HomeCompareLlamaIndex vs Haystack
Framework Comparison
LlamaIndexVSHaystack

Which AI Agent Framework Should You Choose?

A detailed comparison of LlamaIndex and Haystack — features, learning curve, use cases, community, and which has more agencies building with it.

23 LlamaIndex agencies13 Haystack agencies
23
LlamaIndex Agencies
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VS
13
Haystack Agencies
Browse →

Side-by-Side Comparison

LlamaIndex
Haystack
Type
Data framework for LLM/RAG applications
Modular NLP and RAG pipeline framework
Language
Python & TypeScript
Python only
Learning Curve
Moderate
Moderate — pipeline DSL takes time to learn
Best For
Production RAG, document Q&A, agentic data retrieval
Enterprise NLP pipelines, evaluation-heavy workflows
Multi-agent Support
Growing — LlamaIndex Agents
Limited — pipeline-focused
Production Readiness
High
High — strong in European enterprise
Community Size
Large (35k+ GitHub stars)
Medium (15k+ GitHub stars)

When to choose LlamaIndex

  • You need rich data connectors — LlamaIndex ships with 100+ loaders for PDFs, databases, APIs, and proprietary data sources.
  • Advanced index types (hierarchical, summary, knowledge graph) are important for your retrieval quality requirements.
  • LlamaCloud's managed ingestion and retrieval infrastructure reduces operational complexity for your team.
  • TypeScript support is required — LlamaIndex has a mature TS SDK alongside its Python core.
  • Your use case involves agentic query workflows where agents select retrieval strategies dynamically.
Find LlamaIndex Agencies →

When to choose Haystack

  • Mature evaluation tooling is a priority — Haystack's BEIR integration and evaluation pipelines are industry-leading.
  • Strong pipeline abstractions with a declarative DSL suit teams that want composable, readable pipeline definitions.
  • deepset Cloud provides managed Haystack hosting for teams that want SaaS deployment without infrastructure management.
  • Your organisation operates primarily in European enterprise markets where Haystack has a well-established track record.
  • Document processing pipelines with strong preprocessing, OCR, and structured extraction are core requirements.
Find Haystack Agencies →
Frequently Asked Questions
What is the key difference between LlamaIndex and Haystack for RAG?+

LlamaIndex is designed around flexible data ingestion and diverse index types, making it stronger for RAG applications that require connecting many data sources or using advanced retrieval strategies. Haystack takes a pipeline-first approach with strong evaluation tooling, making it better suited for teams that need to rigorously measure and improve retrieval quality.

Which is better for large-scale document processing?+

Both handle large-scale document processing well. Haystack has particularly strong document preprocessing capabilities with mature OCR and extraction integrations. LlamaIndex's 100+ data loaders give it an edge for connecting diverse document sources, while LlamaCloud offers managed ingestion infrastructure for high-volume workloads.

Is there a difference in European versus US market preference?+

Yes — Haystack, built by deepset (a German company), has a stronger presence and track record in European enterprise markets. LlamaIndex has broader adoption in North American and global markets. For European enterprises with existing deepset relationships or GDPR-sensitive deployments, Haystack may be the more natural choice.

How does evaluation tooling compare between the two?+

Haystack has historically had stronger built-in evaluation tooling, including BEIR benchmark integration and comprehensive pipeline evaluation utilities. LlamaIndex has been closing this gap with LlamaEval and its evaluation modules, but teams for whom evaluation is a first-class concern often still prefer Haystack's more mature evaluation ecosystem.

Which framework do AI agencies typically use for each?+

AI agent agencies building general RAG products, document Q&A systems, or agentic data pipelines tend to favour LlamaIndex for its data connectivity and growing agent capabilities. Agencies with strong NLP research backgrounds or European enterprise clients often prefer Haystack for its evaluation rigour and pipeline composability. Ask any agency you're evaluating to describe their retrieval evaluation process — their answer will reveal which framework they're truly expert in.

Find LlamaIndex and Haystack Agencies

When evaluating an AI agent agency's LlamaIndex versus Haystack expertise, ask specifically about their retrieval evaluation process: how do they measure retrieval precision and recall for your document type, and how do they iterate on chunking and indexing strategy? An agency with genuine depth will describe a systematic evaluation methodology, not just default to whichever framework they're most comfortable with.

Which has more agencies?

In our directory, there are currently 23 LlamaIndex agencies and 13 Haystack agencies. LlamaIndex leads the directory — reflecting its longer history and broader ecosystem adoption. However, Haystack agency numbers are growing as the framework matures.

23 LlamaIndex Agencies →13 Haystack Agencies →

Bottom line

LlamaIndex is the stronger choice for RAG-first agentic applications where data connectivity, index diversity, and query flexibility are paramount. Haystack excels in evaluation-centric NLP pipelines with deep enterprise roots, particularly in European markets. Both are production-ready; the decision often comes down to whether your priority is data ingestion breadth and agentic flexibility (LlamaIndex) or evaluation rigour and pipeline composability (Haystack).

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