LangChain vs LlamaIndex: Framework Comparison for RAG and Agents

LangChain and LlamaIndex are the two most widely used Python frameworks for building LLM applications in 2026, and they are frequently compared because they overlap significantly in use cases — retrieval-augmented generation (RAG) and AI agents in particular.

The comparison is more nuanced than picking a winner. Both are actively developed, both are used in production at scale, and both have expanded into territory the other originally owned. The right choice depends on whether your primary challenge is retrieval quality or agent orchestration breadth.

For foundational concepts, see What Is Retrieval Augmented Generation? and What Is an AI Agent Framework?.

Decision Snapshot#

• Pick LlamaIndex when your primary use case is document retrieval at depth — complex knowledge bases, heterogeneous data sources, structured plus unstructured data, and when retrieval precision is the first-order concern.
• Pick LangChain when you need a broad agent platform — diverse tools, memory management, multi-step chain composition, and integration with systems beyond document retrieval.
• Use both together when your agent needs best-in-class retrieval (LlamaIndex query engines) as tools within a broader orchestration pattern (LangChain agents or LangGraph).

Feature Matrix#

| Dimension | LangChain | LlamaIndex | |---|---|---| | Primary design philosophy | Composable AI application framework | Data framework for LLM applications | | Core mental model | Chains, agents, tools, memory, retrievers | Documents, nodes, indexes, query engines | | RAG support | Mature — full retrieval ecosystem | Strongest in class — retrieval-first design | | Document indexing depth | Standard chunking and vector indexing | Advanced — tree, list, keyword, graph indexes | | Query engine diversity | Vector similarity, sparse search | 10+ query engine types including SQL, Pandas | | Structured data retrieval | SQL agent, tool-based | Native text-to-SQL, Pandas engines | | Agent support | Strong — ReAct, OpenAI Functions, LangGraph | Growing — agent module + Workflows API | | Multi-agent orchestration | LangGraph — explicit stateful graphs | LlamaIndex Workflows — event-driven | | Tool ecosystem | 100+ pre-built tool integrations | Growing tool library | | LLM integrations | 100+ via integrations | 50+ via integrations | | Vector store integrations | 50+ | 30+ | | Observability | LangSmith (paid) | LlamaTrace / Arize Phoenix integration | | Community size | Largest LLM framework community | Large and rapidly growing | | Learning curve | Moderate to high — broad surface area | Moderate — steeper for agent patterns | | Production maturity | More battle-tested at scale | Production-ready, tighter scope |

What LangChain Is#

LangChain is a composable framework for building LLM-powered applications in Python and TypeScript. Released in late 2022 by Harrison Chase, it quickly became the dominant framework for AI application development by providing abstractions across every layer: prompt templates, LLM interfaces, output parsers, memory systems, tool definitions, retrieval components, and agent architectures.

LangChain's core value proposition is its breadth. It integrates with virtually every LLM provider, vector database, and data source that matters. Its expression language (LCEL) allows developers to compose these components declaratively. LangGraph, the stateful multi-agent extension, brings explicit graph-based control flow for complex agent architectures.

For teams building systems that span retrieval, tool use, external API calls, memory, and multi-step reasoning — and who need integrations with diverse infrastructure — LangChain provides a unified framework where components are pre-built and composable.

Key strengths:

• Broadest ecosystem across LLMs, vector stores, and tools
• LangGraph for stateful multi-agent architectures
• LCEL for declarative chain composition
• LangSmith for production observability
• Largest community and most extensive documentation

For a hands-on implementation walkthrough, see Build AI Agents with LangChain.

What LlamaIndex Is#

LlamaIndex (formerly GPT Index) is a data framework for LLM applications, with a primary focus on connecting LLMs to diverse data sources through sophisticated indexing and retrieval architectures. Created by Jerry Liu and released in 2022, it has grown into one of the most capable retrieval frameworks available.

LlamaIndex's core insight is that retrieval quality — not agent logic — is the primary bottleneck for most real-world LLM applications. It provides a richer indexing model than LangChain: beyond standard vector similarity search, LlamaIndex supports tree indexes (for hierarchical summarization), list indexes, keyword table indexes, knowledge graph indexes, and composite indexes that combine multiple types.

Its query engine abstraction is powerful: a single interface handles retrieval from vector databases, SQL databases, Pandas DataFrames, and knowledge graphs interchangeably. This heterogeneous retrieval capability is a significant advantage for enterprise applications where information is spread across structured and unstructured sources.

LlamaIndex has also expanded into agents and workflows. The Workflows API (released 2024) provides event-driven, stateful multi-step pipeline orchestration that competes with LangGraph as an alternative architecture for complex agent systems.

Key strengths:

• Deepest retrieval architecture — multiple index types and query engines
• Heterogeneous data: unstructured documents, SQL, DataFrames in one pipeline
• LlamaIndex Workflows for event-driven stateful agent orchestration
• Data connectors (LlamaHub) for 100+ data source integrations
• Growing ecosystem for evaluation and observability

For a deep dive on retrieval architecture decisions, see Introduction to RAG for AI Agents.

Deep Dive: Retrieval Architecture#

Retrieval quality is where LlamaIndex most clearly differentiates from LangChain.

Standard RAG in LangChain uses a retriever component — typically a vector store retriever with similarity search or maximum marginal relevance (MMR) — combined with a prompt that injects retrieved context. LangChain supports hybrid search, re-ranking, and contextual compression, but these require assembling components manually.

LlamaIndex retrieval starts from a richer indexing foundation. Several index types address different retrieval challenges:

• VectorStoreIndex: Standard semantic similarity retrieval — equivalent to LangChain's vector retriever
• SummaryIndex: Stores all nodes sequentially; useful for summarization tasks over long documents
• TreeIndex: Builds a hierarchical tree of summaries; answers queries by traversing from root to leaf nodes
• KeywordTableIndex: Builds keyword-to-document mappings; effective for exact term retrieval
• KnowledgeGraphIndex: Extracts entity-relationship triples; enables graph-based traversal queries

LlamaIndex also provides query engine combinators — tools that route queries to different indexes based on query type, or synthesize answers across multiple indexes. For applications where a single vector index is insufficient (legal document review, technical documentation with structured and prose content, multi-source enterprise knowledge bases), these primitives are material.

Both frameworks also support post-retrieval re-ranking using models like Cohere Rerank or cross-encoders — an important step for improving final retrieval precision that teams often overlook.

Deep Dive: Agent and Workflow Architecture#

This dimension has shifted significantly in the last 18 months as LlamaIndex has invested heavily in agent capabilities.

LangChain agents use ReAct, OpenAI Functions/Tools, or custom reasoning loops to select and call tools iteratively. LangGraph is the stateful extension: developers define a directed graph where nodes are processing functions and edges define control flow. This explicit graph model works well for workflows with clear state machines, conditional branching, and human-in-the-loop steps.

LlamaIndex Workflows uses an event-driven model: steps emit typed events, and other steps subscribe to those events. This is a fundamentally different mental model than LangGraph's explicit graph — one that some developers find more natural for asynchronous, concurrent agent patterns. Workflows supports async execution natively, making it well-suited for agents that need to run multiple tool calls in parallel.

LlamaIndex agents can use any LlamaIndex query engine as a tool — meaning an agent can have a tool backed by a SQL database, another tool backed by a vector index, and another tool backed by an API, all queried through a consistent interface. For retrieval-heavy agents, this composability is elegant.

For multi-agent orchestration architecture decisions, see Multi-Agent Systems Guide and CrewAI vs LangChain.

Use-Case Recommendations#

Choose LlamaIndex when:#

• Your primary use case is RAG over complex, large, or heterogeneous document corpora
• You need multiple index types for different document structures
• Your application retrieves from both unstructured (documents) and structured (SQL, DataFrames) sources
• Retrieval precision is your primary quality metric
• You prefer the Workflows event-driven model over LangGraph's explicit graph

Choose LangChain when:#

• You need a broad agent ecosystem with many pre-built tool integrations
• Your agent workflows involve diverse tool types beyond document retrieval
• You need production observability through LangSmith
• LCEL's declarative chain composition model suits your team's style
• You need the widest LLM and vector store coverage with minimal integration work

Use both when:#

• You need best-in-class retrieval (LlamaIndex query engines as tools) within a broader LangChain agent architecture
• Your team has split ownership: data engineering team owns the retrieval layer (LlamaIndex), application engineering team owns the agent layer (LangChain)
• You are building a production system where retrieval quality and agent orchestration are both first-order concerns

Cost and Ecosystem Considerations#

Both frameworks are open-source (MIT license) and free to use. Costs arise from:

• LLM API consumption (same for both — framework choice does not affect model costs)
• Vector database costs (both integrate with the same providers)
• Observability: LangSmith charges per trace for LangChain observability; LlamaIndex integrates with Arize Phoenix (open-source) and other providers
• Engineering time: LlamaIndex's tighter focus on retrieval may mean less time learning irrelevant abstractions for RAG-primary teams; LangChain's breadth may save time for teams needing diverse integrations

Verdict Summary#

LangChain and LlamaIndex are complementary more than competitive. LlamaIndex is the retrieval specialist; LangChain is the general-purpose AI application platform. For teams where retrieval quality is the central challenge — enterprise search, technical documentation Q&A, legal document analysis — LlamaIndex's indexing and query engine primitives justify making it the primary framework.

For teams building diverse agentic workflows where retrieval is one capability among many, LangChain's broader ecosystem and LangGraph's agent orchestration make it the more complete platform.

The production pattern many teams arrive at: LlamaIndex query engines as retrieval tools within LangChain or LangGraph agent architectures. This combination gets the best of both frameworks without arbitrary constraint.

For broader framework comparisons, see Open-Source vs Commercial AI Agent Frameworks and CrewAI vs LangChain.

Frequently Asked Questions#

Can LlamaIndex build AI agents?#

Yes. LlamaIndex has a full agent module and the newer Workflows API for event-driven stateful multi-step orchestration. It is strongest for agents where retrieval is the primary tool.

Is LlamaIndex harder to learn than LangChain?#

Different rather than harder. LlamaIndex's concepts map naturally to retrieval problems. LangChain's surface area is broader, making full mastery take longer.

Do LangChain and LlamaIndex work together?#

Yes. A common production pattern: LlamaIndex query engines as tools within LangChain agents or LangGraph workflows.

Which has better structured data retrieval?#

LlamaIndex, with native text-to-SQL query engines and Pandas DataFrame query engines alongside vector retrieval.

What is LlamaIndex Workflows?#

An event-driven stateful pipeline orchestration API, competing with LangGraph as an alternative mental model for complex agent workflows.

Which framework is better for production at scale?#

Both are production-ready. LangChain has more mature observability tooling (LangSmith). LlamaIndex is easier to tune for retrieval-heavy applications.