Knowledge retrieval API for AI agents

Give your agents accurate
retrieval in minutes

Connect your unstructured data and give your agents accurate, cited context from your knowledge base via API or MCP.

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200+ leading companies chose Kapa instead of building retrieval themselves

200+ leading companies chose Kapa

instead of building retrieval themselves

Capabilities

Everything you need to give your AI agents context from unstructured knowledge

01

Pre-built connectors

Connect any technical knowledge source in minutes, with every connector's auth, pagination, and rate limits, handled for you.

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02

Continuous sync

Kapa detects and re-processes only what changed in your sources, and updates land in minutes, so agents are never fed stale context.

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03

Managed retrieval pipeline

Chunking, embedding, hybrid search, reranking and evals, tuned continuously for you. All of it behind one API call or MCP server.

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04

Full observability

See what agents are querying, where Kapa couldn't provide a confident answer, and know what documentation to write next.

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Use cases

What teams are building on Kapa retrieval

One knowledge base grounds every agent across your product, support, engineering, marketing and sales stack in your technical documentation.

In-product agent

AI agents embedded directly in your application. Build one on Kapa's Retrieval API, or use the pre-built components in the Kapa Agent SDK.

Learn more
route.tsCopy
1// Your own in-app agent, on Kapa Retrieval
2const RETRIEVAL =
3 "https://api.kapa.ai/query/v1" +
4 "/projects/acme/retrieval/";
5
6export async function POST(req: Request) {
7 const { question, user } = await req.json();
8
9 // Ranked chunks from your knowledge base
10 const res = await fetch(RETRIEVAL, {
11 method: "POST",
12 headers: {
13 "X-API-KEY": process.env.KAPA_API_KEY,
14 "Content-Type": "application/json",
15 },
16 body: JSON.stringify({
17 query: question,
18 integration_id: INTEGRATION_ID,
19 top_k: 5,
20 use_pruning: true,
21 user: { unique_client_id: user.id },
22 }),
23 });
24
25 // [{ source_url, content }], best first
26 const chunks = await res.json();
27 return streamAnswer(question, chunks);
28}
app.acme.com/projects/prod-eu/api-keys
Acme Cloud
API keys
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Acme Product Agent
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Why the best engineering teams chose not to build retrieval themselves

Rated 4.9/5 on G2

Y

Backed by Y Combinator

“Kapa started as an agent on our docs. Now it’s the interface layer between our product and everyone.”

Matan Grady

Product Director, Port

“We use Kapa to power self-serve AI agents on our docs, which cuts time-to-value dramatically.”

Jan Oberhauser

CEO, n8n

“Thousands of complex sources across PDFs, versioned docs, and code. Kapa keeps it all in sync as we ship.”

Deidre Casey

AI Integration Manager, Nordic Semiconductor

“Kapa lets us focus on building agents, not maintaining retrieval infrastructure.”

Sam Bhagwat

CEO, Mastra

“Kapa is super easy to set up. As we expand, we continue to find more integration points to bring the right context to our agents.”

Dana Lawson

CTO, Netlify

“Kapa is key infrastructure for powering context in our user-facing agents.”

Daniel Hai

AI Product Manager, monday.com

Enterprise-grade security and controls

Talk to us

Zero data retention & PII masking

Security controls to limit data retention and PII processing for sensitive use cases.

SOC 2

SOC 2 & GDPR

Independently audited controls for security, availability, and confidentiality.

SSO & RBAC

Single sign-on and role-based access across the platform.

EU/US

Data regions

US and EU hosting available, so your knowledge stays where compliance needs it.

Everything you need to give your agents accurate retrieval, ready in minutes.

Get started for free. Connect your sources and get an API key or hosted MCP server to give your agents the context they need to do reliable work at scale.

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Frequently asked questions

What is Kapa?

−

Kapa is an ingestion and retrieval system for your unstructured knowledge. Ingestion assembles a unified knowledge base from 20+ types of sources - documentation sites, PDFs, tickets, community threads, API specifications — and keeps it up to date as that content changes. Agentic retrieval searches across it to find what the agent needs, the moment it needs it. You hand Kapa to your agent as a search tool via MCP, or call it directly through the API. And for the cases where you do not want to build an agent at all, Kapa offers Prebuilt Agents for common use cases, out of the box.

What can I build with Kapa?

+

Why do agents need retrieval? Can’t I just put my docs in the context window?

+

How is this different from building retrieval in-house?

+

When would I use a web search API like Exa or Tavily instead?

+

Which knowledge sources can Kapa ingest?

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How does Kapa keep the knowledge base up to date?

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Does it work with my agent framework and model?

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I just want a documentation assistant. Do I have to build an agent?

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Is my data secure? Is it used for training?

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How does pricing work?

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Can Kapa run on-prem or in our own VPC?

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Pricing

Documentation

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