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LangChain vs LlamaIndex

General-purpose LLM orchestration framework vs data-focused RAG and indexing framework — choosing the right foundation for LLM application development.

Overview​

LangChain and LlamaIndex are both Python/TypeScript frameworks for building LLM applications, but they solve different primary problems. LangChain is a general-purpose orchestration framework with composable chains, agents, and tool integrations for diverse LLM workflows. LlamaIndex (formerly GPT Index) is a data framework focused on connecting LLMs to data sources — its core strengths are document indexing, retrieval strategies, and RAG pipeline optimization.

In practice, many production systems combine both: LlamaIndex for data ingestion and retrieval, LangChain for orchestration and agent logic. Understanding their distinct strengths helps architects decide where each framework fits in the application stack.

For production RAG architecture patterns using these frameworks, see the Production RAG Systems guide.

Architecture Diagram​

┌─────────────────────────────────────────────────────────────────┐
│                     LLM Application Layer                       │
│  ┌──────────────┐  ┌──────────────┐  ┌───────────────────────┐  │
│  │ RAG System   │  │ AI Agent     │  │ Data Analysis         │  │
│  │              │  │ Workflow     │  │ Pipeline              │  │
│  └──────┬───────┘  └──────┬───────┘  └───────────┬───────────┘  │
│         └──────────────────┴─────────────────────┘              │
└─────────────────────────────┬───────────────────────────────────┘
                              │
              ┌───────────────┴───────────────┐
              │                               │
    ┌─────────▼──────────┐        ┌───────────▼─────────┐
    │     LangChain       │        │     LlamaIndex       │
    ├─────────────────────┤        ├─────────────────────┤
    │ • Chain/graph       │        │ • Index abstractions │
    │   composition       │        │ • Document loaders   │
    │ • Agent framework   │        │ • Chunking engines   │
    │   (LangGraph)       │        │ • Query engines      │
    │ • Tool integrations │        │ • Response synthesis  │
    │ • Memory management │        │ • Retrieval strategies│
    │ • Output parsers    │        │ • Evaluation tools    │
    └─────────────────────┘        └─────────────────────┘

Architecture Differences​

LangChain​

LangChain's architecture centers on composable primitives — chains, agents, tools, and memory. LangGraph extends this with stateful graph-based workflows for complex agent patterns. The framework is designed to be the orchestration layer that connects LLMs to tools, databases, APIs, and other services. It provides a unified interface across LLM providers (OpenAI, Anthropic, Google, local models via Ollama).

LlamaIndex​

LlamaIndex's architecture focuses on the data layer. It provides abstractions for document loading, text splitting, embedding, indexing, and retrieval. The framework includes purpose-built components like vector store indices, knowledge graphs, document summary indices, and tree indices — each optimized for different retrieval patterns. Its query engine handles context window management and response synthesis automatically.

Feature Comparison Table​

FeatureLangChainLlamaIndex
Primary Use CaseLLM orchestration, agents, and tool integrationData indexing, retrieval, and RAG optimization
Core AbstractionChains, agents, toolsIndices, query engines, retrievers
RAG SupportBasic retrieval chainsAdvanced retrieval (hybrid, recursive, multi-step)
Agent FrameworkLangGraph (stateful graph agents)Agent-like query pipelines
Document Loading160+ document loaders via integrations160+ data connectors (LlamaHub)
ChunkingRecursiveCharacterTextSplitter, othersSentenceSplitter, SemanticSplitter, others
MemoryConversation buffer, summary, entity memoryChat memory via ChatEngine
Output ParsingStructured output parsers, Pydantic modelsStructured output via query engines
StreamingNative streaming supportNative streaming support
TypeScript SupportLangChain.js (full parity)LlamaIndex.TS (growing parity)
EvaluationLangSmith (separate platform)Built-in evaluation modules
Vector Store Support50+ vector store integrations40+ vector store integrations

Deployment Considerations​

LangChain​

  • Dependency footprint: Large dependency tree — use langchain-core for minimal installations
  • LangServe: Built-in HTTP serving with automatic OpenAPI documentation
  • LangGraph Platform: Managed deployment for stateful agent workflows
  • Versioning: Rapid release cycle — pin versions carefully in production
  • Observability: Native LangSmith integration for tracing and debugging

LlamaIndex​

  • Modular installation: llama-index-core plus optional integration packages
  • LlamaCloud: Managed parsing and indexing service for enterprise documents
  • Deployment: Standard Python packaging — deploy as API service or embed in applications
  • Versioning: Stable API surface with clear migration guides between major versions
  • Observability: OpenTelemetry-compatible instrumentation, Langfuse integration

Security Capabilities​

Security FeatureLangChainLlamaIndex
Input ValidationVia integration (Guardrails AI, etc.)Via integration
Prompt Injection DefenseVia LangChain guardrails or external toolsVia external tools
PII DetectionVia integration (Presidio, etc.)Via integration
API Key ManagementEnvironment variables, LangSmith secretsEnvironment variables
Sandboxed ExecutionLangGraph with tool permissionsNot built-in
Output GuardrailsVia output parsers and validatorsVia response evaluators

For securing LLM pipelines built with these frameworks, see Secure LLM Pipelines and Prompt Injection Defense.

Choose LangChain When​

  • You are building agent-based workflows with tool calling and multi-step reasoning
  • Your application orchestrates multiple LLM providers, APIs, and external services
  • You need stateful conversation management with complex memory patterns
  • LangSmith integration for development debugging and production monitoring is valuable
  • The application is primarily an orchestration layer, not a data retrieval system

Choose LlamaIndex When​

  • RAG is the core use case and retrieval quality is the primary optimization target
  • You need advanced retrieval strategies (hybrid, recursive, multi-document)
  • Document processing pipelines with custom chunking and metadata extraction are required
  • You want built-in evaluation tools for retrieval and response quality
  • The application is data-centric — connecting LLMs to structured and unstructured data sources

Use Both When​

  • LlamaIndex handles data ingestion, indexing, and retrieval
  • LangChain orchestrates the overall workflow, agent logic, and tool integrations
  • This is a common production pattern for complex RAG applications