@agentskit/rag

@agentskit/rag

Plug-and-play retrieval-augmented generation: chunk documents, embed them, and retrieve the right context at query time.

Tags: ai · agents · llm · agentskit · rag · retrieval · vector-search · embeddings · ai-agents · semantic-search · knowledge-base

How this fits the ecosystem

@agentskit/rag is the retrieval layer: load documents, chunk them, embed them, rerank results, and feed precise context back to agents.

• AgentsKit: compose it with the other packages in this repo to build agents from small, swappable parts.
• Registry: look for ready agents and templates that already use this layer at registry.agentskit.io.
• Playbook: learn the production patterns behind this layer at playbook.agentskit.io.
• AKOS: run the same concepts with enterprise deployment, governance, and observability at akos.agentskit.io.

Docs: package guide · agent handoff

Why rag

• Your data, your agent — no fine-tuning required; ingest plain text and query with natural language
• Composable stack — uses any EmbedFn and any VectorMemory from @agentskit/adapters and @agentskit/memory; swap either layer without touching RAG logic
• Retriever-ready — createRAG() returns a Retriever you pass to @agentskit/runtime or useChat so context is injected automatically
• Tune chunking without a PhD — chunkSize, chunkOverlap, or a custom split function — three knobs that cover 95% of use cases

Install

npm install @agentskit/rag @agentskit/memory @agentskit/adapters

Quick example

import { createRAG } from '@agentskit/rag'
import { openaiEmbedder } from '@agentskit/adapters'
import { fileVectorMemory } from '@agentskit/memory'

const rag = createRAG({
  embed: openaiEmbedder({ apiKey: process.env.OPENAI_API_KEY! }),
  store: fileVectorMemory({ path: './vectors' }),
})

await rag.ingest([
  { id: 'doc-1', content: 'AgentsKit is a JavaScript agent toolkit...' },
])

const docs = await rag.search('How does AgentsKit work?', { topK: 5 })

With runtime (retriever)

Pass the RAG instance as retriever so the runtime injects retrieved context into the task:

import { createRuntime } from '@agentskit/runtime'
import { openai } from '@agentskit/adapters'

const runtime = createRuntime({
  adapter: openai({ apiKey: process.env.OPENAI_API_KEY!, model: 'gpt-4o' }),
  retriever: rag,
})

const result = await runtime.run('Explain the AgentsKit architecture based on ingested docs')
console.log(result.content)

You can also call rag.retrieve({ query, messages }) to satisfy the core Retriever contract (for example from a custom controller).

Features

• createRAG({ embed, store }) — single entry point for ingest + retrieve.
• rag.ingest(docs) — chunk, embed, and store documents.
• rag.search(query, { topK }) — semantic similarity search.
• rag.retrieve({ query, messages }) — Retriever contract v1 for runtime/controller injection.
• Configurable chunking: chunkSize, chunkOverlap, custom split.
• Works with any EmbedFn and any VectorMemory.
• Rerankers: createRerankedRetriever (Cohere Rerank, BGE, BM25 default), createHybridRetriever (vector + BM25 blend), standalone bm25Score. Recipe.
• Document loaders: loadUrl, loadGitHubFile, loadGitHubTree, loadNotionPage, loadConfluencePage, loadGoogleDriveFile, loadPdf (BYO parser). Recipe.

Ecosystem

Package	Role
@agentskit/core	Retriever, VectorMemory, types
@agentskit/memory	Vector backends (fileVectorMemory, etc.)
@agentskit/adapters	openaiEmbedder and other embedders
@agentskit/runtime	retriever integration for agents
@agentskit/react	useChat + chat UI with the same core types

Contributors

License

MIT — see LICENSE.

Docs

Full documentation · GitHub