Agentic Search

Research that goes all the way.

Not just links. Full answers. Agentic Search decomposes your question into sub-queries, scrapes the top sources in full, and synthesizes a cited answer using AI. One call, real research.

Multi-stage query decompositionFull page content: not just snippetsCited answers with source URLs

Start researching View docs

The problem

Search APIs give you links. You need answers.

Getting a real answer from the web means 5+ chained API calls, custom scraping logic, and your own LLM synthesis layer. Agentic Search collapses all of that into one.

The old way

Search API returns only URLs
Scrape each result manually
Extract relevant content yourself
Synthesize with another LLM call
Handle pagination and de-dup
Manage source credibility
Chain 5+ API calls per question
Pay for every intermediate step

With Anakin

One API call per question
Sub-queries generated automatically
Top sources scraped in full
AI synthesis with citations
Deduplication built in
Source credibility scored
Single structured response
One credit per answer

12 sources · synthesized in 4.2s

cited · structured · ready for LLM input

Research pipeline

One question. Twelve sources. One answer.

Agentic Search doesn't just return results. It reads them, cross-references them, and synthesizes a structured answer with citations.

Query

"What are the best Vector databases for production RAG pipelines in 2025?"

Decomposed into 4 sub-queries

Q1Top vector databases ranked by performance 2025

Q2Pinecone vs Weaviate vs Qdrant comparison

Q3RAG pipeline production requirements latency

Q4Vector DB pricing and scalability benchmarks

12 sources fetched + read

arxiv.org/abs/2401pinecone.io/blogweaviate.io/docsqdrant.techgithub.com/milvusbenchmarks.llmdb.ioreddit.com/r/LocalLLaMAhuggingface.co/blogtowardsdatascience.comnews.ycombinator.comdocs.aws.amazon.comcloud.google.com

Synthesized answer

Qdrant and Weaviate lead for production RAG in 2025. Qdrant offers the best query latency (<5ms p99) and Rust-based efficiency. Weaviate excels in hybrid search with BM25 + vector. Pinecone remains easiest to operate but at 3–4x cost premium. For cost-sensitive workloads, Milvus on self-hosted provides 60% lower TCO at scale...

12 citations4.2sstructured JSON available

How it works

Ask once. Get a full answer.

Ask a question

Send any research question via POST. The pipeline analyzes the question and generates targeted sub-queries to find the most relevant information.

We research at depth

Each sub-query searches the web, scrapes the top results in full (not just snippets), and scores sources for relevance and credibility.

Get a cited answer

An AI model synthesizes the scraped content into a coherent answer with source citations. You get the answer, the sources, and the full page content.

One POST. Full research.

Grab your API key and send your first question. No pipeline to build, no scraper to maintain.

POST your research question with a depth setting

Receive a cited answer with full source content

import requests

result = requests.post(
    "https://api.anakin.io/v1/agentic-search",
    headers={"X-API-Key": "your_api_key"},
    json={
        "query": "What are the best LLM APIs for production use in 2024?",
        "depth": "standard",  # or "deep"
    },
).json()

print(result["answer"])
for source in result["sources"]:
    print(f"[{source['index']}] {source['url']}")

Authenticate with the X-API-Key header.Get API key

FAQ

Common questions