I Migrated Our RAG System from LangChain to LlamaIndex

Our LangChain RAG system was choking. We had 50k+ documents, and queries were taking 3-4 seconds on a good day. The memory usage was insane - our Docker containers were eating 8GB RAM just sitting idle. When I found myself writing custom chain logic to make basic document search not suck, I knew something was wrong.

The Real Problems LangChain Couldn't Solve

Query performance was complete trash. Our users wouldn't shut up about it. I burned weeks optimizing retrieval chains, messing with similarity thresholds, and swapping vector stores. Nothing worked. The whole thing crumbles under document-heavy workloads.

Memory leaks everywhere. After processing a few thousand queries, memory usage would balloon. I'd restart containers daily. The chain abstraction created objects that never got garbage collected properly - a known issue in the LangChain community.

Agent hell. Don't even get me started on LangChain agents. Half the time they'd fail silently. The other half they'd spiral into infinite loops calling the same tool over and over like a broken record. Debugging felt like trying to fix a car with a blindfold on because error messages were useless: "Agent failed to complete" - oh wow, thanks for that earth-shattering insight. The agent documentation looks pretty but production reality is a dumpster fire.

What Actually Made Me Switch

I tried LlamaIndex on a Friday afternoon, mostly out of frustration. Built a simple RAG system in 30 minutes that worked better than our LangChain monstrosity. Here's what immediately worked:

• Queries went from 3+ seconds to under 500ms - same documents, same questions
• Memory usage dropped from 8GB to 2GB for the same workload
• Error messages were actually readable - "Failed to retrieve from vector store: Connection timeout" instead of cryptic chain failures

The breaking point was when I ran both systems side-by-side for a week. LlamaIndex consistently returned more relevant results and didn't crash once. LangChain went down twice from memory issues, including one 3am outage that had me frantically restarting containers.

Look, after dealing with that 3am bullshit, here's when you should NOT put yourself through this migration:

Before You Make This Mistake

Don't migrate if you're heavily using LangChain agents. LlamaIndex's agent support is half-baked at best. If your system depends on complex multi-step reasoning with tools, stick with LangChain until LlamaIndex catches up.

Don't migrate if you're doing basic chat. If you're just building a chatbot without document retrieval, LangChain's conversation chains work fine. LlamaIndex is overkill.

Do migrate if document search is your main use case. That's where LlamaIndex actually shines.

Version Reality Check

LlamaIndex changes constantly - I migrated on v0.13.6 but they just dropped v0.14.1 with breaking changes. Pin your fucking versions or everything explodes. The API changes constantly, so pin your versions or watch everything implode. I learned this when v0.13.7 randomly broke our PDF processing pipeline on a Tuesday morning - spent 4 hours rolling back.

The migration took me two weeks because I didn't expect the dependency hell and API differences. Plan for longer than you think. Check the migration guides for specific component conversions.

Step 1: Dependency Hell Setup

Don't just pip install llama-index. You'll get import errors for days. Here's what you actually need according to the official installation guide:

## This breaks for reasons I still don't understand
pip install llama-index llama-index-llms-openai llama-index-embeddings-openai

## If you get \"No module named 'llama_index.core'\" (you will), you need:
pip install llama-index-core

## For your specific vector store (this WILL be missing):
pip install llama-index-vector-stores-pinecone  # or chroma, weaviate, etc.

## For reading files that aren't basic text:
pip install llama-index-readers-file
pip install llama-index-readers-pdf  # PDFs need separate package

What breaks first: PDF reading. The SimpleDirectoryReader chokes on complex PDFs and gives you zero useful error messages. Install `llama-index-readers-pdf` or your documents won't load.

Windows gotcha: Path limits will screw you. Windows PATH limit is 260 chars and LlamaIndex package names are stupidly long. Use --user flag or short paths, or pip install fails silently with exit code 1.

Mac M1/M2 gotcha: Some PDF processing libraries randomly crash with "illegal hardware instruction" error on ARM chips. If PDFs stop working for no reason, this is probably why. Had to downgrade to an older PyPDF2 version to fix it.

Environment variables are the same as LangChain, so that's one thing that won't break.

Step 2: Document Loading (Where Everything Goes Wrong)

This should be simple. It's not.

LangChain code:

from langchain.document_loaders import DirectoryLoader, TextLoader
loader = DirectoryLoader('./data', glob=\"*.txt\", loader_cls=TextLoader)
documents = loader.load()

LlamaIndex "equivalent":

from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader('./data').load_data()

Why this breaks:

1. Silent PDF failures - SimpleDirectoryReader pretends to read PDFs but returns garbage text
2. Memory explosion - Large files get loaded entirely into memory, crashes on 100MB+ documents (known issue)
3. Encoding issues - Non-UTF8 files fail with cryptic Unicode errors

The fix that actually works:

from llama_index.core import SimpleDirectoryReader
from llama_index.readers.pdf import PDFReader

## Use explicit readers - ugly but it actually works
## Check https://llamahub.ai because there's readers for everything
pdf_reader = PDFReader()
documents = SimpleDirectoryReader(
    './data',
    file_extractor={
        \".pdf\": pdf_reader,
    },
    required_exts=[\".txt\", \".pdf\"],  # Skip files that will break
).load_data()

Pro tip: Test with one file first. I spent 2 hours debugging why batch processing failed, only to find one corrupted PDF (corrupted-report-2023.pdf) was killing the entire 5,000 document pipeline. Use document metadata to track which files cause issues.

Step 3: Text Chunking (Where Performance Dies)

LangChain chunking:

from langchain.text_splitter import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(
    chunk_size=1000,
    chunk_overlap=200
)
texts = text_splitter.split_documents(documents)

LlamaIndex chunking:

from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import Settings

## Global settings are cursed - don't use them
parser = SentenceSplitter(chunk_size=1000, chunk_overlap=200)
nodes = parser.get_nodes_from_documents(documents)

What goes wrong:

• chunk_size=1000 is hot garbage for most documents - queries crawl because chunks are pathetically small (chunking best practices)
• Default sentence splitting breaks on code blocks, bullets, etc.
• Memory usage explodes with large documents and small chunks

What actually worked:

## Chunk size 1000 is garbage for most docs
parser = SentenceSplitter(
    chunk_size=2048,  # Bigger chunks = better context
    chunk_overlap=400,  # More overlap = better retrieval
    paragraph_separator=\"

\",  # Don't break paragraphs
)

## This randomly breaks on perfectly normal text
try:
    nodes = parser.get_nodes_from_documents(documents)
except Exception as e:
    print(f\"Chunking failed: {e}\")
    # Give up and just split on newlines

I spent a weekend testing chunk sizes from 512 to 4096. Bigger chunks (2048+) worked better for our financial documents but killed performance on our technical manuals. Your mileage will vary. Check the chunking strategies guide for more details.

Step 4: Vector Store Hell

Vector stores are where everything breaks. The APIs look similar but behave completely differently.

LangChain:

from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings

embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(texts, embeddings)

LlamaIndex (what the docs show):

from llama_index.core import VectorStoreIndex, Settings
from llama_index.embeddings.openai import OpenAIEmbedding

Settings.embed_model = OpenAIEmbedding()
index = VectorStoreIndex.from_documents(documents)

Why this fails in production:

1. Global Settings are completely broken - they implode with multiple indexes or async operations (Settings documentation)
2. from_documents() is slow as hell - re-embeds everything even if embeddings exist
3. No progress indication - indexing 50k documents with zero feedback

What actually works:

## Fuck global Settings - they never work
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import VectorStoreIndex

embed_model = OpenAIEmbedding()

## Track progress or you'll think it froze
import tqdm
nodes_with_embeddings = []
for node in tqdm.tqdm(nodes, desc=\"Embedding nodes\"):
    node.embedding = embed_model.get_text_embedding(node.text)
    nodes_with_embeddings.append(node)

index = VectorStoreIndex(nodes_with_embeddings, embed_model=embed_model)

Migrating existing vector stores:
If you have Chroma/Pinecone/etc data, don't use LlamaIndex's connectors. They're buggy and don't handle metadata properly. Export your data, transform it, then re-import. It's slower but it actually works. Check the vector store integration guides for specifics.

Step 5: Query Engine (This Actually Works)

If you got the previous steps right, queries will be noticeably faster.

LangChain:

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI

llm = OpenAI()
retriever = vectorstore.as_retriever()
qa_chain = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    return_source_documents=True
)

response = qa_chain({\"query\": \"What is the main topic?\"})

LlamaIndex:

from llama_index.llms.openai import OpenAI

## Still avoiding global Settings because they suck
llm = OpenAI()
query_engine = index.as_query_engine(
    llm=llm,
    response_mode=\"compact\",

Agent Migration: Don't Even Try

LlamaIndex agents are half-baked and unreliable. I wasted 3 days trying to migrate our agent workflows before throwing in the towel. The `ReActAgent` crashes for no reason, tool errors kill everything, and debugging is a nightmare.

If you're using LangChain agents in production, just don't migrate. Keep LangChain for agents and maybe use LlamaIndex only for the underlying retrieval. Hybrid setups are messy but better than broken agents.

## Looks innocent but will ruin your day
from llama_index.core.agent import ReActAgent
agent = ReActAgent.from_tools(tools, llm=llm)
## This breaks in mysterious ways at 2am

Memory Systems: Prepare for Disappointment

LangChain's memory systems are actually pretty good. LlamaIndex's are basic at best.

## What you had in LangChain
## LangChain memory - sophisticated control
from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory(
    return_messages=True,
    memory_key="chat_history",
    output_key="answer"  # This level of control doesn't exist in LlamaIndex
)
## See: https://python.langchain.com/docs/how_to/chatbots_memory/

## What you get in LlamaIndex
## LlamaIndex memory - basic implementation  
from llama_index.core.memory import ChatMemoryBuffer
memory = ChatMemoryBuffer.from_defaults(token_limit=2000)
## That's it. No fine-grained control, no custom keys, no flexibility.
## Docs: https://docs.llamaindex.ai/en/stable/module_guides/deploying/chat_engines/

Our conversational system went from sophisticated memory management to basic chat history. Users noticed the downgrade.

Production Deployment Nightmares

Version hell. LlamaIndex changes APIs between minor versions. What works in 0.13.3 breaks in 0.13.4. Pin your versions aggressively and test everything when upgrading. Check the changelog religiously.

Global Settings are a disaster in production. They create race conditions in async environments and make debugging impossible when you have multiple indexes. Avoid global settings in multi-threaded apps.

## Production killer - don't do this
from llama_index.core import Settings
Settings.llm = OpenAI()  # Don't do this - multithreading kills everything

## Tedious but actually works in prod
query_engine = index.as_query_engine(
    llm=OpenAI(),
    embed_model=OpenAIEmbedding(),
    # More typing but doesn't randomly fail
)

Memory leaks still exist. Not as bad as LangChain, but they're there. After 10k queries, memory usage creeps up. Restart your services periodically. Monitor with tools like `memory_profiler` and track GitHub issues.

What Breaks First in Production

1. PDF processing fails silently - one corrupted PDF kills your entire indexing pipeline (file readers docs)
2. Embedding API rate limits - LlamaIndex doesn't handle backoff well, prepare for 429 errors
3. Vector store connections time out - error messages are useless, debugging takes hours
4. Large document memory explosions - 50MB+ documents crash the process (memory optimization guide)
5. Async operations are buggy - stick to synchronous if you want reliability (async limitations)

Monitoring and Debugging

Error messages are better than LangChain but still not great. You'll see "Failed to create query engine" instead of specific failure reasons. Enable debug logging for better error tracking.

Add your own error handling everywhere:

import traceback

try:
    response = query_engine.query(question)
except Exception as e:
    print(f"Query failed: {e}")
    print(f"Full traceback: {traceback.format_exc()}")
    # Because helpful error messages are apparently illegal

Performance monitoring is basic. Unlike LangChain's integration with LangSmith, LlamaIndex observability tools are limited. Build your own metrics collection or use OpenTelemetry integration.

Rollback Strategy (You'll Need It)

Keep your LangChain system running in parallel for at least a month. Route a percentage of traffic to LlamaIndex and compare results. We found issues that only appeared under real user load. Use A/B testing frameworks to compare performance systematically.

Database migration is the hardest part. If you need to change metadata schemas, plan for downtime. There's no clean way to migrate vector stores in-place. Check vector store migration guides for platform-specific approaches.

Reality Check

The migration did improve our system, but it took 6 weeks instead of the promised "few days." I spent an entire weekend debugging why the same query returned different results randomly (turns out it was a race condition in the global settings that only happened under load). Budget at least 3-4x longer than whatever bullshit timeline you read online.

What actually got better:

• Query speed: went from 2-6 seconds to 400-800ms consistently
• Memory usage: dropped from ~8GB to ~2GB baseline
• System stability: went from daily restarts to running weeks without issues

What got worse:

• Agent capabilities: went from good to broken
• Memory management: lost sophisticated features
• Development velocity: API changes slow us down

Bottom line: Worth it for document retrieval, disaster for complex workflows. For detailed migration strategies, see the official migration documentation and community migration discussions.