AI DevelopmentComparison
LangChain and LlamaIndex are the two most widely used open-source frameworks for building LLM applications. LangChain started as a chains-based orchestration framework for building AI pipelines and agents — it covers the broadest set of LLM use cases. LlamaIndex is more specialized, focused on data indexing, retrieval, and RAG applications — it's the best framework specifically for connecting LLMs to your private data.
Metric
9.1
Overall Score
8.8
Overall Score
7-Dimension Scorecard
Core Capability
9.0
Most comprehensive LLM framework. Chains, agents, RAG, and LangGraph for multi-agent.
9.0
Best-in-class for RAG applications — data connectors, indexing strategies, query engines, and retrieval primitives are purpose-built for LLM data access.
Ease of Use
7.0
Powerful but complex. Abstraction can be a hindrance. Learning curve is steep.
7.0
Python-first. Well-documented but requires LLM application development experience to use effectively.
Integrations
10.0Better
200+ model providers, vector stores, and tool integrations.
9.0
160+ data connectors, 30+ LLM providers, 40+ vector stores. Broad ecosystem coverage.
Pricing Value
9.5
Open source — free to use. LangSmith for observability at $39/mo for developers.
9.5
Fully open-source and free. LlamaCloud managed service for those who want hosted indexing and retrieval.
AI Sophistication
9.5
LangGraph for complex multi-agent workflows. Best-in-class agent orchestration.
9.5
Purpose-built for LLM applications — advanced retrieval strategies (HyDE, ReRank, FLARE), multi-modal support, and agentic workflows.
Community & Support
10.0Better
Largest AI framework community. 90k+ GitHub stars. Extensive docs.
9.0
Large and active community, excellent documentation, regular office hours, and a thriving Discord.
Scalability
9.0Better
LangSmith Enterprise for production teams. LangGraph Cloud for hosted agents.
8.5
Self-hosted scales with your infrastructure. LlamaCloud provides managed scalability for production deployments.
Verdict
LangChain is the better general-purpose LLM framework for agents, tool use, and complex AI pipelines. LlamaIndex is the better choice for RAG-focused applications where sophisticated data indexing, retrieval strategies, and knowledge base search are the core requirements.
Choose LangChain
Use LangChain if you're building agents, tool-using workflows, complex LLM chains, or need the broadest ecosystem of model providers, tools, and integrations.
Full ScorecardChoose LlamaIndex
Use LlamaIndex if your primary use case is RAG — connecting LLMs to your documents, PDFs, databases, or knowledge base with advanced retrieval strategies.
Full ScorecardPricing
LangChain
Open SourceFree
LangSmith Dev$39/mo
LangSmith Plus$39+/mo
EnterpriseCustom
LlamaIndex
Open Source$0
LlamaCloudFrom $97/mo
EnterpriseCustom
Frequently Asked Questions
Can LangChain do RAG?
Yes — LangChain has RAG capabilities via its retrieval chains and document loaders. However, LlamaIndex's retrieval primitives (query engines, retrievers, node postprocessors) are more specialized and often achieve better results for RAG-specific applications.
Can LlamaIndex do agents?
Yes — LlamaIndex has agent capabilities and LlamaAgents for multi-agent workflows. However, LangGraph (LangChain's agent framework) is more mature and flexible for complex agentic workflows that go beyond RAG.
Do many teams use both?
Yes — it's common to use LlamaIndex for the retrieval/RAG layer and LangChain for orchestration, tool use, and the agent layer. The two frameworks are complementary and have native integrations with each other.
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