LangChain vs LlamaIndex - LLM Framework Comparison

LangChain and LlamaIndex are the two most popular frameworks for building LLM-powered applications. Despite frequent comparison, they solve different primary problems: LangChain is a general-purpose LLM application framework, while LlamaIndex is specialized for data retrieval and RAG. Understanding this distinction prevents choosing the wrong tool.

Both frameworks reached major milestones recently. LangChain and LangGraph hit their stable 1.0 releases on October 22, 2025, with a public commitment to no breaking changes until 2.0. LlamaIndex raised a Series A and brought its managed LlamaCloud platform (including LlamaParse) to general availability in March 2025. Both have shifted their centre of gravity toward agentic, stateful workflows rather than the simpler chain and query-engine patterns they were originally known for.

Core Focus

LangChain is a general framework for building applications with LLMs. It provides abstractions for chains (sequences of LLM calls), agents (LLMs that decide which tools to use), memory (conversation state), and integrations with hundreds of services. It is a toolkit for any LLM application pattern.

LlamaIndex is focused on connecting LLMs with data. It provides abstractions for data ingestion, indexing, retrieval, and query engines. It excels at turning your data into a format that LLMs can reason over effectively.

Architecture Comparison

Data Handling

LlamaIndex has a clear advantage for data-centric applications:

Ingestion. LlamaIndex supports hundreds of data connectors through LlamaHub, covering databases, APIs, file formats, and SaaS applications. For complex documents, LlamaParse (the parsing engine behind the managed LlamaCloud platform, generally available since March 2025) extracts tables, charts, and images from PDFs, Office files, and many other formats. LlamaParse v2 simplified its configuration into a small set of tiers (Fast, Cost Effective, Agentic, Agentic Plus) with automatic model routing. LangChain has document loaders but fewer specialized connectors.

Indexing. LlamaIndex provides multiple index types (vector, keyword, tree, knowledge graph) with automatic optimization. LangChain relies on external vector stores without the same indexing abstraction layer.

Advanced retrieval. LlamaIndex offers sophisticated retrieval strategies: recursive retrieval, multi-document agents, sentence-window retrieval, auto-merging retrieval, and hybrid search. LangChain’s retrieval is functional but less specialized.

Query planning. LlamaIndex can decompose complex queries into sub-queries, route them to appropriate indices, and synthesize results. This is built into the framework rather than requiring custom chain logic.

Workflows. LlamaIndex now centres orchestration on Workflows, an event-driven, async-first system for composing multi-step processes with loops, branches, and parallel paths. This has narrowed LangChain’s historical lead on complex application logic, so the gap on agentic workflows is smaller than it once was.

Agent and Chain Support

LangChain has a clear advantage for complex application logic:

Chain composition. LangChain Expression Language (LCEL) provides a clean way to compose workflows from simple building blocks using piping, branching, and parallel execution. As of the 1.0 release, agent construction has become the primary pattern, and much of the older chain-centric and legacy retriever code has moved into a separate langchain-classic package, while the core langchain package focuses on agents, models, messages, and tools.

Agent frameworks. In LangChain 1.0, the idiomatic way to build an agent is the create_agent function, which runs on the LangGraph runtime and exposes a middleware system for fine-grained control of the agent loop (with built-in middleware for human-in-the-loop, summarization, and PII redaction). LangGraph 1.0 underpins this with stateful, multi-actor workflows. The legacy AgentExecutor approach is deprecated.

Tool ecosystem. LangChain integrates with hundreds of tools (APIs, databases, search engines, calculators) that agents can use. The tool abstraction is mature and well-documented.

Memory systems. Multiple conversation memory types (buffer, summary, entity, knowledge graph) for maintaining state across interactions.

Developer Experience

LangChain has a steeper learning curve due to its breadth. The framework has many abstractions, and knowing which to use for a given problem requires experience. Documentation is extensive but can be overwhelming. Historically the ecosystem moved fast with frequent breaking changes, though the 1.0 release adopted semantic versioning and pledged no breaking changes until 2.0, which should make the moving target more stable.

LlamaIndex has a gentler learning curve for RAG use cases. The high-level API gets you from data to working RAG system quickly. For advanced customization, the low-level API is available. Documentation is focused and practical.

Both frameworks provide:

• Python and TypeScript/JavaScript implementations
• Integration with major LLM providers
• Active community and regular updates
• Observability and debugging tools (LangSmith for LangChain, various integrations for LlamaIndex)

Performance Considerations

Overhead. Both frameworks add abstraction overhead compared to direct API calls. For latency-sensitive applications, benchmark the framework overhead.

Token efficiency. LlamaIndex’s retrieval optimizations (sentence-window, auto-merging) can reduce token usage compared to naive RAG. LangChain provides similar capabilities but requires more manual configuration.

When to Choose LangChain

• Building complex multi-step LLM workflows
• Need agent capabilities with tool integration
• Building conversational applications with sophisticated memory
• The application involves more than just data retrieval
• Need LangGraph for stateful, multi-actor workflows

When to Choose LlamaIndex

• Building a RAG system over your data
• Data ingestion from diverse sources is a primary requirement
• Need advanced retrieval strategies out of the box
• The core problem is connecting LLMs to structured or unstructured data
• Want to get a working retrieval system quickly

Using Both Together

LangChain and LlamaIndex are not mutually exclusive. A common pattern is using LlamaIndex as the retrieval layer within a LangChain application: LlamaIndex handles data ingestion, indexing, and retrieval, while LangChain manages the overall application workflow, agents, and memory. LlamaIndex provides a LangChain-compatible retriever that plugs directly into LangChain chains.

The Alternative: Build Without a Framework

For simple applications (single LLM call, basic RAG), consider using the LLM provider’s SDK directly. Frameworks add value when you need their abstractions; for straightforward use cases, they add complexity without proportional benefit. Start simple and introduce a framework when the complexity warrants it.

See Also

• RAG (glossary) , Agentic RAG , Vector Database
• Tool Use , Function Calling , Model Context Protocol
• LangChain vs DSPy , CrewAI vs LangGraph
• RAG vs Fine-Tuning

Sources and Further Reading

• Lewis, P., Perez, E., Piktus, A., et al. (2020). Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks. NeurIPS 2020. arXiv:2005.11401. https://arxiv.org/abs/2005.11401
• Yao, S., Zhao, J., Yu, D., et al. (2023). ReAct: Synergizing Reasoning and Acting in Language Models. ICLR 2023. arXiv:2210.03629. https://arxiv.org/abs/2210.03629
• Khattab, O., Singhvi, A., Maheshwari, P., et al. (2024). DSPy: Compiling Declarative Language Model Calls into State-of-the-Art Pipelines. ICLR 2024. arXiv:2310.03714. https://arxiv.org/abs/2310.03714
• Gao, Y., Xiong, Y., Gao, X., et al. (2023). Retrieval-Augmented Generation for Large Language Models: A Survey. arXiv:2312.10997. https://arxiv.org/abs/2312.10997
• LangChain documentation. https://python.langchain.com/docs/introduction/
• LangChain. LangChain and LangGraph Agent Frameworks Reach v1.0 Milestones. (October 22, 2025). https://blog.langchain.com/langchain-langgraph-1dot0/
• LangChain. Migrate to LangChain v1. https://docs.langchain.com/oss/python/migrate/langchain-v1
• LangGraph documentation. https://langchain-ai.github.io/langgraph/
• LlamaIndex documentation. https://docs.llamaindex.ai/
• LlamaIndex. Series A Funding and LlamaCloud General Availability. (March 4, 2025). https://www.llamaindex.ai/blog/announcing-our-series-a-and-llamacloud-general-availability
• AWS. Amazon Bedrock Knowledge Bases. https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html