LangChain vs LlamaIndex vs Haystack vs AutoGen - Which One Won't Ruin Your Weekend

I spent the last year building production systems with LangChain, LlamaIndex, Haystack, and AutoGen. Here's what happens when you actually try to ship something instead of just running tutorials.

LangChain: Powerful Once You Survive the Learning Curve

LangChain v0.3 released in September 2024. They finally fixed the API breaking every other week, but getting there was painful.

What actually works:

• Tons of integrations: Every major LLM, vector DB, and tool you can think of has a connector
• LangGraph is solid: Once you wrap your head around the state machine concept
• LangSmith saves your ass: $47/month but worth every penny for debugging chains that fail mysteriously
• Real production deployments: Robinhood uses it, enterprise deployments are growing

The gotchas that'll fuck your Friday:

• v0.3.0 broke everything: ImportError: cannot import name 'Agent' from 'langchain.agents' - they moved it to langchain_core.agents without warning. Spent my entire Friday updating imports across 47 files.
• Memory leaks in production: Our RAG service hit 8GB RAM and crashed with MemoryError. Turns out AgentExecutor doesn't clean up intermediate steps. Had to add explicit cleanup every 100 queries.
• Async chains randomly hang: LangChain 0.2.16 had a bug where streaming responses would timeout after 30 seconds with no error message. Spent 3 days thinking it was our OpenAI setup. The fix? Downgrade to 0.2.15. Classic.
• Error messages that lie: AttributeError: 'NoneType' object has no attribute 'invoke' - thanks for telling me which of my 12 chain components is None, very helpful

Reality check: LangChain works great once you figure out the patterns, but expect 2-3 weeks of pain getting there. If you're building complex multi-step workflows, it's worth the suffering.

LlamaIndex: The One That Actually Works Out of the Box

LlamaIndex raised $19M Series A earlier this year and you can feel the difference. They actually focused on making RAG systems that don't make you want to quit programming.

Why it doesn't suck:

• SimpleDirectoryReader: Loads 40+ file formats without configuration hell
• Smart chunking by default: Their text splitters actually understand document structure
• Query engine just works: Built-in retry logic, fallbacks, and error handling
• Sub-question query decomposition: Automatically breaks complex queries into manageable parts

The only real complaints:

• Fewer integrations than LangChain: But the ones they have actually work
• Less flexibility: You get sensible defaults, not infinite customization
• LlamaCloud ain't cheap: Starts at $523/month, but cheaper than hiring another engineer

Bottom line: If you need RAG working tomorrow, start with LlamaIndex. I've seen junior developers get complex document Q&A running in 2 hours.

Haystack: German Engineering for Production Systems

Haystack Enterprise recently launched with enterprise features. Think of it as the PostgreSQL of AI frameworks - boring, reliable, and built for scale.

Production-ready out of the gate:

• Pipeline architecture makes sense: Visual flow charts that map to actual code
• Built-in monitoring: Metrics, logging, and alerts without extra tooling
• Multi-modal from day one: Text, images, tables in the same pipeline
• Zero-downtime updates: Swap pipeline components without restarting

Where it gets annoying:

• Steep learning curve: Concepts are different from other frameworks
• Verbose configuration: Everything requires explicit YAML or Python config
• Smaller community: Fewer Stack Overflow answers when you're stuck
• Enterprise features: The good features cost real money

Real talk: Haystack shines when you need rock-solid reliability. BMW uses it for internal knowledge systems, Airbus for technical documentation. If uptime matters more than development speed, this is your framework.

AutoGen: Cool Demos, Production Nightmare

Microsoft's AutoGen v0.4 is a complete rewrite. They had to throw out everything because the previous version was that broken.

The demo magic:

• Multi-agent conversations look impressive: Perfect for convincing executives AI is magic
• AutoGen Studio: Drag-and-drop agent building that actually works (now in autogen/python/packages/autogen-studio)
• Microsoft backing: Not going anywhere, gets regular updates
• Zero licensing costs: Unlike everything else on this list

The production nightmare:

• Infinite loops are real: Watched 2 agents argue about HTTP status codes for like 6 hours straight, burning maybe $200-something in OpenAI credits while they repeated the same damn conversation pattern
• Debugging is hopeless: When agents go rogue, you get zero visibility. No stack traces, no conversation flow, just AGENT_1: Let me think about this... repeated 500 times
• v0.4 apocalypse: Upgrading broke GroupChat, AssistantAgent, and UserProxyAgent - basically the entire API changed. 3 weeks rewriting everything from scratch.
• Basic examples fail: Fresh install, copy their "hello world" from docs, get ModuleNotFoundError: No module named 'autogen.agentchat'. Thanks Microsoft. Turns out you need to install with pip install pyautogen[autogen-studio] but that's not mentioned anywhere in the quickstart.

Hard truth: AutoGen is perfect for research papers and conference demos. I've never seen it work reliably in production. If you need multi-agent workflows that actually ship, build them in LangGraph or write custom orchestration.

OK enough framework bitching.

After shipping production LLM apps with all four, here's what actually determines your choice: how much pain you can tolerate, your team's debugging skills, and whether you care more about shipping fast or sleeping at night.

The Real Decision Matrix:

Based on Your Pain Tolerance

For Small Teams (1-5 developers) Who Want to Ship:

• LlamaIndex:

Get RAG working in a day, worry about scaling later

• Skip AutoGen:

Unless you enjoy debugging agent conversations at midnight

• Skip Haystack:

You don't have time to learn German engineering principles

• LangChain only if:

You have experienced developers who don't mind breaking changes

For Medium Teams (6-20 developers) With Some DevOps:

• LangChain:

If you can afford the learning curve and maintenance overhead

• LlamaIndex:

Still the fastest path to working RAG

• Haystack:

Only if you have dedicated platform engineers

• AutoGen:

For research projects, not production systems

For Enterprise Teams (20+ developers) With Money to Burn:

• Haystack:

If compliance and reliability matter more than velocity

• LangChain + LangSmith:

If you can afford $47/user/month and debugging time

• LlamaIndex:

Scales better than you'd think, cheaper than the alternatives

• AutoGen:

Still no. This isn't Microsoft Research.

Technical Considerations by Use Case

Just Want RAG to Work?

Winner: LlamaIndex (by a landslide)
Stop overthinking this. LlamaIndex gets RAG working fast and keeps it working.

I've built RAG systems with all four frameworks, and LlamaIndex is the only one that doesn't make you want to quit.

When to pick something else:

• LangChain:

If RAG is just one piece of a complex agent workflow

• Haystack:

If you need enterprise compliance and have months to spare

• AutoGen:

Never. Multi-agent RAG is a solution looking for a problem

Building Complex Agent Workflows?

Winner: LangChain (I hate saying this)
Look, I complain about Lang

Chain constantly, but LangGraph is genuinely brilliant for agent orchestration.

Still makes me want to quit programming, but it works when you need complex workflows. I'm biased against it because of all the import hell, but credit where it's due.

Reality check:

• AutoGen: Multi-agent conversations sound cool but break in weird ways
• Haystack:

Can handle complex workflows but the setup will crush your soul

• LlamaIndex:

Great for simple agents, not complex orchestration

Honest advice: Start simple.

Most "complex workflows" can be solved with a single agent and good prompt engineering.

Enterprise Production Systems (Where Downtime Costs Money)?

**Winner:

Haystack (if you have the budget and patience)**
Haystack is the only framework built by people who understand production operations. The enterprise sales team will annoy you, but the reliability is real.

Alternatives based on your constraints:

• LangChain:

If you need flexibility more than reliability

• LlamaIndex: Scales better than expected, much cheaper than Haystack
• AutoGen:

Still no. Enterprise doesn't mean "experimental"

Real talk: Most "enterprise" requirements are just enterprise theater.

I've seen teams spend 6 months evaluating frameworks when they could have shipped with Llama

Index in 2 weeks. Pick based on what your system actually needs, not what sounds impressive in meetings.

The Hidden Costs Nobody Talks About

Time to First Success (Measured with a Stopwatch)

• LlamaIndex: 30 minutes to working RAG, 2 days to production-ready with error handling (assuming you don't hit the dreaded PDF parsing bug in v0.14.2)
• LangChain: 3 hours to understand chains, 2-3 weeks before you stop hitting weird edge cases
• Haystack: 6 hours reading docs, 2-4 weeks getting YAML pipelines to work without ComponentNotFoundError (the docs lie about the required field names)
• AutoGen: 45 minutes to impressive demo, 2+ months to something that doesn't crash in prod (spoiler: you'll give up first)

Developer Sanity Costs

• AutoGen:

Free framework, infinite debugging time

• LangChain: $47/user/month for Lang

Smith, worth every penny

• LlamaIndex: $523/month for LlamaCloud beats hiring another engineer
• Haystack:

Enterprise license pays for itself in reduced therapy costs

Real TCO for 10-Person Team (Year 1)

• From Actual Experience
• AutoGen: $0 + 50% developer turnover from frustration (we lost 2 good devs over this shit)
• LangChain: $5,123 + 2-3 weeks onboarding per developer (longer if they're junior)
• LlamaIndex: $6,276 + fastest feature delivery (our PM loves this one)
• Haystack: $15,000+ + months of setup but then it just works (if you have patience)

The Bottom Line:

Stop Overthinking It

Pick LlamaIndex if:

• You want RAG working by Friday (seriously, pip install llama-index and you're 30 minutes from a working system)
• Document Q&A is your main use case
• their chunking and retrieval just works
• Your team values shipping over debugging architectural decisions
• You need to demo something to investors next week

Pick LangChain if:

• You need agents that do more than just Q&A (workflows, tool calling, state management)
• Your team includes senior devs who won't quit when they see from langchain.agents.agent_toolkits import... imports
• You're willing to pay $47/month per dev for LangSmith because debugging complex chains without it is hell
• You need integrations with every possible tool/API (they have connectors for literally everything)

Pick Haystack if:

• Reliability matters more than development velocity
• You have enterprise compliance requirements
• You have DevOps engineers who can handle the complexity
• Budget isn't a constraint

Skip AutoGen because:

• I wasted 6 months on this garbage
• Multi-agent conversations that never converge aren't "cutting edge"
• they're broken
• v0.4 broke everything without warning.

Literally everything.

• Microsoft backing means it'll exist forever in perpetual beta hell

The real truth: Most projects start with Llama

Index, graduate to LangChain when they need more features, and occasionally end up at Haystack when they need enterprise reliability. AutoGen stays in the research lab where it belongs.

Pick based on what you need to ship next month, not what you might need next year.

After a year of building with all four frameworks, here's the brutal truth about switching between them - and why most teams stick with their first choice longer than they should.

The Real Migration Stories

From LangChain to LlamaIndex (Escape Route)

• Why people switch: Tired of ImportError: cannot import name 'create_retrieval_chain' every fucking update
• Reality: 2-3 weeks rewriting, but ended up with 70% less code and zero memory leaks
• Hidden costs: Lost LangSmith's debugging (painful), but gained system stability (priceless)
• Success rate: High - developers actually smile during standups again (I witnessed this transformation firsthand)

From LlamaIndex to LangChain (Masochist Route)

• Why people switch: Need agent workflows beyond simple Q&A, LlamaIndex's agent framework is pretty basic
• Reality: 6 weeks learning LangGraph state machines, debugging chains with 15+ components, wrestling with AgentExecutor timeouts
• Hidden costs: Deploy times went from 5 minutes to 30 minutes, error rates tripled, needed 2 senior devs instead of 1 junior
• Success rate: Medium - half the team quits, other half becomes LangChain experts and demands raises

From Either to Haystack (Enterprise Exile)

• Why people switch: GDPR compliance audit failed, need SOC2 certification, or current system crashed during board demo
• Reality: 3+ months translating everything to YAML pipelines, $217/hour Kubernetes consultant, DevOps team size doubles
• Hidden costs: Enterprise license starts at $53k/year, platform engineers cost $183k each, 6-month sales cycle just for pricing
• Success rate: High if your budget is bigger than most startup valuations

From Anything to AutoGen (Don't)

• Why people try: Multi-agent conversations sound impressive in meetings
• Reality: Endless debugging, unpredictable behavior, no production stories
• Hidden costs: Developer sanity, project timelines, customer trust
• Success rate: Near zero for production systems

Framework Lock-in Reality

Data Lock-in (Low)

• All frameworks work with standard formats (JSON, text, vectors)
• Moving data between systems is straightforward
• Vector embeddings are transferable

Code Lock-in (High)

• Each framework has completely different abstractions
• No shared patterns or concepts between them
• Complete rewrite required for switching

Team Knowledge Lock-in (Highest)

• Learning each framework takes weeks/months
• Hard to find developers experienced in multiple frameworks
• Team productivity drops significantly during transitions

Vendor Lock-in by Framework:

• LangChain: Medium (LangSmith dependency)
• LlamaIndex: Low (can run completely self-hosted)
• Haystack: High (enterprise features require paid licenses)
• AutoGen: Low (open source, but also low value)

The Bottom Line on Switching

Don't switch frameworks unless:

• Current framework fundamentally can't handle your use case
• You have 2+ months to invest in the transition
• Your team is committed to learning new abstractions
• The new framework solves problems worth the migration cost

Most successful pattern: Start with LlamaIndex for MVP, graduate to LangChain when you need complex workflows, move to Haystack only for enterprise requirements.

AutoGen migration advice: Just don't.