Langsmith taking over San Francisco!!! @LangChain
→ View original post on X — @langchain, 2026-04-06 22:04 UTC
@langchain
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LangChain announces Agent Infrastructure track at AI Council 2026
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Very excited to talk about file systems in agents at the @AICouncilConf this May! Grab tickets now before they sell out: AI Council (@AICouncilConf) Shipping an agent is the easy part. Keeping it alive while you ship 10 more? That's where things fall apart. At AI Council this year we're dedicating an entire track to the people actually solving this. Presenting the "Agent Infrastructure" lineup, curated by @tristanzajonc (Co-Founder & CEO of @continual_ai): → @BraceSproul, Head of Applied AI at @LangChain : “The File System Is All You Need” → @thesephist, Head of AI at @ThriveCapital: “Context Engineering at the Frontier” → @felipehuici, Co-Founder & CEO at @UnikraftCloud: “Running Millions of (Millisecond) AI Sandboxes without Breaking the Piggy Bank” → @glcst, CEO at @tursodatabase: “Agents will need trillions of databases. Let's give it to them!” → @MeryemArik9, CEO at @Doubleword_: “Inference for Async Agents in Production” → @jacopotagliabue, Founder at @Bauplan_labs: “Forgiveness, Not Permission: Running Agents On Production Data” → @TheParminderS , Co-Founder & CEO at Redscope AI: “Building Durable, Long-Running Autonomous Agents” Big thanks to Tristan for pulling these cracked engineers together to share what’s actually working in production. See you at AI Council 2026 in SF, May 12-14! 🎟️ aicouncil.com — https://nitter.net/AICouncilConf/status/2041254177058496866#m
→ View original post on X — @langchain, 2026-04-06 20:58 UTC
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LangSmith Cost Alerting: Monitor Agent Spending in Production
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a year ago, it was hard enough to build useful agents that most companies didn't have cost issues. that's changed a lot just from the start of this year! use langsmith to track agent costs and alert when anything unexpected happens LangChain (@LangChain) Introducing Cost Alerting in LangSmith 💸 More and more agents are making it to production, and costs are increasing dramatically. Use LangSmith to set configurable alerts on total cost, so you know right away when your agents are spending more than they should. Docs: docs.langchain.com/langsmith… Sign up: smith.langchain.com/ — https://nitter.net/LangChain/status/2041236011439771648#m
→ View original post on X — @langchain, 2026-04-06 19:28 UTC
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LangSmith Introduces Cost Alerting for Production Agents
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Introducing Cost Alerting in LangSmith 💸 More and more agents are making it to production, and costs are increasing dramatically. Use LangSmith to set configurable alerts on total cost, so you know right away when your agents are spending more than they should. Docs: docs.langchain.com/langsmith… Sign up: smith.langchain.com/
→ View original post on X — @langchain, 2026-04-06 19:26 UTC
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Day 3: Travel Agent with MCP, State Management, and Multi-Agent Systems
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Day 3 of becoming 100xdevs: – make a travel agent with mcp – learn about the state – learn about runtime context – making multiagents – tracking sub-agents from Langsmith Akash (@akashcorex) Day 2 of becoming 100xdevs: – LangSmith tracking of each agent – how to create MCP – run MCP locally and use it – create a travel agent with mcp + ai — https://nitter.net/akashcorex/status/2040872532816154958#m
→ View original post on X — @langchain, 2026-04-06 18:58 UTC
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LangChain Builds Community Middleware Page, Seeks Agent Middleware Use Cases
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we're building out a community middleware page for @LangChain, and we need your help growing it. agent middleware is one of the most powerful building blocks we've shipped. what are you building with it? docs.langchain.com/oss/pytho…
→ View original post on X — @langchain, 2026-04-06 17:17 UTC
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Interrupt 2026: LangChain’s Agent Conference Returns May 13-14
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🚀 Interrupt sold out last year, so don't wait: May 13-14 in San Francisco. https://t.co/fPMwmmdSxf
— LangChain (@LangChain) 6 avril 2026
Last year, hundreds of builders packed a room to share what's actually working in agent production. This year, we're going bigger.@hwchase17, @AndrewYNg, @cj_mongodb, are… pic.twitter.com/qXIrAEzlnu🚀 Interrupt sold out last year, so don't wait: May 13-14 in San Francisco. interrupt.langchain.com/ Last year, hundreds of builders packed a room to share what's actually working in agent production. This year, we're going bigger. @hwchase17, @AndrewYNg, @cj_mongodb, are headlining. Alongside them: real-world sessions from teams shipping agents today, time with LangChain engineers, and a pioneering AI builder community. If you're working on agents or thinking about it, this is two days with the people furthest along.
→ View original post on X — @langchain, 2026-04-06 16:59 UTC
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Community Developer Builds Claude Compaction Engine for LangChain
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the langchain community is so awesome claude code's source leaked last week and @IeloEmanuele immediately built claude's compaction engine as @LangChain middleware drop this into your agents/deepagents today! github.com/emanueleielo/comp…
→ View original post on X — @langchain, 2026-04-06 11:59 UTC
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Five middleware patterns for customizing agent harness engineering
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did a big series on using @langchain's middleware to customize your agent harness last week icymi, here's a quick blog explaining 5 different patterns for harness engineering! blog.langchain.com/how-middl…
→ View original post on X — @langchain, 2026-04-06 11:54 UTC
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Context Layer: The Core of Enterprise AI Stack Architecture
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One of the core things we’re going to have to contend with in AI is that even the most advanced models in the word can’t have all the relevant knowledge needed to be useful, because everyone has different use-cases and ways they’ve designed their workflows. Perhaps most importantly, as you get into the enterprise, everyone has entirely different access levels to corporate knowledge and information. Continual learning at the model layer, even at a single enterprise level, is near impossible because every user knows and has access to something different than another user. This isn’t like coding where by and large most developers can access all the relevant stuff to their job. On a single banking team, bankers have entirely different sets of documents they’re ever allowed to see. Sanitizing this is hard and having the model keep secrets is impossible. This is why the context layer is going to always be the core part of the AI stack for applied use cases to turn general models turn into useful agents. Can’t fight the physics on this one. Harrison Chase (@hwchase17) x.com/i/article/204046441296… — https://nitter.net/hwchase17/status/2040467997022884194#m
→ View original post on X — @langchain, 2026-04-05 04:40 UTC