Open Source · MIT Licensed

The shadcn of
AI components.

Configure a wizard, get a complete RAG pipeline — runnable code, vector DB setup, config, and an eval suite. Yours to own, no black boxes.

6architectures4frameworks5vector DBs100%runnable output
pipeline.pygenerated
1

Generates code for the stack you already use

LangChainLlamaIndexVercel AI SDKQdrantPineconeNeo4jAnthropicOpenAIGooglepgvectorWeaviateChromaLangChainLlamaIndexVercel AI SDKQdrantPineconeNeo4jAnthropicOpenAIGooglepgvectorWeaviateChroma
Why RAGiment

Everything a RAG pipeline needs. Nothing it doesn't.

No lock-in

Generated code is yours — plain, readable, framework-agnostic.

Eval suite included

Every component ships with retrieval precision, faithfulness, and latency benchmarks.

Vector DB steps

Setup guides surface alongside code — no hidden infra abstractions.

Dual codegen modes

Static templates (free) or BYOK LLM codegen tailored to your exact params.

Pipeline anatomy

Watch the data flow.

Every generated pipeline follows a transparent, inspectable architecture. This is Standard RAG — hybrid retrieval, reranking, grounded generation.

Explore all 6 walkthroughs
INGESTION & INDEXINGDUAL RETRIEVAL (PARALLEL)AUGMENTED GENERATIONLoadSplitVectorizeIndex textFusionRetrieveAugmentGenerateAnswer
Component registry

6 production-ready architectures. Pick your stack.

View all
How it works

Wizard in. Pipeline out.

01

Answer 7 questions

Tell us about your documents, queries, and infrastructure. Takes under 2 minutes.

02

Get a recommendation

Our deterministic engine matches your constraints to the right RAG architecture.

03

Download your pipeline

Runnable code, config.yaml, vector DB setup steps, and an eval suite — all parameterized.

Stop scaffolding RAG by hand.

Generate a production-ready pipeline in under two minutes — and own every line of it.