u/Meher_Nolan

Ik this doesn't sound like the best question to ask in LangChain's own subreddit. But a few months ago it felt like every LLM app stack discussion led back to LangChain somehow. Now I mostly see people talking about OpenClaw, Hermes, MCP workflows, or fully custom orchestration stacks.

Curious about people still heavily using LangChain:

> what made you stay?

> are you using LangSmith or some Open-source alternative for observability/evals?

Feels like the ecosystem shifted insanely fast.

I’ve been going down a rabbit hole of Git-related tooling lately and it’s kinda wild how broad this space actually is now.

There’s everything from terminal utilities and diff viewers to automation workflows, repo analysis tools, visualizers, experimental interfaces, etc. Curious what interesting projects people here have come across recently.

WebContainers honestly feel like one of the most interesting things to happen to web tooling in a while, but outside online IDEs it doesn’t feel like the ecosystem has fully leaned into the possibilities yet.

I recently stumbled onto an open-source project clawless using browser runtimes for AI/dev workflows, and it made me realize how much stuff can already be done fully inside the browser now with WASM sandboxing and modern runtimes.

Meanwhile most newer tooling still seems very backend-heavy and cloud-dependent.

Feels like browser-native workflows could solve a lot of annoying problems around setup, portability, isolation, etc. Especially for dev tools and automation workflows.

Is this mostly a technical limitation thing, or is the space just still maturing?

Maybe I’m missing something, but browser runtimes seem way more capable than people realize. With stuff like web containers and WASM sandboxing, we can already run well capable environments fully inside the browser. I saw an open source project recently that used this well, and it made me think about how much we're still stuck on this everything needs a heavy backend kinda mindset for AI tools.

It feels like there's massive potential here for portable, sandboxed tooling yet it's still being treated as a niche.

Are there major technical limitations here that I’m not seeing, or is this space just still early?

Feels like production RAG stacks are getting less LangChain-centric lately. A few months ago LangChain felt like the default answer for almost every LLM/RAG workflow discussion. Now I mostly see people moving toward LangGraph, MCP-style workflows, lighter custom orchestration, or fully in-house pipelines.

For people still using LangChain heavily in production RAG systems:

- what made you stay with it?

- did LangGraph replace most of your old chain setups?

- are you using LangSmith or Open-source tooling for observability/evals?

Feels like a few months ago it was basically the definitive answer for every LLM app stack discussion.

Now I mostly see people talking about LangGraph, Openclaw, Hermes, MCP stuff, or just rolling their own orchestration completely.

Curious to know the opinion of people still using LangChain for their whole workflow,

* What made you stick with it?

* Are you using LangSmith or some open-source alternative for it?

Share your projects and what tools have you used to make them. Let's see how bonkers they are.

Not talking about the LLM itself but about the memory layer on top. Curious if there are solid open source projects out there that people have actually tried and stuck with. Which one just worked for you?

Not talking about the LLM itself but about the memory layer on top. There are quite a few out there now, open source ones and proprietary frameworks. Curious what people have actually tried and stuck with. Which one just worked for you?

The India AI scene keeps bouncing between these two takes. "We're going to lead the world" and "we're just a services country." We keep having the same two arguments about India and AI and neither one is going anywhere.

Compute is the obvious problem. India has under 5% of global AI optimized compute. The US and China together have over 70%. The IndiaAI Mission is supposed to help but 64% of Indian AI founders still don't have a sustainable compute strategy. There's a gap between what the government announces and what builders can actually access.

Where India is actually ahead is multilingual stuff. Sarvam's Vision OCR scored 84.3% on olmOCR-Bench, ahead of biggies like Gemini and ChatGPT. Twenty-two languages at scale is genuinely hard and our teams came the closest in solving it.

But the production readiness gap might be a less obvious problem but is indeed more important. Eval pipelines, audit trails, rollbacks, RBAC, compliance, issues like these are consistently underbaked. Risk averse capital pushed most startups toward application layers instead of the infrastructure that makes agents reliable in production. We ship prototypes, not production systems. That's what's holding things back.

Are we actually solving the production gap? Wonder what people here think about this.

Trying to set up local RAG that is fully offline with just my own notes and stuff. Not a demo thing, I actually want to query my stuff without it leaving the machine.

The embedding model and how you chunk documents matter way more than the LLM. Benchmarks are useless for real personal retrieval.

Fifty docs? Works fine. Hit five hundred and it degrades in ways that are hard to notice until you stop trusting the results. Hierarchical indexing helps but then you're maintaining an indexing strategy instead of using the tool.

Still not sure whether LlamaIndex is worth it for a single user local setup versus just writing it yourself. What are you guys running day to day?

Spent way too long thinking complexity in my agent was a me problem. Bad prompts, bad memory setup, bad tool definitions. Kept tweaking Langchain configs trying to fix behavior I couldn't even properly observe.

Turns out half the problem was I had no idea what was actually happening under the hood. The framework was doing too much quietly. Swapped it out for raw API calls and a dead simple Python class. Suddenly failures were obvious, loops were traceable, tool calls made sense. But nothing was changed about the agent logic, I just had the visibility this time.

Now, the magic is less but I have more control. Boring code that I understand beats clever abstractions I don't every single time in prod.

So I just got to know that micheal movie will be in flat ratio everywhere. Since melody is not playing Micheal, will jagadamba be our best option? Are subs confirmed?

So Inorbit already open aipoindhi kabatti AAA ki emaina expected opening date ichara? Epiq luxon or at least hdr by barco vache chance undha?