LlamaIndex vs Haystack
LlamaIndex
Python/TypeScript library specialized in retrieval-augmented generation with intelligent document indexing and query engines.
Teams building LLM applications, semantic search systems, document Q&A platforms, and production RAG pipelines needing fast iteration.
Haystack
Production-focused RAG framework optimized for document search, retrieval, and question-answering pipelines.
Enterprise teams needing diverse NLP capabilities, projects combining traditional NLP with RAG, organizations requiring extensive pipeline customization.
Short Answer
LlamaIndex specializes in retrieval-augmented generation (RAG) with a focus on data indexing and querying, while Haystack is a broader NLP framework supporting both RAG and traditional NLP pipelines. LlamaIndex excels at LLM-centric applications, whereas Haystack offers more flexibility for diverse NLP tasks.
Our Verdict
AI-assistedChoose LlamaIndex if you're building RAG applications, need LLM-first architecture, or want a specialized tool with faster setup and strong vector database support. Choose Haystack if you need a versatile NLP framework supporting traditional NLP tasks alongside RAG, prefer a more established enterprise solution, or want flexibility across different use cases.
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Choose LlamaIndex if
Teams building LLM applications, semantic search systems, document Q&A platforms, and production RAG pipelines needing fast iteration.
Choose Haystack if
Enterprise teams needing diverse NLP capabilities, projects combining traditional NLP with RAG, organizations requiring extensive pipeline customization.
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Key Differences at a Glance
Key Facts & Figures
| Metric | LlamaIndex | Haystack | Diff |
|---|---|---|---|
| Vector Store Integrations(count) | 35+ | 10+ (Elasticsearch, Weaviate, Pinecone, Qdrant) | +250% |
| Monthly NPM/PyPI Downloads(downloads) | 180,000+ | 280 thousand | -36% |
| Documentation Pages(pages) | 500+ | 350+ | +43% |
| Vector Database Integrations(integrations) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | — | — |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | — | — |
| Average Setup Time(minutes) | 2-4 hours | — | — |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | — | — |
| Latest Release Activity(commits per month (avg)) | 150+ commits/month | — | — |
| Pre-trained Models(models) | 100+ integrations | — | — |
| Data Connectors/Loaders(connectors) | 200+ | — | — |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | — | — |
| GitHub Stars(stars) | 33,000+ | 15,200+ | +117% |
| LLM Integrations(integrations) | 45+ providers | 50+ | -10% |
| Vector Store Support(integrations) | 35+ stores | — | — |
| Enterprise Market Share(%) | 28% of RAG-focused projects | — | — |
| Setup Time for Basic RAG(minutes) | 5-10 minutes | 15-25 minutes | -65% |
| LLM Provider Integrations(count) | 30+ | 30+ | — |
| Memory Types Supported(count) | 3 (chat history, retrieval context, summary) | 3 (chat history, retrieval context, summary) | — |
| Document Processors Available(count) | 15+ (OCR, summarization, metadata, etc.) | 15+ (OCR, summarization, metadata, etc.) | — |
| Typical Memory Footprint (Loaded State)(MB) | 256-384 MB | 256-384 MB | — |
| Agent Types(count) | 2 (basic retrieval agent) | 2 (basic retrieval agent) | — |
All figures sourced from publicly available data. Last updated Jun 2026.
Key Differences
LlamaIndex
RAG and LLM data indexing
Haystack
General NLP pipelines and RAG
LlamaIndex
40+ models (OpenAI, Anthropic, Hugging Face, local)
Haystack
50+ models (broader ecosystem coverage)🏆
LlamaIndex
35+ vector databases🏆
Haystack
30+ vector databases
LlamaIndex
Easier for RAG-focused projects🏆
Haystack
Steeper due to broader scope
LlamaIndex
28,500+ stars🏆
Haystack
17,200+ stars
LlamaIndex
Excellent with 500+ examples🏆
Haystack
Very good with 350+ tutorials
LlamaIndex
180,000+ monthly🏆
Haystack
85,000+ monthly
Full Comparison
| Attribute | LlamaIndex |  Haystack |
|---|---|---|
| Vector Store Integrations(count) | 35+ | 10+ (Elasticsearch, Weaviate, Pinecone, Qdrant) |
| Primary Use Case Optimization(null) | RAG and retrieval-augmented systems | — |
| LLM Provider Integrations(count) | 30+ | — |
| Memory Types Supported(count) | 3 (chat history, retrieval context, summary) | — |
| Document Processors Available(count) | 15+ (OCR, summarization, metadata, etc.) | — |
Show 1 more attributeAgent Types(count) 2 (basic retrieval agent) — | ||
| Monthly NPM/PyPI Downloads(downloads) | 180,000+ | 280 thousand |
| Documentation Pages(pages) | 500+ | 350+ |
| Enterprise Support Available | Yes (LlamaIndex Cloud) | Yes (Haystack Cloud) |
| License Type | MIT (open source) | Elastic License (commercial) |
| Vector Database Integrations(integrations) | 20+ (Pinecone, Weaviate, Milvus, Qdrant, Chroma, etc.) | — |
| Primary Language Support(languages) | Python (primary), TypeScript/JavaScript | — |
| LLM Model Providers Supported(providers) | 40+ (OpenAI, Claude, Gemini, Ollama, LLaMA, etc.) | — |
| Average Setup Time(minutes) | 2-4 hours | — |
| Enterprise Connectors(connectors) | 20+ (Slack, Notion, Google Workspace, etc.) | — |
| Azure/Microsoft Ecosystem Integration(integration level) | Minimal (basic Azure OpenAI support) | — |
| Latest Release Activity(commits per month (avg)) | 150+ commits/month | — |
| Pre-trained Models(models) | 100+ integrations | — |
| Data Connectors/Loaders(connectors) | 200+ | — |
| Transformers Library Monthly Downloads(downloads) | Not tracked separately | — |
| Enterprise Market Share(%) | 28% of RAG-focused projects | — |
| Production Observability Features(null) | Built-in logging, caching, callback handlers | — |
| Production Monitoring Tools(tool availability) | Basic logging via LlamaDebug | — |
| API Inference Service(null) | No native inference API | — |
| Learning Curve (weeks to productivity)(weeks) | 1-2 weeks | — |
| GitHub Stars(stars) | 33,000+ | 15,200+ |
| LLM Integrations(integrations) | 45+ providers | 50+ |
| Vector Store Support(integrations) | 35+ stores | — |
| RAG Pipeline Maturity(maturity level) | Purpose-built with auto-optimization | — |
| Agent Framework Maturity(maturity level) | Emerging (basic tool support) | — |
| Setup Time for Basic RAG(minutes) | 5-10 minutes | 15-25 minutes |
| Typical Memory Footprint (Loaded State)(MB) | 256-384 MB | — |
HaystackShow 1 more attribute
Visual Comparison
Side-by-side comparison of numeric attributes
Pros & Cons
LlamaIndex
Pros
- Purpose-built for RAG with intuitive data indexing abstractions
- 40+ LLM integrations including OpenAI, Claude, Llama 2, and local models
- 35+ vector store connectors (Pinecone, Weaviate, Milvus, Chroma)
- Active community with 28,500+ GitHub stars and strong documentation
- Lightweight design optimized for production RAG pipelines
Cons
- Limited outside RAG domain—lacks traditional NLP capabilities
- Smaller ecosystem compared to Haystack for non-RAG use cases
Haystack
Pros
- Broader NLP support beyond RAG (NER, classification, question answering)
- 50+ LLM integrations with enterprise-grade reliability
- Modular pipeline architecture supporting complex multi-step workflows
- Strong enterprise adoption and production track record
- Flexible component system allowing custom model integration
Cons
- Steeper learning curve due to broader scope and abstraction levels
- Smaller community (17,200 stars) compared to LlamaIndex
Frequently Asked Questions
LlamaIndex is superior for this use case. It provides simpler abstractions for document indexing, retrieval, and LLM querying out-of-the-box. Setup takes 5-10 minutes versus 15-25 minutes with Haystack, and LlamaIndex's vector store integrations (35+) are more comprehensive. Use LlamaIndex unless you need additional NLP pre/post-processing.
Resources & Learn More
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