LangChain vs LlamaIndex
LangChain vs LlamaIndex compared - RAG, agents, data integration, and performance. Find out which AI framework fits your use case in 2026.
Saturncube
12 August 2026
If you are building an application that connects a large language model to real data whether that is a document search tool, an AI assistant that answers from your internal knowledge base, or an autonomous agent that reasons across multiple steps you have almost certainly encountered both LangChain and LlamaIndex. These are the two most widely adopted open-source Python frameworks for LLM application development, and they are frequently compared against each other.
The honest answer to "LangChain vs LlamaIndex: which is better?" is that they solve overlapping but distinct problems, and the right choice depends on what you are actually building. This article breaks down what each framework does, where each one excels, how they compare on the decisions that matter most, and when it makes sense to use both together.
What Is LangChain?
LangChain is an open-source framework for building applications powered by large language models. It was first released in October 2022 and has since become one of the most widely used libraries in the LLM ecosystem, with a large contributor community and broad integrations across LLM providers, vector databases, tools, and deployment environments.
LangChain's core design philosophy is composability. It provides building blocks chains, agents, tools, memory, retrievers, and output parsers that developers can combine to construct applications of varying complexity. Whether you need a simple question-answering interface over a document set or a multi-agent system that uses external tools, calls APIs, and maintains state across long interactions, LangChain provides the abstractions to build it.
LangChain's primary strengths:
LangChain has evolved significantly since its initial release. LangGraph, introduced as a separate but closely integrated package, is now LangChain's recommended approach for building agents with complex control flow, persistent state, and human-in-the-loop capabilities.
What Is LlamaIndex?
LlamaIndex (previously GPT Index) is an open-source data framework designed specifically for connecting LLMs to external data sources. It was released in late 2022 alongside LangChain and has developed a distinct focus: making it straightforward to ingest, structure, query, and retrieve data from a wide range of sources for use with LLMs.
Where LangChain emphasises composable LLM application architecture broadly, LlamaIndex emphasises the data layer specifically the ingestion pipelines, indexing strategies, retrieval algorithms, and query engines that determine how effectively an LLM can access and reason over external information.
LlamaIndex's primary strengths:
LlamaIndex's positioning has become more product-oriented over time. The team has moved from purely open-source tooling toward a complete platform (LlamaCloud) for enterprise RAG, while keeping the core open-source library actively maintained.
How LangChain Works
LangChain's architecture is built around a set of composable abstractions. Understanding how these fit together clarifies what the framework is and is not suited for.
Core components:
Chains: The fundamental building block a sequence of calls to LLMs, tools, or other components. Chains can be simple (a single prompt with an LLM call) or complex (multiple LLM calls with conditional logic, tool use, and output transformation).
Agents: Dynamic systems where an LLM decides which actions to take, in what order, based on its observations. Agents use tools functions the LLM can call to interact with external systems and gather information before producing a final response.
LangGraph: LangChain's graph-based framework for building stateful agents. LangGraph represents agent workflows as directed graphs where each node is a processing step and edges define the flow of information. This makes it possible to build agents with persistent state, branching logic, cycles, and human approval steps patterns that are difficult to implement reliably with simple chain-based architectures.
Memory: Abstractions for storing and retrieving conversation history, enabling conversational AI applications that maintain context across turns.
Retrievers: Components that fetch relevant documents from a data store given a query the interface LangChain uses to connect agents and chains to external data. LangChain supports multiple retrieval backends but leaves the details of indexing and retrieval strategy to the developer or to a dedicated library like LlamaIndex.
LCEL (LangChain Expression Language): A declarative syntax for composing LangChain components into pipelines, with built-in support for streaming, async execution, and parallel processing.
How LlamaIndex Works
LlamaIndex is built around a data-centric architecture. Its components are designed to solve the problem of getting structured and unstructured data into a form that an LLM can reason over effectively.
Core components:
Data connectors (readers): LlamaIndex provides readers for 160+ data sources PDFs, Word documents, Notion databases, Google Drive, Slack, web pages, SQL databases, and many more. These handle the ingestion of raw data in formats that vary significantly in structure.
Node parsing: LlamaIndex transforms ingested documents into nodes chunks of text with associated metadata. The chunking strategy (size, overlap, relationship tracking between chunks) has a significant impact on retrieval quality, and LlamaIndex provides multiple parsing strategies to optimise for different content types.
Indexes: Data structures that organise nodes for efficient retrieval. LlamaIndex supports vector indexes (using embeddings for semantic similarity search), keyword indexes, summary indexes, and knowledge graph indexes each suited to different query patterns.
Retrievers: LlamaIndex's retrieval layer is more sophisticated than most frameworks. It supports hybrid search (combining vector and keyword retrieval), recursive retrieval (querying sub-documents when a top-level summary matches), and query decomposition (breaking a complex question into sub-questions and retrieving against each separately before synthesising a final answer).
Query engines: Interfaces that combine retrieval with LLM reasoning to produce final answers. LlamaIndex's query engines handle citation tracking, answer synthesis across multiple documents, and result re-ranking.
Response synthesis: The stage where retrieved context is combined with the original query and sent to the LLM for answer generation. LlamaIndex provides multiple synthesis strategies including tree summarisation (for long documents) and accumulate-and-reduce patterns.
LangChain vs LlamaIndex: Key Similarities
Despite their different emphases, LangChain and LlamaIndex share a number of characteristics that matter for practical usage.
Both are open-source Python libraries with active communities, regular releases, and extensive documentation. Both support multiple LLM providers OpenAI, Anthropic Claude, Google Gemini, Cohere, Mistral, Llama, and local models through Ollama. Both integrate with the major vector databases Pinecone, Weaviate, Chroma, Qdrant, Milvus, and others. Both support RAG pattern implementation, though they approach it differently. Both have TypeScript/JavaScript versions (LangChain.js and LlamaIndex.TS), though Python remains the primary and more feature-complete implementation for both.
Dimension | LangChain | LlamaIndex |
|---|---|---|
Primary focus | LLM application architecture and agents | Data ingestion, indexing, and retrieval for LLMs |
RAG complexity | Moderate — retrievers are pluggable but not the main focus | High — retrieval is the core specialisation |
Agent development | Strong — LangGraph provides stateful multi-agent support | Improving — agents supported but less mature than LangGraph |
Data source coverage | Good — common sources | Excellent — 160+ connectors via LlamaHub |
Retrieval sophistication | Standard retrieval patterns | Advanced — hybrid search, recursive retrieval, query decomposition |
Evaluation tooling | Basic | Strong — built-in RAG evaluation metrics |
Learning curve | Steeper for beginners | More focused — easier if RAG is the primary need |
Cloud offering | LangSmith (observability/tracing), LangGraph Platform (deployment) | LlamaCloud (managed RAG infrastructure) |
Community size | Larger | Smaller but highly specialised |
RAG and Data Integration
RAG is where LlamaIndex has the clearest advantage. If your primary use case is building an application that retrieves information from a corpus of documents and uses an LLM to synthesise answers, LlamaIndex's purpose-built retrieval machinery produces better results with less custom engineering.
Why LlamaIndex produces better RAG pipelines:
The quality of a RAG system depends critically on what gets retrieved. Standard top-k vector similarity search which is what most frameworks default to retrieves the k most semantically similar chunks to the query. This works for simple, focused queries but breaks down when:
LlamaIndex provides retrieval strategies that address these cases: recursive retrieval that navigates document hierarchies, hybrid search that combines vector similarity with keyword relevance, query decomposition that breaks complex questions into sub-queries before retrieval, and re-ranking that applies a cross-encoder model to improve the precision of the final retrieved context before synthesis.
LangChain can implement these patterns, but requires more custom code or integration with LlamaIndex's retrieval components to do so.
For practical guidance on building production RAG systems with these frameworks, see Saturncube's article on how to build AI agents from scratch, which covers the architectural decisions that apply across both libraries.
Agent Development
For agent development systems where an LLM dynamically decides which tools to call, in what order, and how to handle failures LangChain's LangGraph is currently the more mature and widely adopted solution.
LangGraph's advantages for agent development:
LangGraph represents agent workflows as directed graphs (nodes and edges) rather than as linear chains. This makes it natural to implement:
LlamaIndex has introduced agent capabilities and its query pipelines support multi-step reasoning, but for complex agentic architectures particularly those requiring stateful, long-running workflows with human oversight LangGraph is the current standard.
If you are building customer support automation, sales agents, or internal workflow automation, our guide on building a customer support AI agent with LangChain walks through the LangGraph architecture that handles these requirements in production.
Framework Flexibility and Ecosystem
LangChain integrations:
LangChain's integration surface is exceptionally broad. It supports the major LLM providers, all significant vector databases, web search tools (Tavily, SerpAPI, DuckDuckGo), code execution environments, external APIs, and dozens of specialised tools. Its LCEL syntax and runnable interface make it possible to compose any combination of these components into a pipeline without extensive boilerplate.
The LangSmith observability platform integrates tightly with LangChain applications providing tracing, evaluation, and debugging tools that are genuinely useful for understanding what an agent or chain is doing in production. For teams deploying production LLM applications, having this observability built in reduces the time spent debugging unexpected agent behaviour.
LlamaIndex integrations:
LlamaIndex's ecosystem strength is in data connectors. LlamaHub the community registry provides readers for 160+ data sources maintained by both the LlamaIndex team and community contributors. This means that connecting LlamaIndex to a new data source an obscure database, an internal tool, an enterprise SaaS platform is often a matter of finding and installing the appropriate reader rather than writing custom ingestion code.
LlamaIndex also integrates with all major vector databases and LLM providers. Its integration depth at the retrieval layer particularly for more sophisticated retrieval patterns tends to be stronger than LangChain's.
Performance and Scalability
Neither framework is a performance framework in the traditional sense both are orchestration layers that call external LLM APIs and data stores. Performance in LLM applications is dominated by LLM API latency and vector database query latency, both of which are determined by the underlying services rather than the orchestration framework.
That said, there are framework-level considerations:
Async support: Both LangChain and LlamaIndex support async execution, which matters for applications that need to run multiple LLM calls or retrievals in parallel. LangChain's LCEL provides built-in support for async and parallel execution through its runnable interface. LlamaIndex supports async query execution across its query engines and retrievers.
Streaming: Both frameworks support streaming LLM responses returning tokens to the user as they are generated rather than waiting for the full response. This significantly improves perceived latency for conversational applications and is well-supported in both libraries.
At scale: For enterprise RAG deployments at scale, LlamaCloud provides managed infrastructure that handles indexing, retrieval, and caching in a way that reduces the operational overhead of self-managing these components. LangGraph Platform provides similar managed infrastructure for agent deployment. The choice between these managed offerings is often as much a commercial decision as a technical one.
When to Choose LangChain
Choose LangChain specifically LangGraph when:
You are building agents with complex workflows. If your application requires an LLM to make sequential decisions, call multiple tools, handle failures gracefully, and potentially involve human review at critical points LangGraph is the mature, production-tested choice.
Your application needs to orchestrate multiple LLMs or AI components. LangChain excels at connecting different AI services a routing LLM that classifies queries, a specialised LLM for specific task types, an embedding model for retrieval, a reranking model for precision into a coherent application.
You need observability from the start. LangSmith's tracing integrates directly with LangChain and LangGraph applications and provides the kind of visibility into what an agent is doing that becomes essential in production.
Your team is building a conversational AI system. LangChain's memory abstractions and conversational chain patterns are well-suited to chatbots and conversational assistants where maintaining context across a conversation is a core requirement.
You need multi-agent coordination. LangGraph's multi-agent graph patterns where a supervisor agent routes tasks to specialised sub-agents are among the most well-documented and practically tested approaches for building this architecture.
For an honest comparison of LangChain alongside the other major agent frameworks, see CrewAI vs AutoGen vs LangGraph Saturncube's breakdown of which agent framework fits which use case.
When to Choose LlamaIndex
Choose LlamaIndex when:
Your primary requirement is RAG quality. If the success of your application depends on accurately retrieving the right information from a large document corpus, LlamaIndex's specialised retrieval machinery will produce better results than a standard retrieval implementation built with LangChain.
You need to connect to many different data sources. If your application needs to ingest data from a combination of PDFs, databases, APIs, cloud storage, and internal tools, LlamaHub's data connector library significantly reduces the ingestion engineering required.
You are building an enterprise knowledge base. LlamaIndex's document hierarchy support, metadata filtering, and citation tracking make it well-suited to enterprise search and knowledge management applications where traceability of retrieved information matters.
You want built-in retrieval evaluation. LlamaIndex includes evaluation utilities for measuring retrieval precision, answer faithfulness, and relevance useful for iterating on RAG pipeline quality without building custom evaluation tooling.
Your team is newer to LLM development and the primary use case is retrieval. LlamaIndex's documentation and abstractions are well-suited to teams learning RAG whose primary challenge is data access rather than complex agent logic.
Can LangChain and LlamaIndex Be Used Together?
Yes and in many production applications, they are.
The most common integration pattern uses LlamaIndex as the data and retrieval layer and LangChain (specifically LangGraph) as the agent and orchestration layer. In this architecture:
This combination leverages the strengths of both frameworks: LlamaIndex's superior retrieval quality and LangGraph's mature agent architecture. It does add integration complexity, but for applications where both retrieval quality and agent capability matter, the combination produces better results than either framework alone.
A practical example: an enterprise customer support agent that needs to answer questions from a large product documentation corpus (LlamaIndex handling retrieval) while also being able to look up order status via an API, escalate tickets to a CRM, and route complex cases to human agents (LangGraph handling the agent workflow).
Practical Use Cases and Examples
Use Case | Recommended Approach |
|---|---|
Simple Q&A over a document set | LlamaIndex alone |
Chatbot with conversation history | LangChain alone |
Enterprise knowledge base with multi-source ingestion | LlamaIndex (with LlamaHub connectors) |
Customer support agent with tool use | LangChain / LangGraph |
Multi-agent workflow with specialised agents | LangGraph |
Sales intelligence tool over CRM + documents | LlamaIndex (retrieval) + LangGraph (agent) |
Internal HR assistant over policies + ticketing system | LlamaIndex (retrieval) + LangGraph (agent) |
Code generation assistant with external context | LangChain with code tools |
Legal document analysis with citation tracking | LlamaIndex |
Real-time data monitoring agent | LangGraph with external tool integrations |
Which Framework Should You Choose?
Choose LlamaIndex if your primary challenge is data access and retrieval quality. If you need to connect an LLM to a large, diverse corpus of documents and return accurate, well-sourced answers, LlamaIndex's retrieval specialisation gives you more capability with less custom engineering.
Choose LangChain (LangGraph) if your primary challenge is agent logic and workflow complexity. If you need an LLM to reason dynamically, use tools, coordinate with other agents, and handle complex multi-step workflows, LangGraph is the more mature and production-tested framework for this.
Use both if you need both. The combination of LlamaIndex for retrieval and LangGraph for agent orchestration is a well-established production pattern and produces better results than forcing either framework to handle a problem it was not optimised for.
For most new projects in 2026, the decision comes down to a single question: is the hard part of your project getting data to the LLM accurately (LlamaIndex), or getting the LLM to do the right things with that data across multiple steps (LangGraph)? The answer to that question points clearly to the right starting framework.
Understanding the full cost and timeline implications of building with these frameworks is also important before committing to a project. The AI agent development cost guide covers what budget to expect for projects at different complexity levels.
Frequently Asked Questions
Is LangChain better than LlamaIndex?
Neither is universally better they are optimised for different problems. LangChain (particularly LangGraph) is stronger for agent development and complex LLM orchestration. LlamaIndex is stronger for RAG pipeline quality and data ingestion from diverse sources. For most production applications, the choice depends on whether your primary challenge is retrieval quality or agent logic.
Can LlamaIndex be used without LangChain?
Yes. LlamaIndex is a complete framework for building RAG applications and does not require LangChain. It includes its own query engines, retrievers, LLM interfaces, and agent capabilities. Many production RAG applications are built with LlamaIndex alone.
Can LangChain be used without LlamaIndex?
Yes. LangChain includes retriever abstractions and vector store integrations that enable RAG pattern implementation without LlamaIndex. For simpler retrieval requirements, LangChain alone is sufficient. For more sophisticated retrieval, adding LlamaIndex's retrieval layer improves quality.
Is LlamaIndex harder to learn than LangChain?
LlamaIndex is generally considered easier to learn for developers whose primary goal is RAG implementation its abstractions are focused and the documentation follows a clear learning path from simple to complex retrieval. LangChain has a broader surface area, which means more to learn initially, though LangGraph's documentation has improved significantly in 2025 and 2026.
Which framework is more popular?
LangChain has historically had a larger community and more GitHub stars, but LlamaIndex has grown significantly. In 2026, both are widely used in production. LangChain is more prevalent in agent-heavy applications; LlamaIndex is more prevalent in enterprise RAG and knowledge management applications.
Do both frameworks support GPT-4o and Claude?
Yes. Both LangChain and LlamaIndex support all major LLM providers including OpenAI (GPT-4o, GPT-4o mini), Anthropic (Claude 3.5 Sonnet, Claude 3 Haiku), Google (Gemini 1.5 Pro, Gemini Flash), Meta (Llama 3), Mistral, and local models through Ollama and similar inference servers.
Which framework is better for enterprise RAG?
LlamaIndex has the stronger offering for enterprise RAG both in the open-source library's retrieval capabilities and in LlamaCloud's managed infrastructure for production deployment. For enterprises that need reliable, high-quality retrieval from large document corpora with built-in evaluation and observability, LlamaIndex is the more complete solution.
Conclusion
LangChain and LlamaIndex are complementary rather than competing frameworks. LangChain specifically LangGraph is the more mature solution for building stateful agents with complex tool use and workflow logic. LlamaIndex is the more sophisticated solution for data ingestion, indexing, and high-quality retrieval.
In practice, the most capable production AI applications in 2026 frequently use both: LlamaIndex as the retrieval and data layer, LangGraph as the agent and orchestration layer. This combination reflects the reality that building useful AI applications requires solving both the data and reasoning problems well.
If you are working out which approach fits your specific use case, or need a team that has built production systems with both frameworks, the Saturncube AI Software Development team works with LangChain, LangGraph, LlamaIndex, and the broader LLM ecosystem across agent, RAG, and custom AI application projects. You can also hire a dedicated AI engineer if you need to extend your team's AI capability without committing to a full project engagement.