✨ Today, we’re thrilled to announce ✨ - The general availability of LangSmith (no more waitlist!) - Our Series A fundraise led by Sequoia Capital - Our beautiful new homepage and brand We've worked hard over the past few months to add requested features and ensure LangSmith can operate at scale. We’re now confident in saying that it is the most complete platform for building production-grade LLM applications, whether or not you’re using LangChain. Learn more here: https://lnkd.in/gZxW8X_V and sign up here: https://lnkd.in/dwXZt_ZT Our series A round will give us the capital needed to grow our open source and platform offerings. Working with Sonya Huang, Romie Boyd, and the rest of the Sequoia team has been a privilege so far! https://lnkd.in/g8nw36_Z Finally, we’re excited to unveil our new homepage and brand. Dive into our new website at https://www.langchain.com/ to see the changes for yourself, explore the expanded resources, and discover what LangChain, LangSmith, and LangServe have to offer. PS — we’re hiring! Explore our careers page and reach out if you think you’re a fit for any of our open positions! https://lnkd.in/g9rXjrvC
About us
We're on a mission to make it easy to build the LLM apps of tomorrow, today. We build products that enable developers to go from an idea to working code in an afternoon and in the hands of users in days or weeks. We’re humbled to support over 50k companies who choose to build with LangChain. And we built LangSmith to support all stages of the AI engineering lifecycle, to get applications into production faster.
- Website
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langchain.com
External link for LangChain
- Industry
- Technology, Information and Internet
- Company size
- 11-50 employees
- Type
- Privately Held
Employees at LangChain
Updates
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⭐️Community Content Building a Serverless Application with AWS Lambda and Qdrant for Semantic Search Used LangChain and OpenAI’s embeddings to create vector representations of document chunks and store them in Qdrant, and then a streamlit UI https://lnkd.in/dZZepRfx
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🕸️Quickly Turn Unstructured Text Into a Knowledge Graph Neo4j has some fantastic resources for working with knowledge graph! In particular, the Graph Builder helps you build knowledge graphs - often the hardest part of getting started! https://lnkd.in/gK-EZAX9
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🧮Spreadsheet UX for agents One UX we've seen become more common is running agents inside a spreadsheet Provides a very natural way to run agents in batch! Companies like Matrices, Cognosys, and Clay are leading the charge here https://lnkd.in/gT_e3XAX
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⭐️Community Content Bridging the Gap: Generative AI Connects Video to Searchable Text The post covers how to think about and implement RAG over video content. We expect there to be a lot more multimodal applications over the next year! https://lnkd.in/gqn5eamt
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⭐️Community Content Web UI Generator Agent This agent, powered by an LLM, generates HTML and Tailwind CSS code based on a UI description from your design team Using LangGraph, contains a human-in-the-loop component for approving the code https://lnkd.in/g9RrNbBh
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⭐️Community Content LangChain Master Class For Beginners Containts +20 examples and uses LangChain V0.2. Over three hours long. Uses a variety of models, and the code is all OSS https://lnkd.in/gxbJrAkx
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Final blog on agent UXs (for now) Bit of a grab bag, covers: 🧮Spreadsheet (run agents in batch) 🖼️Generative UI (agent generates UI) 👩👧Collaborative (agent/humans working together in a Google doc) https://lnkd.in/gT_e3XAX
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We love seeing what Rakuten is building with OpenAI and LangChain products in order to elevate their AI solutions for merchants, hotels, retail stores, and more. ❤️ Rakuten designs, tests, and continuously improves their workflow using LangGraph to build reliable agents and LangSmith for an added layer of observability. On their journey to empower users with AI, the Rakuten engineering team has reduced customer service response times 10x for users, and now develops LLM apps in minutes instead of months. Check out how Rakuten uses LangChain products across their AI app lifecycle: https://lnkd.in/gRihZ-t8 Learn from the OpenAI x Rakuten case study: https://lnkd.in/gJ74Z_sm
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🪖LangGraph Engineer This is an alpha version of an agent that can help bootstrap LangGraph applications It will focus on creating the correct nodes and edges, but will not attempt to write the logic to fill in the nodes and edges - rather will leave that for you Try out the deployed version: https://lnkd.in/gSsnQjXC The agent consists of a few steps: 1. Converse with the user to gather all requirements 2. Write a draft 3. Run programatic checks against the generated draft (right now just checking that the response has the right format). If it fails, then go back to step 2. If it passes, then continue to step 4. 4. Run an LLM critique against the generated draft. If it fails, go back to step 2. If it passes, the continue to the end. Deployed on LangGraph Cloud, and made publicly accessible (the ability to do this is behind a feature flag, DM me if interested) Code: https://lnkd.in/giUgDrKc Youtube Walkthrough: https://lnkd.in/gJnMU5uR