LlamaIndex vs Haystack: which RAG framework for your LLM app?
Use LlamaIndex if you want a batteries-included framework with automatic data connectors, auto-indexing, and minimal boilerplate. Use Haystack if you need fine-grained control over pipeline architecture and prefer explicit component composition.
VERDICT
Side-by-side comparison
| Feature | LlamaIndex | Haystack | Winner |
|---|---|---|---|
| Learning curve | Steep (many APIs) | Moderate (pipeline-centric) | Haystack |
| Data connectors | 100+ built-in loaders | 50+ via integrations | LlamaIndex |
| Index management | Automatic + configurable | Manual via components | LlamaIndex |
| Pipeline abstraction | High-level (QueryEngine) | Low-level (DAG) | Haystack |
| Model flexibility | LLM + embedding abstractions | Direct Hugging Face integration | Tie |
| Production readiness | Proven at scale (Y Combinator apps) | Production-ready, less adoption | LlamaIndex |
| Installation size | ~150MB (heavy deps) | ~80MB (lighter) | Haystack |
| Streaming support | Yes (async first) | Yes (callback-based) | Tie |
Performance benchmarks
Setup time (5-doc RAG endpoint)
LlamaIndex SimpleDirectoryReader + QueryEngine vs Haystack manual Document + Pipeline construction with custom retrievers
Time to add a new data connector
LlamaIndex has connectors for Notion, Google Drive, Slack; Haystack requires custom Document fetcher
Dependency footprint
LlamaIndex includes many optional integrations; Haystack is more modular
Model support (OSS LLMs)
Both support local models; Haystack's integration is more direct
When to use each
- ✓ Building a RAG chatbot in days: LlamaIndex's SimpleDirectoryReader and auto-indexing handle document ingestion without boilerplate
- ✓ Integrating data from SaaS platforms (Notion, Slack, Google Drive, Airtable): 100+ pre-built connectors save weeks of integration work
- ✓ You have unstructured documents (PDFs, web pages) and need smart chunking + metadata extraction: LlamaIndex's document processing is sophisticated
- ✓ Production RAG at scale: LlamaIndex powers thousands of deployed apps; battle-tested error handling and caching
- ✓ You want a single query interface: QueryEngine abstracts complexity; one call to answer questions across indexed data
- ✓ Building a custom research/experimental RAG pipeline: Haystack's explicit component DAG lets you prototype novel retrieval strategies
- ✓ You need minimal dependencies and a lightweight framework: Haystack is ~50% smaller install; good for edge or constrained environments
- ✓ Working directly with Hugging Face models for embedding/generation: Haystack's tight Transformers integration avoids extra layers
- ✓ You want complete visibility into what's happening: Haystack's pipeline as code approach reveals every step; great for debugging
- ✓ Building retrieval evaluation systems: Haystack's Eval components make it easier to test and iterate on retrieval quality
Common misconceptions
LlamaIndex
LlamaIndex is just a thin wrapper around LLMs
LlamaIndex is a full data orchestration framework: it handles indexing, caching, multi-document summarization, and query optimization. Many bugs come from not understanding its opinionated defaults (e.g., chunk size, overlap, similarity threshold).
LlamaIndex connectors just download data: they're production-ready
Connectors vary in maturity. Some are thin wrappers around APIs. You often need to add retry logic, rate limiting, and pagination yourself. Check the source code before deploying to production.
LlamaIndex's auto-indexing is always better than hand-tuned retrieval
Auto-indexing is a good starting point, but for high-precision RAG (legal, medical, financial), you'll spend 60% of time tuning chunk size, overlap, and similarity thresholds. It's not a 'set and forget' feature.
Haystack
Haystack's component model is more flexible: it's better for everything
Flexibility has a cost: you write 3-5x more code to build what LlamaIndex does out of the box. For simple RAG, Haystack is over-engineered. Save it for custom retrieval logic.
Haystack's 'pipeline as DAG' is cleaner than LlamaIndex's QueryEngine
DAGs are visually clear but require more explicit wiring. LlamaIndex's QueryEngine is more magical but also more opaque. Neither is objectively 'cleaner': it's a taste difference.
Haystack integrates seamlessly with any Hugging Face model
Haystack is tightly coupled to the Transformers library for local inference. If your model isn't on HF or you need vLLM/TensorRT optimization, you'll write custom components. LlamaIndex abstracts this better via LiteLLM.
Code examples
Task: Load documents from a directory, build an index, and run a query against it.
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.llms.openai import OpenAI
import os
# LlamaIndex auto-handles doc loading, chunking, embedding
documents = SimpleDirectoryReader(input_dir="./data").load_data()
# Automatic indexing with default OpenAI embeddings
index = VectorStoreIndex.from_documents(documents)
# Create query engine: single interface for retrieval + generation
query_engine = index.as_query_engine(llm=OpenAI(api_key=os.environ["OPENAI_API_KEY"]))
response = query_engine.query("What is the main topic of these documents?")
print(response)LlamaIndex abstracts indexing, embedding, and retrieval into high-level methods: you get a working RAG app with 6 lines of code. The framework handles chunking, metadata extraction, and caching automatically.
from haystack import Document, Pipeline
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.components.builders.prompt_builder import PromptBuilder
from haystack.components.generators import OpenAIGenerator
from pathlib import Path
import os
# Haystack requires explicit document loading
documents = [Document(content=p.read_text()) for p in Path("./data").glob("*.txt")]
doc_store = InMemoryDocumentStore()
doc_store.write_documents(documents)
# Build pipeline step-by-step
pipeline = Pipeline()
pipeline.add_component("retriever", InMemoryBM25Retriever(document_store=doc_store))
pipeline.add_component("prompt_builder", PromptBuilder(template="Answer: {query}\nContext: {documents}"))
pipeline.add_component("generator", OpenAIGenerator(api_key=os.environ["OPENAI_API_KEY"]))
# Connect components explicitly
pipeline.connect("retriever.documents", "prompt_builder.documents")
pipeline.connect("prompt_builder.prompt", "generator.prompt")
result = pipeline.run({"retriever": {"query": "What is the main topic?"}, "prompt_builder": {"query": "What is the main topic?"}})
print(result["generator"]["replies"])Haystack requires explicit component construction and wiring: you control every step of the pipeline. More code upfront, but complete visibility into retrieval, prompt building, and generation logic.
Migration path
- From LlamaIndex to Haystack:
- Install: pip install haystack-ai (v2+) instead of llama-index.
- Replace SimpleDirectoryReader + VectorStoreIndex with Document construction + InMemoryDocumentStore + InMemoryBM25Retriever.
- Replace QueryEngine with a Pipeline; add PromptBuilder and OpenAIGenerator components.
- Connect components via pipeline.connect(): what was implicit in QueryEngine is now explicit.
- Run queries via pipeline.run({component: {input}}) instead of query_engine.query(). From Haystack to LlamaIndex:
- Install: pip install llama-index.
- Replace Document creation + document store with SimpleDirectoryReader().
- Replace Pipeline + component wiring with VectorStoreIndex.from_documents().
- Replace PromptBuilder + OpenAIGenerator with index.as_query_engine().
- Query via query_engine.query(): 1 line instead of pipeline.run() with explicit wiring. Migration is a rewrite: pick based on your architecture need, not as a drop-in replacement.
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