@agentskit/rag

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

Why

• 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
• Retriever-ready — createRAG() returns a Retriever you pass to @agentskit/runtime or useChat so context is injected automatically

Install

npm install @agentskit/rag @agentskit/memory @agentskit/adapters
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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 })
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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)
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You can also call rag.retrieve({ query, messages }) to satisfy the core Retriever contract (for example from a custom controller).

Next steps

• Tune chunking with chunkSize, chunkOverlap, or a custom split function on createRAG
• Swap fileVectorMemory for redisVectorMemory or a custom VectorMemory for production
• Use geminiEmbedder, ollamaEmbedder, or any (text) => Promise<number[]> as embed

Ecosystem

Docs

Full documentation