# AI search API

An AI search API is a developer tool that returns ranked, structured web results with citations for AI models and agents to reason over.

## What is an AI search API?

It is a programmatic endpoint that lets AI agents and applications search the live web in a single call. It returns results a model can use, including dense excerpts, structured JSON, and citations, instead of the raw links a human search returns.

Language models have a training cutoff and can’t see new events on their own. An AI search API gives them fresh, verifiable web data when they need it.

## Key characteristics

- **Objectives in plain language:** Agents state intent in natural language instead of building keyword queries, the same idea behind [semantic search](https://parallel.ai/articles/what-is-semantic-search).
- **Output built for models:** Results arrive as dense excerpts and structured JSON, so they fit inside the context window without extra HTML cleanup.
- **Source attribution:** Every result carries citations, so an agent’s claims stay verifiable and auditable.
- **Freshness and source control:** You can control freshness with live fetches and filter the domains you include or exclude.

## Example

Picture an AI agent that answers a current research question. The agent sends an objective to the search API, gets back ranked URLs with cited excerpts, and grounds its answer in fresh evidence. This pattern powers retrieval-augmented generation, and this [RAG pipeline](https://parallel.ai/articles/how-to-build-a-rag-pipeline-with-web-search-instead-of-vector-databases) guide walks through the build.

In one documented deployment, a knowledge graph RAG system reached a 28.6% reduction in resolution time at LinkedIn’s customer service team. These [RAG production results](https://arxiv.org/html/2506.00054v1) come from one team, so treat them as scoped.

![Parallel web search API quality benchmarks comparing accuracy and latency across providers](https://cdn.sanity.io/images/5hzduz3y/production/9ad153f1f7dbecfdf970a99a736836a6bf92a6c4-1920x1080.png)

_Public benchmarks measure AI search API accuracy and latency across providers. Source: __[Parallel benchmarks](https://parallel.ai/benchmarks)__._

## Related terms

- [web search API](https://parallel.ai/articles/what-is-a-web-search-api): the broader category that includes AI search.
- [AI agents](https://parallel.ai/articles/what-is-an-ai-agent): the main consumers of these results.
- [deep research](https://parallel.ai/articles/what-is-deep-research): an adjacent capability for multi-step web investigation.

## FAQ

**How is an AI search API different from a traditional search or SERP API?**
A SERP (search engine results page) API returns the same ranked links a human sees and leaves the parsing to you. An AI search API takes an objective in plain language and returns compressed, cited excerpts a model can reason over.

**What do developers use an AI search API for?**
Developers use it to ground large language model (LLM) responses and to give autonomous agents current web data for multi-step research, including retrieval-augmented generation.

**Is ChatGPT an AI search API?**
No, ChatGPT is a conversational model that can optionally include web_search as a parameter, but at up to 10x the cost of model-agnostic alternatives. The [Parallel Search API](https://parallel.ai/products/search) is one example of an AI search API built for agents. It's compatible with any large language model, including OpenAI's GPT models. [Start Building](https://docs.parallel.ai/home) with the docs.