LangChain vs LlamaIndex: How to Pick the Right Framework in 2026

If you're weighing LangChain vs LlamaIndex for your next AI project, here's the short version: they solve different problems. LangChain is a general-purpose agent-orchestration framework built for multi-step workflows, tool calling, and stateful pipelines. LlamaIndex is retrieval-first, optimized for document ingestion, indexing, and RAG. Both are MIT-licensed, Python-first, and were created within weeks of each other in late 2022 by engineers who had worked together at Robust Intelligence. In practice, many production teams run both. LlamaIndex handles data ingestion and retrieval; LangChain handles agent orchestration. It's not really an either/or.

This guide breaks down the core architectural differences, walks through setup and code for each, and gives you a practical decision framework for choosing LangChain or LlamaIndex (or both) for your project.

Origins and Ecosystem at a Glance

Both frameworks came out of the same moment in late 2022, when developers first needed tooling to build structured applications on top of LLMs.

LangChain was created by Harrison Chase. Initially released in October 2022, it was incorporated as LangChain Inc. in January 2023. The GitHub tagline: "The agent engineering platform." The 2026 stack has three layers. The LangChain framework provides provider-agnostic interfaces for models, tools, and prompts. LangGraph is a durable stateful agent runtime. LangSmith is closed-source observability. The project has roughly 147K GitHub stars, 1,000+ integrations, and reports over 80 million monthly downloads.

LlamaIndex was created by Jerry Liu. Released in November 2022 as "GPT Tree Index," it was rebranded upon incorporation in April 2023. The GitHub tagline: "the document processing platform for AI." Core primitives include Readers (data loaders), Documents/Nodes, Indexes, Retrievers, Query Engines, and event-driven Workflows for orchestration. Roughly 52K GitHub stars, 300+ integration packages, and the commercial product LlamaParse handles document parsing at scale.

| | LangChain | LlamaIndex | |---|---|---| | Focus | Agent orchestration, tool calling, workflows | Data ingestion, indexing, RAG | | GitHub stars | ~147K | ~52K | | Integrations | 1,000+ | 300+ integration packages | | Language SDKs | Python, TypeScript | Python (TS archived April 2026) | | Commercial product | LangSmith (observability) | LlamaCloud / LlamaParse | | License | MIT | MIT | | Total funding | ~$260M | ~$28M |

LangChain vs LlamaIndex: Core Architecture Differences

The fundamental split comes down to what each framework treats as the primary unit of work.

LangChain: Agents and Orchestration

LangChain models everything as a pipeline of composable steps. You define tools, wire them to a model, and let an agent decide which tools to call and in what order. LangChain v1.0 (released October 2025) introduced the create_agent abstraction, middleware for hooking into agent/tool/LLM interactions, and standard content_blocks for provider-agnostic responses. LangGraph handles the stateful runtime: conditional branching, cycles, checkpointing, and dynamic tool calling.

Pick LangChain when your application needs to take sequential actions across multiple external tools, manage complex conversational state, or orchestrate multi-agent workflows. According to LangChain's State of Agent Engineering report, the majority of production agents now use some form of graph structure (DAG or state machine) rather than simple linear chains.

LlamaIndex: Data and Retrieval

LlamaIndex treats your data as the primary concern. It ships with built-in hierarchical chunking, auto-merging retrieval, sub-question decomposition, and multiple index types (vector, tree, keyword, knowledge graph). The default RAG pipeline takes roughly 30-40% less code than an equivalent LangChain setup, and LlamaIndex's sentence-level retrieval approaches tend to achieve higher recall than LangChain's default RecursiveCharacterTextSplitter in head-to-head benchmarks. Results depend heavily on chunk size configuration, though.

Pick LlamaIndex when your primary problem is getting accurate answers from private documents: internal knowledge bases, PDF archives, structured databases, or multi-source corpora.

Getting Started: Setup and Code

Installing LangChain

LangChain requires Python 3.10+ and installs provider integrations as separate packages:

pip install -U langchain langchain-anthropic
# or with uv
uv add langchain langchain-anthropic

A minimal agent with LangChain v1.0:

from langchain.chat_models import init_chat_model
from langchain.agents import create_agent
from langchain.tools import tool

@tool
def search_docs(query: str) -> str:
    """Search internal documentation."""
    # your retrieval logic here
    return results

model = init_chat_model("anthropic:claude-sonnet-4-20250514")
agent = create_agent(
    model=model,
    tools=[search_docs],
    system_prompt="You are a helpful research assistant."
)
response = agent.invoke("What is our refund policy?")

Installing LlamaIndex

LlamaIndex installs as a starter bundle or individual packages:

# Starter bundle (includes OpenAI defaults)
pip install llama-index

# Local-only stack (no API key needed)
pip install llama-index-core llama-index-readers-file \
  llama-index-llms-ollama llama-index-embeddings-huggingface

A working RAG pipeline in five lines:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

documents = SimpleDirectoryReader("./data").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine()
response = query_engine.query("What is our refund policy?")

To persist the index and reload it later:

# Save
index.storage_context.persist("./storage")

# Load
from llama_index.core import StorageContext, load_index_from_storage
storage_context = StorageContext.from_defaults(persist_dir="./storage")
index = load_index_from_storage(storage_context)

Running LlamaIndex Fully Local

If you need a private, air-gapped stack with no external API calls:

from llama_index.core import Settings
from llama_index.llms.ollama import Ollama
from llama_index.embeddings.huggingface import HuggingFaceEmbedding

Settings.llm = Ollama(model="llama3.1", request_timeout=360.0)
Settings.embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")

Note: LlamaIndex replaced ServiceContext with the global Settings singleton in v0.10. Older tutorials using ServiceContext.from_defaults() are outdated.

When to Use Each: A Decision Framework

Pick LangChain (with LangGraph) when:

• Your agent needs to call multiple external APIs in sequence with conditional logic
• You need stateful multi-turn conversations with checkpointing and resumption
• You're building multi-agent systems where agents delegate to sub-agents
• You need production middleware (PII redaction, content filtering, human-in-the-loop approvals)

Pick LlamaIndex when:

• Your primary problem is retrieving accurate answers from documents (PDFs, databases, wikis)
• You need advanced chunking strategies (hierarchical, sentence-window, auto-merging)
• You want the shortest path from raw documents to a queryable interface
• You need to parse complex document formats (via LlamaParse)

Use both together when:

• You need high-quality RAG and multi-step agent orchestration
• Your architecture has a data layer (LlamaIndex indexes and retrieves) feeding an agent layer (LangGraph orchestrates actions based on retrieved context)

This hybrid pattern shows up a lot in production. Jeppesen (Boeing) reported saving approximately 2,000 engineering hours using LlamaIndex's event-driven workflows for document processing, while other teams layer LangGraph on top for orchestration.

Consider skipping both when:

• You have a single LLM call or a tightly scoped chatbot
• Direct provider SDKs (OpenAI, Anthropic) plus a vector store like pgvector cover your needs
• You want fewer dependencies and simpler debugging

Common Pitfalls

A few things to watch for with either framework.

• LangChain abstraction overhead. A "simple" RAG chain can quietly fire 3-5 LLM calls per user query. Monitor token usage from day one. LangSmith tracing helps, but adds another dependency.
• LlamaIndex breaking changes. The rapid release cadence means version pinning is essential. Pin your llama-index-core version in production.
• Both frameworks leave deployment unsolved. Neither handles persistent hosting, scaling, or infrastructure. You still need a deployment layer on top.

Beyond the Framework: The Deployment Layer

The LangChain vs LlamaIndex debate usually stops at framework selection. But production teams quickly discover that neither framework handles the infrastructure side: persistent agent hosting, scaling across concurrent users, managing 190+ tool integrations, or running agents that stay alive between sessions.

That's where a deployment platform fits in. Gamut works as the orchestration and deployment layer for agents built on either framework (LangChain, LlamaIndex, CrewAI, or others). It provides persistent AI agents with access to 190+ MCP integrations out of the box, so the framework you pick for your agent logic is decoupled from how you host and scale it. If you're exploring LangChain alternatives or evaluating multi-agent patterns like CrewAI vs LangGraph, the deployment question stays the same regardless.

FAQ

Which is better, LangChain or LlamaIndex?

Neither is universally better. LangChain excels at agent orchestration and multi-step workflows. LlamaIndex excels at document retrieval and RAG. Your choice depends on whether your primary challenge is acting on information (LangChain) or finding information (LlamaIndex). Many teams use both.

Can you use LangChain and LlamaIndex together?

Yes. The most common hybrid pattern uses LlamaIndex for data ingestion and retrieval (indexing documents, building query engines) and LangChain/LangGraph for agent orchestration (deciding which tools to call, managing state, handling multi-step workflows). The two frameworks have no conflicts at the dependency level.

Is LangChain harder to learn than LlamaIndex?

LlamaIndex has a lower barrier to entry for RAG. A working pipeline is five lines of code. LangChain's abstraction layers (chains, agents, LangGraph nodes) require more upfront learning. That said, LlamaIndex's complexity increases significantly once you move beyond basic vector-store RAG into custom workflows and agents.

What is the difference between LangChain and LangGraph?

LangChain is the core framework providing model interfaces, tools, prompts, and the create_agent abstraction. LangGraph is the stateful runtime layer that handles durable execution: conditional branching, cycles, checkpointing, and dynamic tool calling. LangGraph shipped its 1.0 release alongside LangChain 1.0 in October 2025.

How do LangChain and LlamaIndex handle MCP?

Both frameworks support the Model Context Protocol. LangChain has langchain.mcp for connecting to MCP servers as tool providers. LlamaIndex wraps MCP servers via its McpToolSpec and FunctionAgent interface. MCP standardizes tool integration across frameworks, which reduces the importance of framework-specific connector counts as a differentiator.