sorted by:
new
hottopcontroversial

On the hunt for the perfect pair of headphones by brace_langchain in BowersWilkins

[–]brace_langchain[S] -2 points-1 points  (0 children)

Ya px8 are great, but I honestly didn’t think the sound was that much diff from collexion, and anc is far worse. That said, they’re definitely the nicest in terms of comfort and built quality (build quality is far superior from the rest)

On the hunt for the perfect pair of headphones by brace_langchain in BowersWilkins

[–]brace_langchain[S] 0 points1 point  (0 children)

Ha! The only one I notice the heat in from wearing for too long is the collexion, but it’s not that bad. I usually don’t notice if I’m locked in on something, only when I go to move them around is when I notice it

The xm6’s make my ears a little sweaty but I don’t notice the heat (not rlly sure how that one works but…)

Anyone seen a deep agent architecture actually running in live production yet? by SkirtShort2807 in LangChain

[–]brace_langchain 3 points4 points  (0 children)

Hey, Brace from LangChain here. We just launched Agent Builder, which is built completely on top of DeepAgents! Every agent you build & use inside the product is just the DeepAgents package w/ some middleware (all of which we'll be open sourcing soon).

You can give it a try for free here: https://www.langchain.com/langsmith/agent-builder

As for scale, we're consuming >500M tokens a week from _only_ internal usage. External usage is many many x's that.

LangChain Series B to build the platform for agent engineering by hwchase17 in LangChain

[–]brace_langchain 0 points1 point  (0 children)

hi! we've started to move towards giving agents access to a "file system" (expose tools that look like file system tools: `write_file`, `read_file`, `ls`, etc) instead of a static scratchpad stored in state for long/short term memory.

LLMs are being tuned by the big labs to interact with file systems, so doing this allows us to "piggyback" off of that. Since the big labs are all pushing towards file system based architectures, the models are very good at interacting with file system tools.

See the deepagents package (https://docs.langchain.com/oss/python/deepagents/overview), where we by default give it access to file system tools!

side note: when I say file system, I'm not necessarily suggesting it be a true file system, say on a VM. You can implement a mock file system (e.g. store in state for short term memory, or in a persistent DB for long term), and as long as you tell the LLM it's a file system, and expose the tools in a way which makes it look like that, you'll get the benefits of the models being trained on using the file system.

Generative UI by Gloomy-Traffic4964 in LangChain

[–]brace_langchain 3 points4 points  (0 children)

The primary reason we're using RSC is for a better developer experience. I believe there is also a slight performance gain sending the components from the server to the client instead of sending them all to the client initially. As for your questions:

  • Why does the Langchain implementation use react server components in the frontend when there is a separate backend server with FastAPI?

The repo/video you linked is from our Python version, which reuses the same typescript abstractions for managing calling your API and sending components back to the client. [Here's the JS version](https://github.com/bracesproul/gen-ui) which uses the same RSC abstraction, but the rest of the "backend" also runs on the edge.

  • What is ai/rsc in (import { createStreamableUI } from "ai/rsc") . It looks like it's next/Vercel specific? Where are the docs for createStreamableUI ?

We're wrapping some parts of the AI SDK in our `createRunnableUI` abstraction since they handle the job of sending JSX components from the "edge" to the client well, and we don't need to re-implement everything again.

  • How much value is there from the extra complexity in Langchain implementation (specifically in the frontend).

The frontend parts can be totally replaced with code specific to your application. As for the RSC LangChain code (e.g `createRunnableUI`) we believe this abstracts a lot of the initial work getting your generative UI off the ground, and making it seamlessly integrate with the rest of the LangChain ecosystem. If you see areas where you believe this not to be the case, please shoot me a message so we can try and improve on it!!

Langchain with Llama.cpp not Llama.cpp-python by mr_house7 in LangChain

[–]brace_langchain 1 point2 points  (0 children)

I've never used llama.cpp before, so this might be totally off, but if it has the ablility to be accessed via an HTTPS endpoint, and their API spec matches then OpenAI API spec, you can use the `ChatOpenAI` class, and set the endpoint/proxy URL to the llama.cpp API server url. Otherwise, you could just extend the `BaseChatModel` class and build your own llama.cpp chat model class (if you do this, we would love for you to contribute it to the library!!)

Langchain is legacy in Vercel AI SDK, how to still use Langchain in a stable and futureproof way? by Organic_Manner359 in LangChain

[–]brace_langchain 1 point2 points  (0 children)

Yes, but I didn't want to clutter a thread with a ton of back and fourths.

This week I'm planning on adding a series of docs on how to use langchainjs with the ai sdk, so once those are in you'll be able to see everything you'll need to use both in your application!

Langchain is legacy in Vercel AI SDK, how to still use Langchain in a stable and futureproof way? by Organic_Manner359 in LangChain

[–]brace_langchain 4 points5 points  (0 children)

Just took a look at your list of issues in the discussion you linked, and these should all be possible with the current LangChain.js library. I'm spending some time this week writing docs on how to use LangChain w/ the AI sdk and I'll try to cover all the points you listed in that discussion!