🎨 AI-Powered Canvas Template Brandon from CopilotKit released a production template for AI canvas apps. Built with LangGraph for agent coordination, it delivers real-time UI-AI synchronization through a Python-Next.js stack. Watch the walkthrough: https://lnkd.in/gkDVTwQH
About us
LangChain provides the agent engineering platform and open source frameworks developers need to ship reliable agents fast.
- Website
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langchain.com
External link for LangChain
- Industry
- Technology, Information and Internet
- Company size
- 51-200 employees
- Type
- Privately Held
Products
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LangChain
Software Development Kits (SDK)
LangChain is the platform for building reliable agents. Our products power top engineering teams — from fast-growing startups like Loveable, Mercor, and Clay to global brands including AT&T, Home Depot, and Klarna.
Employees at LangChain
Updates
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🤖 🧠 Deep Agents Evolution A breakthrough in AI architecture enabling agents to scale from 15 to 500+ steps through advanced planning and memory systems, revolutionizing how AI handles complex tasks. Learn more about this evolution 🔍 https://lnkd.in/gcM82m2n
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📚🤖 Article Explainer An AI document analysis tool that breaks down complex technical articles using LangGraph's Swarm Architecture. The system uses multiple agents to provide interactive explanations and insights through natural language queries. Check it out on GitHub 🔍 https://lnkd.in/g6NjHi_z
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📚🔍 Event Deep Research An AI system that transforms historical research into structured timelines, automatically extracting and organizing biographical data from multiple sources into chronological JSON format. Check out the project 🎯 https://lnkd.in/gUY7PbJb
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🧠 LangGraph × cognee Integration cognee brings persistent memory to LangGraph agents, letting AI applications maintain context across sessions while seamlessly working with existing LangGraph features. Check out how to add memory to your agents 🔗 https://lnkd.in/gAUc2S_s
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Launch Week is right around the corner — and we're kicking it off in person! We'll be hosting meetups in San Francisco, Boston, and NYC to celebrate LangChain's 3rd birthday and share what's coming next during Launch Week. Come hang with the team, connect with our amazing community of builders shipping agents in production, and get the inside scoop on what's happening during Launch Week. Expect great food, drinks, and good conversation. It’s going to be an exciting evening! 📍 Pick your city and RSVP: 🌉 SF: https://luma.com/7baj9rx5 🦪 Boston: https://luma.com/135zbg4u 🗽 NYC: https://luma.com/f5jrv7t6 See you there 👋
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In this blog piece, you’ll learn why and how we built LangGraph for production agents. Building upon feedback from the super popular LangChain framework, we aimed to find the right abstraction for AI agents, and decided that was little to no abstraction at all. Instead, we focused on control and durability, and the core features needed to scale. https://lnkd.in/gaY9gHVH
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LangChain reposted this
FOUNDING ENGINEER: probably the most important and under-discussed role at an early stage startup. I sat down with founding engineers from LangChain (Nuno Campos), Perplexity (Nikhil Thota), and Stytch (Alex Zaldastani) to talk about what makes a good one, what they look for, how they make technical decisions, and more:
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Learn how to use LangSmith to debug your AI applications. 🛠️ In this video, we’ll show you how "Studio," our IDE for building agents, works, and how you can use it with any LangGraph agent you’ve built. You’ll also get everything you need to get started with Studio. https://lnkd.in/duhc72Qr
Getting Started with LangSmith (3/8): Debugging with Studio
https://www.youtube.com/
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LangChain reposted this
As your organization builds agentic AI, one architectural imperative stands out: 𝗵𝘂𝗺𝗮𝗻-𝗶𝗻-𝗹𝗼𝗼𝗽 𝗰𝗼𝗻𝘀𝗲𝗻𝘁 + 𝗮𝘂𝘁𝗵𝗼𝗿𝗶𝘇𝗮𝘁𝗶𝗼𝗻. Agents need dynamic access, not permanent keys. Langgraph enables exactly this human-in-loop consent flow: agents interrupt, ask for permission, then resume. 𝗪𝗵𝘆 𝗵𝘂𝗺𝗮𝗻-𝗶𝗻-𝗹𝗼𝗼𝗽 𝗶𝘀 𝗻𝗼𝗻-𝗻𝗲𝗴𝗼𝘁𝗶𝗮𝗯𝗹𝗲 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲: 🌱 Prevent privilege creep - agents only gain new permissions when a human explicitly approves 📜 Ensure auditability - every access request and consent step is logged, reviewed, and reportable 🛡️ Build governance confidence - executives, compliance, and security teams can see oversight in action 🔒 Least privilege + just-in-time - agents receive only the access they need, when they need it 𝗛𝗼𝘄 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 / 𝗟𝗮𝗻𝗴𝘀𝗺𝗶𝘁𝗵 𝗱𝗲𝗹𝗶𝘃𝗲𝗿: 1. You register OAuth providers (GitHub, internal APIs, etc.). 2. When the agent needs new access, it pauses and surfaces a consent URL for the human to approve. 3. On consent, the agent resumes with the scoped token - future flows are smoother thanks to token refresh logic. 4. Optionally, tokens can be human-scoped (shared across agents) rather than tied to a single agent. Check out link to blog on Agent Authorization and docs link on how to set up Agent Auth in Langgraph.
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