Agentic applications are making their way to production, powered by LangChain products⚡️ In September, we highlighted: 💠 Replit: Powered by LangGraph, the Replit Agent team used LangSmith to debug their agent interactions and monitor multi-turn conversations, See their workflow: https://lnkd.in/gEHuJBmp 💠 Tradestack: Their WhatsApp assistant, deployed on LangGraph Cloud, slashed quote creation time for trades businesses from hours to minutes. Read more: https://lnkd.in/ge6kC_65 💠 Paradigm: Transforming spreadsheets with AI, Paradigm built thousands of agents with LangChain and used LangSmith to understand LLM usage. See how: https://lnkd.in/gpVZgW6b
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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🔎Building a Retrieval Agent with LangGraph and Exa Agentic RAG is being used more and more to allow for more complex question/answering applications Exa is a search engine which can retrieve data in a format ready to be consumed by LLMs https://lnkd.in/genYqa_K
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🎩Service Desk Automation w/ LangGraph This application automates technical support interactions through a chat interface It integrates with various APIs and services to facilitate ticket creation, knowledge article management, and response generation https://lnkd.in/gQjk8QvQ
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🌐Web Search with DuckDuckGo and LangGraph This open source implementation tries to create a Perplexity-like experience using DuckDuckGo and LangGraph Check out the graph structure below! https://lnkd.in/gdwEMkxR
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💬Conversational Agent FastAPI Backend for a Conversational Agent using Cohere, (Azure) OpenAI, Langchain & Langgraph and Qdrant as VectorDB Goes over how to deploy it as well - so you can expose it to real users https://lnkd.in/gYMSXkX6
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🌲Building a RAG with Astro, FastAPI, SurrealDB and Llama 3.1 Open source models (and Llama 3.1 in particular) are good enough to build impressive RAG applications with Check out this example from our friends at Fireworks on how to do so https://lnkd.in/gw7fUEw7
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📚GPT Researcher as a LangChain Tool GPT Researcher is a complex multi-agent research agent This new repo sets it up so you can use it as a LangChain tool! This easily enables other agents to call it, further enabling multi-agent architectures https://lnkd.in/g5Wh9mRX
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🤖Simple Agentic RAG for Multi Vector stores with LangChain & LangGraph This community tutorial shows how to create an agent that can interact with multiple sources of data The agent chooses which vector store to query - a great example of agentic RAG https://lnkd.in/gv26zFxD
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🧮RagBuilder: RAG Optimization RagBuilder is a toolkit that helps you create optimal Production-ready RAG setup for your data automatically Does hyperparameter tuning on various RAG parameters (chunking strategy, chunk size, etc https://lnkd.in/g_igANZy
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Fall into AI with us at our Boston meetup on November 4th 🍂 Join us and the Elastic user group at Fidelity Labs for lightning talks on exciting AI topics, with plenty of refreshments and time to network. Interested in speaking? We're also having an open call for speakers 🎙️ Email us at hello@langchain.dev. RSVP to attend: https://lnkd.in/gV8EPuYB