LangChain vs LlamaIndex: When to Use Each One

Two Frameworks, Different Purposes

LangChain and LlamaIndex are the two most popular AI frameworks in the Python ecosystem — and they're frequently treated as drop-in replacements for each other. In practice, they solve different problems and work better together than in competition.

LangChain was built for LLM pipeline orchestration: chains, agents, tools, memory, and integration with dozens of services. LlamaIndex was built for document indexing and retrieval — RAG (Retrieval-Augmented Generation) is its core specialty.

Understanding this fundamental difference saves weeks of trial and error.

LangChain: Orchestrating LLM Workflows

LangChain shines when you need to coordinate multiple steps, tools, and models in a complex workflow.

When LangChain excels:

from langchain.agents import create_react_agent, AgentExecutor
from langchain.tools import DuckDuckGoSearchRun, PythonREPLTool
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o", temperature=0)
tools = [DuckDuckGoSearchRun(), PythonREPLTool()]

agent = create_react_agent(llm, tools, prompt)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)

result = executor.invoke({"input": "Search for the current Bitcoin price and calculate how much I'd have today if I had invested $1000 in 2020"})

In this example, the agent autonomously decides when to search the web and when to execute Python code. LangChain manages the reasoning-action-observation loop (ReAct) automatically.

LangChain strengths:

• Agents with access to external tools (web, databases, APIs)
• Complex chains with multiple chained LLMs
• Conversation memory (buffer, summary, entity)
• Integration with 300+ providers (OpenAI, Anthropic, HuggingFace, etc.)
• LCEL (LangChain Expression Language) for declarative pipelines

Limitations:

• Heavy abstraction — hard to debug when something goes wrong
• Docs don't always keep up with the library's pace of change
• Overhead for simple use cases (basic RAG doesn't need LangChain)

LlamaIndex: Structured Information Retrieval

LlamaIndex was built for a specific problem: how to make an LLM answer questions about a document base accurately and in a controlled manner.

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter

# Load and index documents
documents = SimpleDirectoryReader("contracts/").load_data()
parser = SentenceSplitter(chunk_size=512, chunk_overlap=64)
nodes = parser.get_nodes_from_documents(documents)

index = VectorStoreIndex(nodes)
query_engine = index.as_query_engine(similarity_top_k=5)

response = query_engine.query("What are the termination clauses in the contract with Company X?")
print(response)
print(response.source_nodes)  # Shows the passages that generated the answer

LlamaIndex automatically handles: chunking, embedding, vector storage, similarity retrieval, and response synthesis.

LlamaIndex strengths:

• RAG with granular control over chunking and retrieval
• Source traceability (source nodes)
• Native support for complex structures (tables, PDFs, HTML)
• Sub-question decomposition for complex queries
• Integrations with vector stores (Pinecone, Weaviate, pgvector, Chroma)

Limitations:

• Less flexible for pipelines that go beyond RAG
• Agents are less mature than LangChain's
• Smaller ecosystem of integrations with external tools

Decision Matrix

Use Case	LangChain	LlamaIndex	Both
Chatbot over internal documents	—	✓	—
Agent with internet access	✓	—	—
RAG pipeline with chunking control	—	✓	—
Multi-step workflow with external APIs	✓	—	—
Synthesis of multiple documents	—	✓	—
Agent that researches then queries a doc base	—	—	✓
Contract and legal document analysis	—	✓	—
Internal process automation	✓	—	—

Using LangChain and LlamaIndex Together

The most powerful combination is using LlamaIndex for indexing and retrieval, and LangChain for agent orchestration:

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from langchain_community.retrievers import LlamaIndexRetriever
from langchain.agents import create_react_agent

# LlamaIndex handles RAG
documents = SimpleDirectoryReader("knowledge-base/").load_data()
index = VectorStoreIndex.from_documents(documents)
retriever = LlamaIndexRetriever(index=index, query_kwargs={"similarity_top_k": 5})

# LangChain handles agent orchestration
from langchain.tools import Tool
knowledge_base_tool = Tool(
    name="company_knowledge_base",
    func=lambda q: retriever.get_relevant_documents(q),
    description="Search the company's internal knowledge base"
)

# Agent with access to knowledge base + internet
tools = [knowledge_base_tool, DuckDuckGoSearchRun()]
agent = create_react_agent(llm, tools, prompt)

Conclusion

Choose LangChain when your system needs agents that make decisions and use diverse tools. Choose LlamaIndex when your main problem is making an LLM answer questions about documents with accuracy and traceability. Use both when you need agents that also query a structured document base.

SystemForge has hands-on experience with both frameworks in AI automation projects. If you want to understand which approach makes more sense for your use case, reach out to our team.