explore the Web Scraping API
Extract content directly when you already know the URL.
Compare plans
Choose the search volume that fits your application.