Making LangChain, LlamaIndex, and CrewAI Work Together Without Losing Your Mind

After trying to build a document analysis system that didn't completely suck, I learned the hard way that each framework is good at exactly one thing and fucking terrible at everything else.

The Brutal Reality of Framework Limitations

LangChain is great at connecting things. It has 400+ integrations with every API and database you've ever heard of, plus extensive tool ecosystems for everything from web searches to database queries. But its agents struggle with complex reasoning, and the documentation is pure fiction when it comes to how easy multi-step workflows actually are in production.

LlamaIndex is brilliant at understanding documents. Give it a PDF and it'll actually comprehend what's in there, using sophisticated chunking strategies and semantic retrieval methods that make LangChain's basic text splitters look amateur. But try to build any kind of complex workflow with it and you'll want to throw your laptop out the window. It's a one-trick pony that does that trick really, really well.

CrewAI makes agents that actually collaborate. Instead of one confused agent trying to do everything, you can have specialized agents that hand work back and forth like a real team using role-based task delegation and collaborative workflows. But it's the newest kid on the block, so expect to debug weird edge cases that aren't in any tutorial.

What Happens When You Try to Use Just One

Started with just LangChain because everyone on Twitter said it was the Swiss Army knife of AI frameworks. Three weeks and 47 Stack Overflow tabs later, I had a system that could connect to Slack, Google Drive, and our PostgreSQL database, but couldn't understand a goddamn thing in any of the documents it retrieved. The retrieval was working perfectly. The responses were hot garbage.

So I switched to LlamaIndex. Suddenly my system understood every document perfectly, but I couldn't get it to do anything useful with that understanding. Want to send the results to Slack? Good luck. Want to chain multiple queries together? Hope you like writing custom orchestration code.

CrewAI looked promising for coordinating multiple specialized tasks, but it's basically useless without tools from other frameworks. It's like having a great project manager with no actual workers.

The Integration Tax You'll Pay

Here's what nobody fucking tells you: using all three frameworks together is slower than molasses. My system went from sub-second responses with LangChain alone to 3-4 second responses with the full integration. But those responses are actually useful now instead of confident bullshit, so I guess it's worth it.

You'll also spend more time debugging integration issues than actual business logic. LangChain 0.2.x breaks compatibility with CrewAI every few weeks. LlamaIndex updates its core APIs without warning. Pin your versions in requirements.txt using Poetry or prepare for pain.

The One Integration Pattern That Actually Works

After 6 months of false starts, here's the only architecture that stayed stable:

1. LlamaIndex owns document understanding - It indexes your docs and provides a query interface
2. LangChain orchestrates the workflow - It calls LlamaIndex for knowledge, other APIs for actions
3. CrewAI coordinates complex tasks - Multiple agents use LangChain tools and LlamaIndex knowledge

The key insight: don't try to make them equal partners. LlamaIndex is your smart retrieval layer, LangChain is your Swiss Army knife, and CrewAI is your team coordinator. Each stays in its lane.

Real Production Experience

I'm running this stack in production for a legal document analysis system processing 10,000+ case files and legal precedents. LlamaIndex ingests all the PDFs, LangChain agents extract key information and check it against regulatory databases, and CrewAI coordinates the review, fact-checking, and report generation teams.

It works, but it's not pretty. The system needs 32GB of RAM minimum because each framework loads its own models. Memory leaks are common at framework boundaries. And when something breaks, the stack traces are useless because they span three different architectures.

Would I build it again? Yes, because the alternative is a system that's either too dumb to understand documents or too limited to do anything useful with them.

What the Tutorials Don't Tell You

Every fucking tutorial makes this look easy. "Just convert your LlamaIndex query engine to a LangChain tool!" they say. What they don't mention is that you'll spend 3 days debugging why the tool conversion randomly fails with AttributeError: 'NoneType' object has no attribute 'metadata' - an error message that tells you absolutely nothing.

The Code That Actually Works

Here's the integration pattern that survived 6 months of production debugging:

## This is the real integration code that actually works
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.tools import QueryEngineTool
from llama_index.core.tools.tool_spec.base import BaseToolSpec
from langchain.tools import Tool
import logging

## Enable debug logging or you're flying blind
## See: https://docs.llamaindex.ai/en/stable/module_guides/observability/
logging.getLogger('llama_index').setLevel(logging.DEBUG)
logging.getLogger('langchain').setLevel(logging.DEBUG)

## LlamaIndex setup - this part is easy
documents = SimpleDirectoryReader('data').load_data()
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(
    similarity_top_k=5,
    response_mode="tree_summarize"  # compact mode breaks with long docs
)

## The integration that breaks every LangChain update
def create_safe_llamaindex_tool(query_engine, name, description):
    """Wrap LlamaIndex in error handling because it will fail"""
    def query_wrapper(query: str) -> str:
        try:
            response = query_engine.query(query)
            return str(response)
        except Exception as e:
            return f"Query failed: {str(e)}"
    
    return Tool(
        name=name,
        description=description,
        func=query_wrapper
    )

knowledge_tool = create_safe_llamaindex_tool(
    query_engine,
    "knowledge_search",
    "Search internal documents. Input should be a specific question."
)

Memory Management Nightmares

Each framework has its own idea about memory. LangChain has conversation buffers, LlamaIndex has context windows, and CrewAI has agent-specific memory. Getting them to share state is like herding cats.

The brutal truth: you'll leak memory at every framework boundary. My production system needs a daily restart because memory usage climbs from 8GB to 32GB over 24 hours. I've tried every memory management pattern in the docs. Spoiler alert: they don't fucking work.

Here's the workaround that actually helps:

## Memory cleanup that saves your deployment
import gc
from typing import Any, Dict

class SharedMemoryManager:
    def __init__(self):
        self.conversation_history = []
        self.knowledge_cache = {}
        self.cleanup_counter = 0
    
    def add_interaction(self, query: str, response: str):
        self.conversation_history.append({"query": query, "response": response})
        self.cleanup_counter += 1
        
        # Force cleanup every 50 interactions or your memory dies
        if self.cleanup_counter % 50 == 0:
            self._aggressive_cleanup()
    
    def _aggressive_cleanup(self):
        # Keep only last 100 interactions
        if len(self.conversation_history) > 100:
            self.conversation_history = self.conversation_history[-100:]
        
        # Clear knowledge cache
        self.knowledge_cache.clear()
        
        # Nuclear option - force garbage collection
        gc.collect()

Integration Failures I've Debugged

LangChain 0.2.0 breaks CrewAI agents - The tool calling interface changed and CrewAI didn't update for 3 weeks. I found out at 2 AM when prod went down. Solution: pin to LangChain 0.1.17 and never upgrade on Fridays.

LlamaIndex query engines timeout randomly - No error message, just hangs forever. Solution: wrap everything in asyncio timeouts and retry logic.

CrewAI agents forget they're part of a crew - They start acting like individual agents instead of collaborating. Solution: explicitly pass crew context in every tool call.

Version compatibility hell - LangChain 0.2.x + LlamaIndex 0.10.x + CrewAI 0.3.x = guaranteed breakage. I maintain a dependency compatibility matrix in our README because the pain is real.

The Production Architecture That Survives

After 8 months of debugging, here's what stays up:

1. LlamaIndex runs in its own process - Isolated to prevent memory leaks from affecting other components
2. LangChain orchestrates through REST APIs - Process boundaries prevent cascading failures
3. CrewAI agents communicate via message queues - Redis pub/sub handles coordination better than direct framework integration
4. Everything has circuit breakers - When one framework shits the bed, the others keep running

This isn't elegant. It's not what the tutorials show. But it's what works when your boss asks why the system is down again.

Performance Reality

Tutorial claims: "Sub-second responses with multi-framework integration!"

Production reality: 3-4 seconds minimum, 10+ seconds when things are struggling. Each framework adds its own overhead:

• LlamaIndex document retrieval: 500-1000ms
• LangChain tool orchestration: 300-800ms
• CrewAI agent coordination: 1000-2000ms
• Framework communication overhead: 500-1500ms

Want faster responses? Use one framework. Want useful responses? Accept the performance hit and use all three.