LangChain vs LlamaIndex: The Short Answer

Both are open-source Python and TypeScript frameworks for building with LLMs. For years, the simple rule was "LangChain for agents and chains, LlamaIndex for RAG over your data." That's still a decent starting point, but both have grown into each other's space.

In October 2025, LangChain and LangGraph both reached 1.0, reshaping LangChain around a single agent abstraction built on LangGraph. LlamaIndex, meanwhile, added Workflows for multi-step agents. Here's how they compare now.

🎯 Quick Answer (30-Second Read)

  • LangChain 1.0 centres on create_agent() plus middleware, running on LangGraph underneath. Older APIs like AgentExecutor are legacy.
  • LangGraph is the low-level runtime for stateful, graph-based agents with persistence and human-in-the-loop.
  • LlamaIndex shines at data ingestion, indexing and retrieval (RAG), with an event-driven Workflows system for agents.
  • Common combo: LlamaIndex for retrieval, LangGraph for orchestration.
  • Often the right answer is neither: for simple apps, the model provider's SDK plus a few functions is easier to debug.

What Each One Is For

LangChain + LangGraph
Agents that call tools, multi-step workflows with state, branching, retries, human approval and memory. Huge integration catalogue.
LlamaIndex
Loading documents (PDFs, Notion, databases), chunking, indexing, advanced retrieval and query engines, plus Workflows for agentic RAG.
flowchart LR subgraph LI["LlamaIndex strengths"] L1["Loaders
PDF, web, DBs"] --> L2["Chunking +
embeddings"] --> L3["Index +
retrievers"] --> L4["Query engine"] end subgraph LG["LangChain / LangGraph strengths"] G1["create_agent
+ middleware"] --> G2["Tools
(APIs, search, code)"] G1 --> G3["State, memory,
human-in-the-loop"] end L4 -->|"used as a retrieval tool"| G2

Code: A Simple Agent (LangChain 1.0)

from langchain.agents import create_agent

def get_weather(city: str) -> str:
    """Get the current weather for a city."""
    return f"It's 31°C and sunny in {city}."

agent = create_agent(
    model="anthropic:claude-sonnet-5-5",
    tools=[get_weather],
    system_prompt="You are a helpful travel assistant.",
)

result = agent.invoke({"messages": [{"role": "user", "content": "Weather in Pune?"}]})
print(result["messages"][-1].content)

Code: RAG Over Your Documents (LlamaIndex)

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader

docs = SimpleDirectoryReader("./company-docs").load_data()
index = VectorStoreIndex.from_documents(docs)        # chunk + embed + index
engine = index.as_query_engine()

print(engine.query("What is our refund policy?"))

Both libraries need provider packages and API keys configured; check each project's quickstart for the current setup.

Head-to-Head

LangChain / LangGraph LlamaIndex
Core strength Agents, orchestration, tool calling Data ingestion, indexing, retrieval
Agent model Graph of nodes with typed state (LangGraph) Event-driven async Workflows
RAG tools Good, many integrations Deeper: advanced retrievers, query engines
Integrations Very large catalogue Large, especially data connectors (LlamaHub)
Learning curve Simpler since 1.0 (create_agent) Easy for basic RAG, deeper for advanced
Commercial add-ons LangSmith (tracing, evals) LlamaCloud (parsing, managed indexes)

Which Should You Choose?

flowchart TD S{"Main job?"} -->|"Answer questions
over documents"| R{"Complex parsing /
retrieval needs?"} S -->|"Agent that uses tools,
multi-step workflows"| A["LangChain create_agent
→ LangGraph for control"] S -->|"Both"| B["LlamaIndex retrieval
+ LangGraph orchestration"] R -->|Yes| LI["LlamaIndex"] R -->|"No, simple RAG"| N["Either, or just the
model SDK + vector DB"]
Watch out: many tutorials online predate LangChain 1.0. If you see AgentExecutor, initialize_agent or old chain classes, the code is outdated. Check the version a tutorial uses before copying it.

When to Skip Frameworks

Frameworks add abstraction layers. For a chatbot with a few tools, the Anthropic or OpenAI SDK with tool calling is often clearer and easier to debug. Reach for a framework when you need what it provides: many integrations, durable state, complex retrieval, or tracing and evaluation tooling.

Frequently Asked Questions

Is LlamaIndex better than LangChain for RAG?

LlamaIndex is generally considered stronger for complex retrieval: document parsing, many index types and advanced query engines. LangChain handles standard RAG well too. Many teams use LlamaIndex for retrieval and LangChain or LangGraph for orchestrating agents.

What changed in LangChain 1.0?

LangChain 1.0, released in October 2025 alongside LangGraph 1.0, centres on a create_agent function with middleware for customisation, built on the LangGraph runtime. Older agent APIs such as AgentExecutor became legacy.

Can I use LangChain and LlamaIndex together?

Yes. A common pattern uses LlamaIndex to load, index and retrieve documents, exposing retrieval as a tool, while LangChain or LangGraph runs the agent that decides when to call it alongside other tools.

Conclusion

Choose LangChain (with LangGraph) when your main challenge is orchestrating agents, tools and state. Choose LlamaIndex when your main challenge is getting the right information out of your documents. Combine them for agentic RAG, and skip both when a plain SDK does the job.

Sources

Last verified: 2 November 2026.

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