LlamaIndex vs LangChain

LlamaIndex vs LangChain: Which Should You Pick?

Pick LlamaIndex for a document-first application. Pick LangChain for an agent that coordinates work across tools. Both can build retrieval-augmented generation, or RAG: retrieving relevant source material before asking a model to answer.

For a founder building a support assistant over product manuals, LlamaIndex is the better starting point. Its document, index and query-engine abstractions put the ingestion-to-answer path at the centre of the code. Start there when your next engineering question is about missing passages, document structure or which evidence reaches the model.

For a developer building an assistant that reads a policy, checks an order and requests approval before issuing a refund, LangChain is the better starting point. Its current agent API makes tools and the model loop central, with LangGraph providing the underlying execution framework. Start there when your next question is about state, approval or which action happens next.

If your existing application already meets its retrieval and workflow requirements, keep it. A different framework name does not repair a badly parsed table or an unsafe payment operation. Add the missing component only after you can name the failure it addresses.

The criteria here are document handling, control over agent execution, integration boundaries and ongoing cost. These are documentation-based judgments, not measured speed or accuracy rankings.

Sources: LlamaIndex documentation, LangChain documentation, the LlamaIndex package and LangChain package, and each maker's LlamaParse pricing and LangSmith pricing. The hosted services in the price row perform different jobs; they are not equivalent subscriptions.

What Each Framework Is for Today

LlamaIndex is more than an index, and LangChain is more than a collection of chains. Their current documentation puts agents in both ecosystems. The distinction is where each gives you the most useful starting structure.

LlamaIndex: Data Pipelines and Document Agents

LlamaIndex is an open-source framework for agents that use your data. Its building blocks cover loading documents, creating searchable representations, retrieving evidence and generating answers. Its Workflows also support event-driven processes with branching, retries and human review. The maker's framework overview describes this broader scope explicitly.

For a contract assistant, the useful unit is often the document passage: where it came from, which version it belongs to, and whether its surrounding section changes its meaning. LlamaIndex's Node, a chunk of source content with metadata and relationships, gives you a place to manage that evidence. A query engine then combines retrieval with answer generation. See its VectorStoreIndex guide.

Its wall is the quality of the material you feed it. A loader that flattens a table can detach a price from its column heading; indexing that text will faithfully preserve the mistake. LlamaIndex's own docs distinguish built-in readers for clean text from the commercial LlamaParse service for harder documents. Choose the framework for its data abstractions, then evaluate parsing separately.

There is also a maintenance consideration: the maker's current package description says its primary focus has shifted toward LlamaParse and related document tooling, while the framework remains available as an open toolkit. For a long-lived dependency, check the maintenance of the particular integrations you need before committing to them.

LangChain: Agents with LangGraph Underneath

LangChain is an open-source framework whose current create_agent API supplies a configurable agent harness: the tools, instructions and surrounding execution logic that let a model perform work. Middleware lets you customize behavior around that loop. Its overview places LangGraph underneath these agents.

For an order-support agent, retrieval might be one tool among several. The application may also look up shipment status and prepare a proposed refund. LangChain is the natural starting point when those tool interactions define the product.

Its wall is that an agent abstraction cannot decide your business invariants. You still define who may receive a refund, what approval is required and how to prevent a repeated execution from issuing it twice. The framework gives you execution mechanisms; the application owns the rules.

LangChain vs LangGraph

Use LangChain's agent abstraction when a model choosing tools fits the job. Use LangGraph directly when you need to define the workflow's state and transitions more explicitly. LangGraph is already part of current LangChain agents, so adding memory or human review does not automatically require a rewrite. The separate LangChain vs LangGraph comparison covers that decision.

Current Python Versions and Licences

Verified on 13 October 2026: the published Python packages were llama-index 0.14.25, llama-index-core 0.14.25, langchain 1.4.5 and langgraph 1.2.15. These are package-specific snapshots, not a single version shared by every integration in an ecosystem.

Both framework repositories carry the MIT licence: LlamaIndex's licence and LangChain's licence. The hosted products have separate commercial terms. A free framework licence does not include model inference, a hosted vector database or your application server.

The Same Small RAG Job in Both

Both examples load local text files, build an in-memory index and answer the same question using retrieved material. They illustrate the API shape; they were checked against the documentation, not executed against a paid model API in this comparison.

Create a data directory containing your own UTF-8 .txt policy files. Set OPENAI_API_KEY, then set RAG_MODEL to the same OpenAI chat model available to your account for both scripts. Both explicitly select text-embedding-3-small for embeddings, the numerical representations used to search for similar content. The LlamaIndex starter tutorial recommends making that embedding choice explicit.

The LlamaIndex Sketch

Install llama-index-core, llama-index-readers-file, llama-index-llms-openai and llama-index-embeddings-openai. Alternatively, the llama-index starter bundle includes those components; see the installation guide.

import os
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI

docs = SimpleDirectoryReader("data", required_exts=[".txt"]).load_data()
embed = OpenAIEmbedding(model="text-embedding-3-small")
index = VectorStoreIndex.from_documents(docs, embed_model=embed)
llm = OpenAI(model=os.environ["RAG_MODEL"])
engine = index.as_query_engine(llm=llm, similarity_top_k=3)
print(engine.query("What is the refund window?"))

Here, from_documents handles the document-to-node transformation and indexing. The query engine handles retrieval and answer synthesis. That is the advantage for a small knowledge assistant: the code expresses the document job directly, while lower-level components remain available when you need to change it.

The LangChain Sketch

Install langchain-core, langchain-openai and langchain-text-splitters. This fixed retrieval flow uses LangChain's component packages; the top-level langchain agent package is unnecessary until you add an agent. The APIs are documented in the RAG guide, in-memory store reference and OpenAI integration.

import os
from pathlib import Path
from langchain_core.documents import Document
from langchain_core.vectorstores import InMemoryVectorStore
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
files = Path("data").glob("*.txt")
docs = [Document(page_content=p.read_text(encoding="utf-8"), metadata={"source": str(p)}) for p in files]
chunks = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100).split_documents(docs)
index = InMemoryVectorStore.from_documents(chunks, OpenAIEmbeddings(model="text-embedding-3-small"))
question = "What is the refund window?"
context = "\n\n".join(d.page_content for d in index.similarity_search(question, k=3))
llm = ChatOpenAI(model=os.environ["RAG_MODEL"])
print(llm.invoke(f"Answer only from this context; if absent, say unknown.\n{context}\nQuestion: {question}").content)

This version exposes the steps: load text, split it, embed and store it, retrieve passages, then call the chat model. That extra wiring becomes useful when retrieval is only one part of a larger application.

Retrieval and Agent Control: Who Wins Each Job?

LlamaIndex wins the starting abstraction for document retrieval. LangChain wins the starting abstraction for an agent coordinating actions. Those are engineering-fit verdicts, not claims that either framework is incapable of the other's work.

Document Ingestion and Retrieval: LlamaIndex Wins

Choose LlamaIndex when most changes will happen between the raw document and the evidence returned to the model. Its indexing and query-engine structure keeps that work together. For example, a procurement assistant answering questions across contract amendments needs to preserve source relationships before it needs another tool-calling loop.

LangChain can build the same retrieval flow. Its RAG documentation separates indexing from retrieval and generation, and the snippet above shows the straightforward case. If you already have a good search service, calling it from LangChain may be less work than introducing another framework.

The decision flips when retrieval stops being the difficult subsystem. A clean knowledge base plus several business tools favours investing in the agent workflow instead.

Tool-Using Agents and Workflow Control: LangChain Wins

Choose LangChain when the application alternates between evidence and actions: read a policy, fetch a customer record, ask for missing information, then prepare an action for approval. Its configurable agent loop and LangGraph foundation fit that structure.

LlamaIndex also supports agents. The maker's agent guide documents tool-using agents, while its starter tutorial adds retrieval as a callable tool. A document assistant that searches, calculates and returns an answer can stay entirely inside LlamaIndex.

For a document-centred workflow already built there, migrating solely because the product now has an “agent” is unnecessary. Move only when the new workflow control you need justifies changing the execution layer.

LlamaIndex vs LangGraph

LlamaIndex and LangGraph often belong at different layers. LlamaIndex can prepare and retrieve evidence; LangGraph can control how a stateful workflow proceeds after receiving it. LlamaIndex's own Workflows are another option for that execution layer, so using both is an architectural choice, not a missing-feature workaround.

The test is ownership: one component should own document retrieval, and one should own the application's execution state. If both independently decide retries and conversation state, diagnosing a repeated action becomes harder.

Retrieval Quality: Evaluate the Failure, Not the Brand

Neither framework earns an accuracy victory from its feature list. Use the same source collection and questions, then inspect whether the correct evidence was retrieved before judging the prose of the answer.

For a policy assistant, include an ordinary refund question, a question answered only by an amendment, a question whose answer is absent, and a question about a document the user cannot access. A fluent answer is not enough: check source version, permission boundaries and whether the answer is supported.

Start with fixed retrieval. Add adaptive searching only where a failed answer could be repaired by another evidence-gathering step. The agentic RAG explainer describes when that additional decision loop earns its cost.

What Is Free, and What Is Paid?

The frameworks are free; hosted document processing, observability and deployment are separate purchases. Prices below were verified against the makers' live US pricing pages on 13 October 2026.

LlamaCloud and LlamaParse

LlamaParse is the name now used on LlamaIndex's public pricing page for its hosted document platform, accessed through the LlamaCloud dashboard. It covers parsing, extraction and indexing. It is a commercial service alongside the open-source framework, not a required subscription for importing llama_index.

The public plans are Free at $0/month with 10,000 credits, Starter at $50/month with 40,000 credits, and Pro at $500/month with 400,000 credits. Enterprise is custom. Standard usage is $1.25 per 1,000 credits.

A PDF page costs different amounts depending on the parsing tier: Fast uses 1 credit, Cost-effective 3, Agentic 10 and Agentic Plus 45. The following chart converts those published rates into the credit value of the same batch. It excludes allowances, subscription minimums and add-ons, so it is not a monthly invoice.

Fast produces spatial text rather than Markdown, so choose it only if that output fits your ingestion path. Credits also do not erase plan limits: Free and Starter allow 5 concurrent parse jobs, compared with 20 on Pro. Upgrade for the capacity or features you need, not because a larger credit bundle automatically makes each credit cheaper. Sources: usage pricing and plan comparison.

LangSmith

LangSmith is the commercial observability, evaluation and deployment platform in the LangChain ecosystem. It can trace applications built with other frameworks, including LlamaIndex, so you can choose it independently of the framework decision. The maker explains that cross-framework support.

LangSmith pricing lists Developer at $0/month for one seat, including 5,000 base traces, and Plus at $39 per seat per month, including 10,000 base traces total. Additional usage is billed. Enterprise pricing is custom.

A trace records an application execution and can contain multiple model calls and tool events. It is not the same thing as a token or an individual retrieved passage. Plus includes a small serverless deployment, but you should budget additional deployment usage separately; the vendor recommends dedicated deployment for customer-facing agents.

The Same Monthly Workload, Priced Both Ways

At equal usage of the same services, switching frameworks produces no subscription saving. Here is a deliberately narrow monthly scenario, with the application and index hosted separately:

• Three developers need LangSmith Plus seats.
• The application produces 20,000 base traces.
• You parse 1,000 new PDF pages using LlamaParse's Agentic tier.
• You use the same chat model, embedding model and token workload in either implementation.
• You do not use hosted Index, retained-file storage or other credit-consuming document operations in this scenario.

The parsing uses 1,000 × 10 = 10,000 credits, fitting the Free monthly allowance if it is otherwise unused. Its cash charge is $0, although the consumed credits have a list-rate value of $12.50. Those are different numbers: credit value does not create an invoice on the Free plan. Inputs: LlamaParse plans and usage rates.

The current LangSmith pricing calculator shows 0.005 LSU per additional trace, with $1 per LSU. Three seats cost 3 × $39 = $117. Trace usage above the shared allowance costs (20,000 − 10,000) × $0.005 = $50. The selected-service subtotal is $167/month with LlamaIndex and $167/month with LangChain.

That subtotal excludes model inference, embeddings and your own hosting. A total app budget is the selected services plus those costs. Comparing the same model workload keeps those inputs equal; a framework that causes extra retrieval or model calls changes the workload and must be measured separately.

There is no framework-driven price crossover here: both framework fees remain zero, and both can use the same paid services. Comparing $50 of document processing with $39 of observability would compare different work. The spending decision is which service your app needs and what it consumes.

The Billing Gap: Retrieval Queries Are Not Chat Turns

LlamaParse's hosted Index v2 prices a retrieval query at 1 credit and a chat turn at 100 credits, in addition to relevant parsing and storage charges. At the standard rate, 1,000 operations have a credit value of $1.25 for retrieval or $125 for hosted chat, before allowances. These are different operations, not two prices for an identical answer. Source: Index pricing.

This distinction matters when designing a combined stack. If your outer agent already generates the final answer, decide whether its retrieval tool needs evidence or another service's synthesized answer. That choice affects both the interface and the meter. A local query_engine.query() call does not automatically incur the hosted Index chat-turn fee; the managed service must actually be in the request path.

When to Use LlamaIndex and LangChain Together

Use both when document retrieval and action orchestration are independently difficult. A support agent that must interpret policy amendments and coordinate an approval workflow is a good candidate. A question-answer box over clean text usually is not.

Give LlamaIndex ownership of document ingestion and retrieval. Expose a narrow function or service that returns relevant passages with source identifiers and metadata. Let the LangChain agent call that function as a tool, with LangGraph underneath its execution. The vendor's combined-framework guidance describes this division.

For the support example, return the policy passage, its document ID and revision metadata. The agent can then compare it with the order record and produce a proposed action. Preserve those source references through the final response so the application can check what supports the answer.

Avoid wrapping a fully synthesized answer in another answer-generating loop by accident. A string from a query engine can be convenient, but it can also hide the underlying passages from the outer agent. When source inspection matters, prefer a retrieval result with explicit evidence. Use synthesized subanswers only when that is an intentional task boundary.

The extra cost of two frameworks is maintenance: two integration surfaces, more dependency updates and another boundary to instrument. Both are justified only when you can describe what each owns without saying they both “handle the agent.”

Switching Frameworks: What Moves and What Breaks?

Migration cost lives in data conventions and workflow state, not in replacing import statements. Switching from LlamaIndex to LangChain, or using LlamaIndex as an alternative to LangChain's retrieval components, should begin with the contract your existing app exposes.

Keep the original source documents and exportable metadata. Preserve document IDs, access labels, source revisions and citation references. Those are application assets. Framework-specific document and node objects are adapters around them.

Existing embeddings may remain useful if the embedding model, dimensions, stored text and database schema remain compatible. Do not assume a new adapter can read every field unchanged. If you change chunk boundaries or the embedding model, plan to rebuild the affected index and reassess retrieval results. LlamaIndex's vector-store guide distinguishes its default in-memory store from persistent integrations.

For workflows, treat conversation state, pending approvals and unfinished actions as a separate migration. Do not replay historical tool calls merely to reconstruct state: a repeated external action can have consequences even when the conversation looks correct. Define how old sessions finish and how new sessions begin before switching traffic.

Do not switch a stable LlamaIndex document agent just to acquire the agent label. Do not replace a working LangChain retrieval flow because another example has fewer lines. Switch when the current abstraction is making a specific, recurring requirement harder to express or maintain.

Frequently Asked Questions

Your Next Build Decision

Start with the failure your prototype must solve. If it is finding the right source passage, build the small retrieval path first with LlamaIndex. If it is coordinating tools and approved actions, start with LangChain and define the state that must survive between steps. Keep the first working version narrow enough that you can inspect every retrieved source and every proposed action.

Use the AI Business Workflow Audit Checklist to define the job, its evidence requirements and its approval boundary before choosing the framework.