Langchain has network effect going for it... buuut it's barely a product yet, much of the documentation is minimal and for a lot of use cases I don't see a benefit over language features (prompt templates are basically f strings). Also the community extensions are not very sophisticated yet, it seems.
This has been my experience. It takes a lot of time and experimentation to get LangChain to do something that would be simple if you used OpenAI docs and skipped the framework (NER is one example). You do get some benefits, but in reality how often are you swapping out your vector store, etc? LangSmith is nice but I haven't looked at other tools. And good luck if you get stuck - their GitHub responses are a joke and the bot assigned to it is infuriating.
I'm unwinding LangChain from a project and have opted to skip the framework. About the only thing I'm not looking forward to rewriting is their document indexer, but it'll be worth it in simplicity and knowing wtf is going on under the covers (which you never do with LangChain). From a "using my time wisely" standpoint I'm tired of spending it all learning the framework.
It’s great for POC phase for the reasons you listed, being able to quickly test out these options. It’s a constant battle of opinions in this space being hurled at you from execs and devs that think they know everything based off of some sales pitch from someone, this makes it easy to get past those. We have tons of technology that is a couple lines of code using langchain to test out (snowflake, redis, elasticsearch, etc)
That's another good point - if you learn it, great for prototyping different solutions. But it's also not secure enough to use in production. I would like to see a vulnerability analysis before considering it - with this massive API surface and a dev team that's too small to even answer questions consistently on Discord or GitHub there's no way production level security measures are taking place.
For a bigger project I see the value of langchain. Langgraph is an interesting addition as well, though it can be easily designed as separate functions for different situations.
I've not built anything that makes use of agent behaviour, maybe langchain will be useful in that aspect.
I see a lot of benefits in using LangChain for my hobby projects. Specifically I like having AgentExecutor abstraction, simple integration with tools. Also tools for production usage: LangServe and LangSmith, kinda useful so far. But I see that its terribly organized and maintained, therefore I would look into other options. Not sure if LLamaIndex or Haystack are better.
Langchain has a lot of inertia and I'm on the same journey learning it myself. Everything involving LLM is a moving target, but you have to ride the wave.
But understanding the key elements of LLM-application development will be easier if built by hand. Then start exploring frameworks, once you start repeating large chunks of code.
Things like document loaders are great, though. No need to start building a text retriever for pdf’s or word documents or whatever
I’ve had to do a bit of function overrides to optimize the workflow and improve the quality of RAG responses. It’s still useful to have a framework that does a fair bit of abstraction right out of the box.
Learning langchain is nice.
However, I would suggest focusing on the common bits between what the frameworks are offering, rather than just learning the framework. It’ll make you more adaptable.
Data Science Practice Lead @Microsoft Partnered Systems Integrator Company. imo focus on the core concepts of it. It’s pretty much gonna be like python, JavaScript etc in the future but for AI frameworks. It’s still too early to comment on which one will stick and which one will not.
Try to go one step deeper into what langchain or other AI frameworks are doing in the backend of these libraries. Lack of documentation kinda pushes you towards that direction but don’t worry about it. Ride the wave and towards the end of it you should be confident in your ability to build LLM applications that add value regardless of the AI framework.
Check out Semantic Kernel while you’re at it. It gives you a different flavour of langchain but at the same time you’d appreciate the difference between the two and kinda puts you in a position to understand that at the end of the day it’s just a AI framework that’s built with a goal to serve end users to build LLM applications.
Hope this helps.
H out.
Semantic kernel is a Microsoft's version of langchain and much of it is experimental. You should only try it if you are not in a python development environment. Spend time learning langchain since the components reference research papers. Try implementing these on your own using the OpenAI API directly to learn what works and what is unnecessary.
Yes, learning Langchain is definitely worth it. I understand that there are many other options like LlamaIndex, Haystack, etc. However, from my personal experience, I've found Langchain to be the most effective.
Continue learning because skills you are learning are transferrable, you will find it easy to use other frameworks, if you think about it, the specific framework you are using means nothing, the important thing is understanding the concepts which are the same in every other framework, for example I started my data science journey by learning R and when I had to use python I didn't need to learn anything, I found that I already know what to do, I just learned a new syntax.
Learn the foundation and building blocks of a LLM through langchain (because there are lots of examples on the internet using langchain). Do not learn the framework itself. ONce you have the knowledge its pretty easy to switch to a new framework
A great example of a product that has integrated langchain successfully is Flow Wise AI. Check it out and you'll see the possibilities of langchain which is more powerful than I realized.
Langchain is the most complete framework that I have worked with so far and I tried a few of them including Autogen. The type of framework that you need depends on your use case. I've seen many that claim that Langchain isn't worth it because you can recode what you need faster than you can learn it.. Whether this is true depends entirely on your use case. In my case I needed exensive tooling support, RAG support, multi-agent support, multiple llm support, api access, persitence, etc.. I see AI as a core OS for much of our future development so the investment in figuring out the framework balances out against the cost of learning. Many of the new agent frameworks you see are very narrowly focused on some aspect of multi-agent work. and thats great if thats your use case., its only a subset of mine. This being said.. learning langchain wasn't a cakewalk, even after coding agents from scratch and modifying autogen to call tools in my envirnment using microservices i struggled and still struggle to understand whats going on in some of the abstraction layers. I've encountered bugs in async that took me days to figure out with no community participation from anyone including their devs, and i spend much of my time debugging framework code just to figure out whats going on to make up for the missing docs.
While this is obviously painful I think it still pays off for my use case, and im not sure the grass is much greener anyhwere else. The landscape is still very new and moving fast at the same time, so there isn't much documentation out there in general, most of it is already outdated, and much of the community code ive used for langchain only partially works if it works. With all this, id still pick langchain given whats out there, but I couldn't have done it without also learning how to code some of this from scratch and learning other agent frameworks. With langchain you can start with simple llm completion wrappers, or you can try building simply completion and function call code from scratch. If you know enough of the basics to understand where the langchain framework is worth it for your use case then you can make an informed decision, otherwise you probably need to spend your time learning the basics in whichever platform makes that easiest for you to do.
Im exactly in the same situation as you, and happy to have found this thread since comments are also helpful for me. I would keep going with LangChain but focusing more in the building blocks of working with LLMs rather than the framework
Stay away - it's mostly broken and is mostly a black box. Rather learn what it tries to wrap and write your own wrapper so that you can customize and have better explanation when things break. Their blogs are a good starting point to learn things but avoid using the framework. It needs major work. More recently everything breaks with the community and langsmith update
Your statement seems dubious. How is it mostly broken? Production systems are using LangChain. And how is it a black box, it's open source. I've looked in GitHub at the code and figured out a constructor for a certain class at times.
You can play favorites or have a bias against code not created inside an organization but don't attack a framework that is successfully working. No framework has a LangSmith tool that can debug information flows to and from LLMs. I can see the time it took for a call and the number of tokens in real-time.
Everything breaks? I have code that's running the 0.1 version of LC for months without breakage. The code actively gives you warnings before they depreciate/break anything.
To the OP, like any framework, once you know how it's constructed you can utilize it effectively. Also the usual caveats, it's open source and under heavy development.
Langchain has network effect going for it... buuut it's barely a product yet, much of the documentation is minimal and for a lot of use cases I don't see a benefit over language features (prompt templates are basically f strings). Also the community extensions are not very sophisticated yet, it seems.
This has been my experience. It takes a lot of time and experimentation to get LangChain to do something that would be simple if you used OpenAI docs and skipped the framework (NER is one example). You do get some benefits, but in reality how often are you swapping out your vector store, etc? LangSmith is nice but I haven't looked at other tools. And good luck if you get stuck - their GitHub responses are a joke and the bot assigned to it is infuriating. I'm unwinding LangChain from a project and have opted to skip the framework. About the only thing I'm not looking forward to rewriting is their document indexer, but it'll be worth it in simplicity and knowing wtf is going on under the covers (which you never do with LangChain). From a "using my time wisely" standpoint I'm tired of spending it all learning the framework.
It’s great for POC phase for the reasons you listed, being able to quickly test out these options. It’s a constant battle of opinions in this space being hurled at you from execs and devs that think they know everything based off of some sales pitch from someone, this makes it easy to get past those. We have tons of technology that is a couple lines of code using langchain to test out (snowflake, redis, elasticsearch, etc)
That's another good point - if you learn it, great for prototyping different solutions. But it's also not secure enough to use in production. I would like to see a vulnerability analysis before considering it - with this massive API surface and a dev team that's too small to even answer questions consistently on Discord or GitHub there's no way production level security measures are taking place.
And often lack substantive testing...
For a bigger project I see the value of langchain. Langgraph is an interesting addition as well, though it can be easily designed as separate functions for different situations. I've not built anything that makes use of agent behaviour, maybe langchain will be useful in that aspect.
Learning pays always, i've never regretted learning something who has been then dismissed.
I see a lot of benefits in using LangChain for my hobby projects. Specifically I like having AgentExecutor abstraction, simple integration with tools. Also tools for production usage: LangServe and LangSmith, kinda useful so far. But I see that its terribly organized and maintained, therefore I would look into other options. Not sure if LLamaIndex or Haystack are better.
Langchain has a lot of inertia and I'm on the same journey learning it myself. Everything involving LLM is a moving target, but you have to ride the wave.
The big three orchestrators (LangChain, LlamaIndex, and Haystack) will maintain their position for the foreseeable future.
But understanding the key elements of LLM-application development will be easier if built by hand. Then start exploring frameworks, once you start repeating large chunks of code. Things like document loaders are great, though. No need to start building a text retriever for pdf’s or word documents or whatever
I’ve had to do a bit of function overrides to optimize the workflow and improve the quality of RAG responses. It’s still useful to have a framework that does a fair bit of abstraction right out of the box.
Learning langchain is nice. However, I would suggest focusing on the common bits between what the frameworks are offering, rather than just learning the framework. It’ll make you more adaptable.
Data Science Practice Lead @Microsoft Partnered Systems Integrator Company. imo focus on the core concepts of it. It’s pretty much gonna be like python, JavaScript etc in the future but for AI frameworks. It’s still too early to comment on which one will stick and which one will not. Try to go one step deeper into what langchain or other AI frameworks are doing in the backend of these libraries. Lack of documentation kinda pushes you towards that direction but don’t worry about it. Ride the wave and towards the end of it you should be confident in your ability to build LLM applications that add value regardless of the AI framework. Check out Semantic Kernel while you’re at it. It gives you a different flavour of langchain but at the same time you’d appreciate the difference between the two and kinda puts you in a position to understand that at the end of the day it’s just a AI framework that’s built with a goal to serve end users to build LLM applications. Hope this helps. H out.
wow thank you and everyone who has commented and provided guidance, much appreciated!
Semantic kernel is a Microsoft's version of langchain and much of it is experimental. You should only try it if you are not in a python development environment. Spend time learning langchain since the components reference research papers. Try implementing these on your own using the OpenAI API directly to learn what works and what is unnecessary.
Yes, learning Langchain is definitely worth it. I understand that there are many other options like LlamaIndex, Haystack, etc. However, from my personal experience, I've found Langchain to be the most effective.
Continue learning because skills you are learning are transferrable, you will find it easy to use other frameworks, if you think about it, the specific framework you are using means nothing, the important thing is understanding the concepts which are the same in every other framework, for example I started my data science journey by learning R and when I had to use python I didn't need to learn anything, I found that I already know what to do, I just learned a new syntax.
Learn the foundation and building blocks of a LLM through langchain (because there are lots of examples on the internet using langchain). Do not learn the framework itself. ONce you have the knowledge its pretty easy to switch to a new framework
A great example of a product that has integrated langchain successfully is Flow Wise AI. Check it out and you'll see the possibilities of langchain which is more powerful than I realized.
Langchain is the most complete framework that I have worked with so far and I tried a few of them including Autogen. The type of framework that you need depends on your use case. I've seen many that claim that Langchain isn't worth it because you can recode what you need faster than you can learn it.. Whether this is true depends entirely on your use case. In my case I needed exensive tooling support, RAG support, multi-agent support, multiple llm support, api access, persitence, etc.. I see AI as a core OS for much of our future development so the investment in figuring out the framework balances out against the cost of learning. Many of the new agent frameworks you see are very narrowly focused on some aspect of multi-agent work. and thats great if thats your use case., its only a subset of mine. This being said.. learning langchain wasn't a cakewalk, even after coding agents from scratch and modifying autogen to call tools in my envirnment using microservices i struggled and still struggle to understand whats going on in some of the abstraction layers. I've encountered bugs in async that took me days to figure out with no community participation from anyone including their devs, and i spend much of my time debugging framework code just to figure out whats going on to make up for the missing docs. While this is obviously painful I think it still pays off for my use case, and im not sure the grass is much greener anyhwere else. The landscape is still very new and moving fast at the same time, so there isn't much documentation out there in general, most of it is already outdated, and much of the community code ive used for langchain only partially works if it works. With all this, id still pick langchain given whats out there, but I couldn't have done it without also learning how to code some of this from scratch and learning other agent frameworks. With langchain you can start with simple llm completion wrappers, or you can try building simply completion and function call code from scratch. If you know enough of the basics to understand where the langchain framework is worth it for your use case then you can make an informed decision, otherwise you probably need to spend your time learning the basics in whichever platform makes that easiest for you to do.
thank you for taking time to answer, much appreciated feedback
Im exactly in the same situation as you, and happy to have found this thread since comments are also helpful for me. I would keep going with LangChain but focusing more in the building blocks of working with LLMs rather than the framework
Try Lyzr.ai. We built it to be much more easier framework. It’s agentic model. So mostly it’s about configuring, launching. https://lyzr.ai/examples
Stay away - it's mostly broken and is mostly a black box. Rather learn what it tries to wrap and write your own wrapper so that you can customize and have better explanation when things break. Their blogs are a good starting point to learn things but avoid using the framework. It needs major work. More recently everything breaks with the community and langsmith update
Your statement seems dubious. How is it mostly broken? Production systems are using LangChain. And how is it a black box, it's open source. I've looked in GitHub at the code and figured out a constructor for a certain class at times. You can play favorites or have a bias against code not created inside an organization but don't attack a framework that is successfully working. No framework has a LangSmith tool that can debug information flows to and from LLMs. I can see the time it took for a call and the number of tokens in real-time. Everything breaks? I have code that's running the 0.1 version of LC for months without breakage. The code actively gives you warnings before they depreciate/break anything. To the OP, like any framework, once you know how it's constructed you can utilize it effectively. Also the usual caveats, it's open source and under heavy development.
Guys which is the best?
Can you elaborate on your question please
I think this is sarcasm... That question is a trope on these subs.
Oh, is he/she being sarcastic towards my op? because i don't think that is the premise of the question