LangChain vs LlamaIndex: which AI framework is right for your project?

When to choose LangChain vs LlamaIndex

That's the generic picture. Your use case, data sources, and team experience will tip this one way or the other. ↓

LangChain vs LlamaIndex: RAG pipeline comparison

A RAG pipeline has several distinct stages: document loading, chunking, embedding, vector storage, retrieval, and response synthesis. The quality of your final LLM output depends on how well each stage is executed, poor chunking leads to irrelevant retrieved context, which leads to poor answers regardless of which LLM you use.

LlamaIndex maps directly and deliberately to each stage of this pipeline. SimpleDirectoryReader handles document ingestion from files, URLs, and databases. SentenceSplitter (and other node parsers) control how documents are chunked into retrievable units. VectorStoreIndex manages the embedding and storage layer, connecting to any supported vector database. VectorIndexRetriever fetches the most relevant chunks given a query. ResponseSynthesizer assembles the retrieved context into a coherent prompt and generates the final answer. These purpose-built abstractions mean you're operating at the right level of abstraction for each task, you don't need to wire together lower-level components yourself.

LangChain handles the same pipeline, but with more manual assembly. You'll use a DocumentLoader, then a TextSplitter, store embeddings in a VectorStore, and wire it into a RetrievalQA chain. The pieces are all there, but the connections between them require more configuration. For teams that primarily need RAG, this additional wiring adds friction without adding capability.

Where LlamaIndex really pulls ahead for production RAG is in advanced retrieval techniques. HyDE (Hypothetical Document Embeddings) generates a hypothetical answer to the query before retrieval, improving embedding space alignment and dramatically boosting recall for complex questions. Reranking with Cohere or cross-encoder models adds a second-pass relevance filter that catches retrieval mistakes before they reach the LLM. Hybrid search combines dense vector retrieval with sparse BM25 keyword search, handling both semantic and exact-match queries. Multi-query retrieval generates multiple phrasings of the user's question to improve coverage. These techniques can move RAG accuracy from 60% to 85%+ in production, and LlamaIndex's implementations of them are more mature and better documented than the equivalent LangChain components.

If retrieval quality is your primary concern, the choice here is clear. Answer 5 questions below to confirm whether RAG is really your core challenge or whether agent orchestration is where the complexity lives.

LangChain vs LlamaIndex: building agents in 2025

An AI agent is an LLM that decides which tools to call in a loop, it receives a goal, selects an action (a tool call or direct response), observes the result, and repeats until the goal is satisfied. The classic pattern is ReAct (Reasoning + Acting): the model reasons about what to do, takes an action, observes the output, and continues reasoning. Simple agents can be implemented in either LangChain or LlamaIndex, but production agents with complex conditional logic require a more principled approach.

LangGraph is LangChain's answer to production agents. It models your agent as a directed graph where nodes are LLM calls or tool invocations, and edges represent conditional transitions between states. This graph-based model makes it natural to express complex workflows: loops (retry until success), conditional branching (if tool A fails, try tool B), human-in-the-loop checkpoints (pause and wait for approval before executing an irreversible action), and parallel execution of independent sub-tasks. LangGraph replaced the older AgentExecutor as the recommended pattern in 2024, and it is the most sophisticated open-source agent framework available in 2025.

LlamaIndex offers its own agent capabilities, FunctionCallingAgent for models with native tool-calling APIs and ReActAgent for the classic ReAct loop. These are well-suited for straightforward use cases: a RAG agent that can choose between multiple retrieval strategies, or a data analysis agent that can call a few tools. But LlamaIndex agents lack LangGraph's state management, and building multi-agent systems (an orchestrator that delegates to specialist agents) is significantly harder.

Multi-agent architectures are increasingly common in production AI systems: an orchestrator LLM that breaks down a task and routes subtasks to specialist agents (a research agent, a coding agent, a data retrieval agent). LangGraph has first-class support for this pattern with its multi-actor primitives. LlamaIndex's multi-agent support is more limited and requires more custom code to implement the same patterns.

Practical guidance: if your application has complex conditional logic, loops, and multiple agents coordinating, LangGraph is the best tool available. If your agent is primarily a retrieval agent with a few tool calls on top of a RAG pipeline, LlamaIndex's built-in agents are simpler to implement.

LangChain vs LlamaIndex: observability and production

LLM observability is harder than traditional software monitoring in ways that matter for production. Outputs are non-deterministic, the same input can produce different outputs across runs. Prompt changes have unpredictable effects on downstream quality. Token costs accumulate in ways that are hard to predict from development usage. Latency varies widely based on model provider, prompt length, and output length. Without proper observability tooling, debugging a regression in a production RAG system is genuinely difficult.

LangSmith is LangChain's integrated observability platform. It automatically traces every LLM call, chain execution, tool invocation, and retrieval operation, giving you a complete execution trace for every user query. You can inspect exactly which documents were retrieved, which prompts were sent to the model, and what the model returned at each step. LangSmith also supports evaluation datasets (a set of question/answer pairs you use to measure quality over time), prompt version management, and a playground for testing prompt variations. The tight integration with LangChain means you get this tracing with minimal configuration, typically a few environment variables.

LlamaIndex integrates with LlamaTrace (its own observability product) and with Phoenix by Arize, an open-source LLM observability tool that has become a popular choice in the LlamaIndex community. Phoenix provides trace visualization, RAG-specific evaluation metrics, and support for OpenTelemetry, which means it can also capture traces from non-LlamaIndex components. For teams committed to open-source tooling, Phoenix is a compelling option that works well with both frameworks.

Production RAG systems need evaluation pipelines that measure three things: context relevance (did we retrieve the right documents?), faithfulness (does the answer accurately reflect the retrieved context without hallucination?), and answer relevance (does the answer actually address the user's question?). Both LangSmith and Phoenix provide metrics for these dimensions. Building these evaluations from day one, not as an afterthought, is the single most impactful investment you can make in a production RAG system.

Regardless of which framework you choose, implement LLM observability from day one. The cost of debugging a quality regression without traces far exceeds the cost of setting up tracing upfront. If you're using LangChain, LangSmith is the natural choice. If you're using LlamaIndex, evaluate Phoenix alongside LlamaTrace.

Common questions about LangChain vs LlamaIndex