LangChain vs LlamaIndex vs CrewAI

By Kokil Thapa | Last reviewed: September 2026

Choosing between LangChain vs LlamaIndex vs CrewAI is one of the first decisions you face when wiring LLM APIs into a real product. All three are Python-first orchestration libraries. They sit between your application and providers like OpenAI or Anthropic. They are not models themselves. On client projects I integrate LLM APIs into Laravel backends, legal portals, and eCommerce workflows. The wrong framework choice often shows up weeks later as brittle chains, slow retrieval, or runaway token bills. This guide compares all three on architecture, RAG depth, agent patterns, and production fit so you can commit before your codebase grows around the wrong abstraction.

If you need hands-on help wiring AI into an existing stack, see our AI integration and automation service in Nepal for Laravel, API, and workflow work.

What is the difference between LangChain, LlamaIndex, and CrewAI?

All three frameworks wrap LLM provider APIs. They add structure around prompts, memory, tools, and retrieval. The split is scope and opinion.

LangChain is the broadest toolkit. It offers Runnable chains, tool-calling agents, memory modules, vector-store adapters, and LangGraph for stateful workflows. Think of it as plumbing for almost any LLM feature.

LlamaIndex (formerly GPT Index) centres on data. It ingests PDFs, web pages, databases, and APIs. It builds indexes, runs hybrid retrieval, and exposes query engines. Agents exist, but retrieval quality is the main selling point.

CrewAI models teams of agents. Each agent gets a role, goal, backstory, and optional tools. Tasks flow through a crew with sequential or hierarchical process modes. It builds on LangChain concepts but hides much of the wiring.

PHP and Laravel teams often run these libraries in a sidecar Python service. Your main app stays in PHP 8.3+ or Laravel 12/13. A queue worker handles embedding, retrieval, or agent runs. For PHP-native options, read our guide on a LangChain alternative for PHP with Prism.

Official references: LangChain Python documentation, LlamaIndex documentation, and CrewAI documentation.

When should you choose LangChain for LLM applications?

Pick LangChain when your product needs more than retrieval alone. Chat with memory, structured output, tool calls to your REST API, and branching logic all fit naturally. LangGraph adds cyclic state machines for approval flows and human-in-the-loop steps.

Install and run a minimal chain

Use Python 3.11+ and a current LangChain release. Pin versions in production — breaking changes still happen across minor releases.

pip install langchain langchain-openai langgraph

# .env: OPENAI_API_KEY=sk-...

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser

llm = ChatOpenAI(model="gpt-4o", temperature=0)
prompt = ChatPromptTemplate.from_messages([
    ("system", "You summarise legal FAQ entries in plain English."),
    ("human", "{question}")
])
chain = prompt | llm | StrOutputParser()

print(chain.invoke({"question": "What documents are needed for notarisation?"}))

On a legal-tech portal I built, this pattern powered FAQ summarisation behind a Laravel queue job. The PHP controller dispatched work. Python returned JSON. Laravel cached the result in Redis 8.10.

LangChain strengths in production

• Hundreds of integrations — vector stores, loaders, chat models, output parsers
• LangGraph for durable, debuggable agent workflows with checkpoints
• LangSmith tracing when you need to audit prompt chains in staging
• Runnable composition (`prompt | llm | parser`) keeps pipelines readable

LangChain weaknesses you should plan for

The surface area is huge. Imports move between packages often. Two developers can solve the same problem with different abstractions. Lock your dependency versions and write integration tests around critical chains.

Pair LangChain services with solid API development practices. Rate-limit the Python sidecar. Log token usage per request. Our AI rate limits and cost optimization guide covers the billing side.

Is LlamaIndex better than LangChain for RAG?

For retrieval-augmented generation, yes — LlamaIndex is usually the better default. LangChain can build RAG, but LlamaIndex treats indexing, chunking, metadata filters, and reranking as first-class concerns. That matters when your knowledge base grows past a few dozen PDFs.

Build a document query engine

pip install llama-index llama-index-llms-openai llama-index-embeddings-openai

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

Settings.llm = OpenAI(model="gpt-4o")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")

documents = SimpleDirectoryReader("./data/notary-guides").load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=5)

response = query_engine.query(
    "What is the fee structure for affidavit notarisation in Nepal?"
)
print(response)

Validate JSON payloads between services with our JSON formatter tool during early integration. Small schema mismatches between PHP and Python cause silent failures.

Where LlamaIndex wins over LangChain RAG

1. Chunk strategies — sentence, semantic, and hierarchical node parsers ship built-in
2. Index types — vector, tree, keyword, and composable indexes without custom glue code
3. Query engines — sub-question decomposition and multi-step retrieval out of the box
4. Evaluation hooks — response relevancy and faithfulness checks for regression testing

For a client portal like Mijar Law Associates, document search over uploaded contracts demands reliable chunk boundaries and metadata filters by client ID. LlamaIndex handles that cleaner than hand-rolled LangChain retrievers.

When your Laravel app needs product search instead of legal docs, see AI-powered search for Laravel products for architecture patterns that complement LlamaIndex backends.

When LangChain RAG is enough

Small FAQ sets under 100 pages rarely need LlamaIndex. LangChain's `RecursiveCharacterTextSplitter` plus a vector store adapter gets you live in an afternoon. Add LlamaIndex when retrieval quality becomes a support ticket driver.

How does CrewAI handle multi-agent workflows?

CrewAI assigns specialised agents to sequential or hierarchical tasks. A researcher gathers facts. A writer drafts copy. An editor refines tone. Each agent can use tools — web search, file readers, or custom Python functions wrapping your internal API.

Define a two-agent crew

pip install crewai crewai-tools

from crewai import Agent, Task, Crew, Process
from langchain_openai import ChatOpenAI

llm = ChatOpenAI(model="gpt-4o", temperature=0.3)

researcher = Agent(
    role="Legal Research Analyst",
    goal="Find accurate information about Nepal court marriage requirements",
    backstory="You specialise in Nepali family law research for client portals.",
    llm=llm,
    verbose=True
)

writer = Agent(
    role="Content Writer",
    goal="Draft a clear FAQ answer suitable for a law firm website",
    backstory="You write plain-language legal content for non-lawyers.",
    llm=llm,
    verbose=True
)

research_task = Task(
    description="Research required documents for court marriage in Nepal.",
    expected_output="Bullet list of documents with brief explanations.",
    agent=researcher
)

write_task = Task(
    description="Turn the research into a 200-word FAQ answer.",
    expected_output="Polished FAQ paragraph with headings.",
    agent=writer
)

crew = Crew(
    agents=[researcher, writer],
    tasks=[research_task, write_task],
    process=Process.sequential
)

result = crew.kickoff()
print(result)

Human review belongs in the loop. Our AI content pipeline draft-review-publish article shows how to gate agent output before it hits production CMS fields.

CrewAI trade-offs

Multi-agent runs consume more tokens than a single prompt. Three agents on GPT-4o can cost Rs 150–400 per job (~USD 1–3) depending on tool loops. Budget accordingly. CrewAI also depends on LangChain-compatible LLM wrappers, so you inherit some of that dependency surface.

Use CrewAI for offline batch work — content drafts, competitive research, internal report generation. Avoid it for sub-second user-facing chat unless you cache aggressively.

Which framework fits production Laravel and API integrations best?

Most production setups I see combine frameworks rather than picking one forever. The decision tree below reflects what actually ships on custom software projects in 2026.

Recommended production architecture

Keep your source of truth in Laravel 12 or 13 with PHP 8.3+. Run Python 3.11+ in a separate container or systemd service. Communicate over HTTP with signed tokens. Never expose provider API keys to the browser.

# FastAPI sidecar example (ai_service/main.py)
from fastapi import FastAPI, Header, HTTPException
from pydantic import BaseModel

app = FastAPI()

class RagQuery(BaseModel):
    question: str
    client_id: int

@app.post("/rag/query")
def rag_query(body: RagQuery, x_api_token: str = Header(...)):
    if x_api_token != os.environ["INTERNAL_AI_TOKEN"]:
        raise HTTPException(status_code=401)
    # LlamaIndex query_engine.invoke here
    return {"answer": "...", "sources": [...]}

Apply the same abuse-prevention mindset from our API rate limiting and abuse prevention guide. AI endpoints are expensive attack targets.

Combining all three

Real projects often stack frameworks:

• LlamaIndex indexes client documents overnight via a scheduled job
• LangChain powers the live chat agent with tools hitting your Laravel API
• CrewAI generates weekly SEO draft batches for human editors

For the Claude provider specifically, read our Anthropic Claude API developer guide. Model choice affects all three frameworks equally.

Governance matters regardless of library. Review AI governance and responsible AI basics before letting agents write client-facing legal content. A portal like Court Marriage In Nepal needs factual accuracy, not fluent hallucinations.

Cost and hosting notes for Nepal teams

Running embeddings locally with Ollama saves foreign-exchange API spend. It adds server RAM requirements — budget Rs 8,000–15,000/month (~USD 60–110) for a GPU-less VPS that handles small models only. For GPT-4o-class quality, provider APIs remain the practical default.

Test regex and extraction patterns in our regex tester before embedding them in ingestion pipelines. Bad parsers corrupt indexes silently.

Enterprise rollouts benefit from upfront planning and research to define retrieval metrics before framework selection locks you in.

Key Takeaways

• LangChain is the flexible default for mixed chains, tools, and LangGraph workflows — pin versions and test critical paths.
• LlamaIndex wins for RAG when document volume, chunk quality, and citation metadata matter.
• CrewAI fits offline multi-agent pipelines — content research, reports, batch drafts — not low-latency chat.
• Wrap any Python framework in a secured FastAPI sidecar; keep Laravel as the auth and business-logic layer.
• Combine frameworks by task type rather than forcing one library to do everything.
• Monitor token costs, cache hot queries in Redis, and keep humans in the loop for client-facing output.

People Also Ask

Can LangChain and LlamaIndex be used together?

Yes. Many teams use LlamaIndex for indexing and retrieval, then pass retrieved context into LangChain chains or agents. LlamaIndex exposes LangChain-compatible retrievers. The combination gives you strong RAG plus flexible downstream orchestration without rewriting either stack.

Is CrewAI built on top of LangChain?

CrewAI uses LangChain-compatible LLM interfaces and can call LangChain tools. It is not a LangChain plugin — it adds its own agent, task, and crew abstractions. You can run CrewAI without writing LangChain chains directly, but LangChain packages often appear in your dependency tree.

Which framework is easiest for beginners in 2026?

CrewAI has the gentlest onboarding for multi-agent demos — define roles and tasks, then call `kickoff()`. For RAG beginners, LlamaIndex's `VectorStoreIndex.from_documents()` path is faster than assembling LangChain retriever chains manually. LangChain itself has the steepest curve due to module breadth.

Do these frameworks work with local models like Ollama?

All three support local inference through Ollama or compatible OpenAI-style endpoints. Point the LLM and embedding configuration at your local base URL. Expect lower answer quality on small models. Always benchmark retrieval and generation separately before going live.

Pick the right tool, then ship the integration

The LangChain vs LlamaIndex vs CrewAI choice is not permanent. Start with the framework that matches your hardest problem — RAG, agents, or multi-agent batches. Wrap it in a thin API your main app owns. Measure retrieval accuracy and token spend from week one. I've seen teams waste months forcing LangChain to do indexing that LlamaIndex solves in a day. I've also seen LlamaIndex-only setups struggle when they needed tool-calling agents later.

If you want help architecting AI into a Laravel, WordPress, or custom portal stack, contact us or explore enterprise application development and web development services. For another shipped example of AI-ready legal content architecture, see Notary Nepal in our portfolio.