LangChain vs LlamaIndex: Which Agent Framework to Use?

Paradigm

LangChain is a general-purpose orchestration layer — AgentExecutor, @tool, ConversationBufferMemory, and OutputParser are designed to be swappable across providers and use cases. LlamaIndex starts from data: VectorStoreIndex is the primitive, and ReActAgent + QueryEngineTool exist to let an agent reason over that index.

LangChain's loop is provider-agnostic plumbing. LlamaIndex's loop is built around retrieval as a first-class tool call.

Ecosystem

LangChain ships the larger catalog: dozens of LLM providers, document loaders, vector stores, plus LangSmith for tracing and LangServe for deployment. It has Sequoia/Benchmark backing and ~3.4M weekly npm downloads.

LlamaIndex is narrower but deeper on data — LlamaHub connectors, document parsers (PDF, HTML, SQL), and pluggable vector stores (Pinecone, Weaviate, pgvector). If you measure ecosystem by integrations-per-domain, LangChain wins on breadth, LlamaIndex on retrieval depth.

Use case

If your agent juggles APIs, databases, and multiple LLM providers, LangChain's AgentExecutor + integration catalog is the better fit — and LangGraph handles branching workflows across nodes.

If your agent's job is to reason over your documents, LlamaIndex's index-as-tool pattern is purpose-built: one QueryEngineTool line and the agent can query a collection alongside any other FunctionTool. Picking the wrong one means fighting the framework's center of gravity.

Pick langchain if your project lives or dies on the breadth of integrations and orchestration tooling around the agent.

• Multi-provider, multi-integration surface: You're swapping between OpenAI and Anthropic, plugging into 3+ vector stores, or composing document loaders with custom chains. The class hierarchy pays off when integration count is high.
• Production observability matters: You want LangSmith traces, eval runs, and prompt versioning without building it yourself.
• Branching workflows beyond a single loop: LangGraph state channels, conditional edges, and parallel nodes are worth the abstraction tax for genuinely multi-step pipelines.

Pick llamaindex if your agent's core job is reasoning over your data, not orchestrating arbitrary tools.

• Retrieval is the product: Multiple document collections, varied retrieval strategies, re-ranking — VectorStoreIndex + QueryEngineTool turns each index into a callable tool in one line.
• Heavy document ingestion: PDFs, HTML, SQL, Notion, Slack — LlamaHub connectors and parsers save weeks of glue code over hand-rolled pipelines.
• Index-aware agent reasoning: You want ReActAgent to decide which index to query, not just whether to retrieve. The framework treats data sources as peer tools, which is hard to replicate cleanly by hand.

Both frameworks pull in large dependency trees and put a class hierarchy between you and the actual /chat/completions call. AgentExecutor, AgentRunner, FunctionTool, @tool — each is a layer to learn, debug through, and keep pinned across version bumps. Breaking changes in either framework have shipped quarterly.

Ramp-up is real: new engineers learn the framework's vocabulary before they learn your agent. If your loop is a while with three tools and one provider, that overhead buys you very little — and shows up every time you read a stack trace at 2 AM.