What Each Framework Actually Does
LangChain and LlamaIndex solve overlapping but distinct problems. They started as peers in 2022, diverged through 2023 and 2024, and have now settled into complementary niches.
- LangChain: A general purpose orchestration framework for LLM applications. Chains, agents, tool calling, memory, and integrations with hundreds of vector stores, APIs, and models. LangGraph, its stateful agent runtime, is now the flagship product for building multi step agents with durable state.
- LlamaIndex: A data framework optimized for indexing and retrieving over unstructured data. Its core primitives (nodes, indexes, query engines, retrievers) are built for RAG from the ground up. LlamaParse handles complex PDFs better than most alternatives, and LlamaCloud offers managed ingestion.
Both frameworks ship Python and TypeScript SDKs. Both integrate with OpenAI, Anthropic, Google, Cohere, and every major vector database. The difference is what they optimize for and how much scaffolding they give you out of the box.
LangChain Strengths in 2026
LangChain v0.3, released in late 2024, cleaned up the package structure that made earlier versions frustrating. The split into langchain-core, langchain, and provider packages like langchain-openai means you can install only what you need.
- Agent orchestration: LangGraph is the clearest win. If you are building an agent that needs to call tools, branch on outputs, persist state across turns, or support human in the loop approvals, LangGraph is the best open source option. It has first class support for checkpointing, interrupts, and streaming.
- Tool calling: LangChain normalizes tool calling across OpenAI, Anthropic, Google, and local models. You write one tool definition and it works across providers. This is underrated until you need to swap models.
- Breadth of integrations: Over 700 integrations. If you need to connect to Snowflake, Notion, Slack, or any obscure API, LangChain probably has it.
- LangSmith: The observability layer. Tracing, eval, prompt management. It is paid, but for teams running agents in production it pays for itself quickly.
Where LangChain struggles is when you try to use it for simple RAG. You end up stitching together loaders, splitters, vector stores, and retrievers manually, and the abstractions leak. For pure retrieval, LlamaIndex is faster to get right.
LlamaIndex Strengths in 2026
LlamaIndex has leaned hard into retrieval and enterprise document workflows. The team shipped LlamaParse, LlamaCloud, and a suite of agent primitives, but the core value is still: get from zero to working RAG faster than anything else.
- Retrieval first design: Indexes, retrievers, and query engines are the primary abstractions. You do not fight the framework to build a RAG pipeline.
- Document handling: LlamaParse handles tables, charts, and complex layouts in PDFs better than PyPDF or Unstructured for most cases. If you are indexing financial filings, research papers, or contracts, this matters.
- Opinionated defaults: LlamaIndex makes more decisions for you. This is a feature for small teams and a bug for teams who want deep control. The defaults are usually good.
- Enterprise adoption: Strong presence in finance, legal, and healthcare where document heavy RAG is the main use case. Companies like KPMG, Salesforce, and Cemex have public LlamaIndex deployments.
LlamaIndex added agent primitives (function calling agents, ReAct agents, workflows) in 2024 and 2025, but they are not as mature as LangGraph for complex multi agent systems.
If your product is a chat interface over a corpus, start with LlamaIndex. If your product is an autonomous agent that uses tools and makes decisions, start with LangChain.
Team Implications
Framework choice is also a team decision. Smaller teams should pick one and stick with it. Context switching between two orchestration frameworks slows everyone down and doubles the surface area for bugs.
- Solo or small team (1-5 engineers): Pick based on your primary use case. If you are building a chatbot over internal docs, LlamaIndex. If you are building an agent that does research or takes actions, LangChain plus LangGraph.
- Mid sized team (5-20 engineers): Still usually one framework, but you can afford to be opinionated about which. Hire engineers with direct experience in your chosen stack.
- Large team (20+ engineers): Often both. A retrieval team uses LlamaIndex for the document indexing service, and an agent team uses LangGraph for the orchestration layer. The two services communicate via APIs, not shared code.
One underrated pattern: use LlamaIndex as a retrieval backend exposed via a REST or gRPC service, and call it from LangChain agents as a tool. This lets each team pick the best tool for their job without forcing everyone onto the same framework.
Cost and Performance Considerations
Both frameworks are open source and free. The cost differences come from what they push you toward.
- Prompt size: LlamaIndex defaults to smaller, more focused retrieval windows. LangChain agents often accumulate large context windows across tool calls. For high volume apps, this shows up in the monthly bill.
- Latency: LangGraph's state machine model adds a small overhead per node, but the durability is usually worth it. LlamaIndex retrievers are typically faster for single shot queries.
- Observability: LangSmith is the best in class for LangChain. LlamaIndex works with Arize, LangFuse, and OpenLLMetry. Budget for observability from day one.
For production workloads, instrument both with OpenTelemetry and track token usage per request. The framework is rarely the bottleneck. The model calls are.
Verdict
Use LlamaIndex when retrieval is the core product. Use LangChain when agents and tool calling are the core product. Use both when you have the team to maintain both. Do not pick based on which has more GitHub stars. Pick based on what you are building.
Key Takeaways
- LlamaIndex wins for RAG over complex documents, especially with LlamaParse for PDFs
- LangChain plus LangGraph wins for multi step agents, tool calling, and stateful workflows
- Small teams should pick one framework. Large teams often run both in separate services
- Both frameworks are production ready in 2026. Pick based on use case, not hype
- Invest in observability (LangSmith, Arize, or LangFuse) from day one regardless of framework
Frequently Asked Questions
Can I use LangChain and LlamaIndex together?
Yes, and many teams do. The common pattern is to use LlamaIndex for document indexing and retrieval, then wrap the LlamaIndex query engine as a LangChain tool that an agent can call. Both frameworks document this integration explicitly.
Is LangGraph a replacement for LangChain?
No. LangGraph is built on top of LangChain and uses its tool calling and model abstractions. Think of LangGraph as the agent runtime and LangChain as the component library it draws from.
Which framework has better TypeScript support?
LangChain.js has more mature TypeScript support, with near parity to the Python version. LlamaIndex.TS exists and is actively developed, but lags the Python version by several months on new features.
How do I evaluate RAG quality in each framework?
LlamaIndex has built in evaluation modules including faithfulness, relevancy, and correctness evaluators. LangChain integrates with RAGAS, TruLens, and LangSmith for evals. Both approaches work. Pick one and run evals on every deployment.
Should I learn both frameworks?
If you are an AI engineer in 2026, yes. Most real world jobs will touch at least one, and the concepts (chains, agents, retrievers, tool calling) transfer. Start with whichever matches your current project.
Hire LangChain and LlamaIndex Talent with South
South places senior AI engineers from Latin America who have shipped production LangChain, LangGraph, and LlamaIndex systems. We vet for real deployment experience, not tutorial knowledge. Start hiring with South.
