Managed Deep Agents is now in Private Beta ICYMI: It’s managed, model-agnostic infra for deep agents you can deploy with a single line of code. A quick thread on what you get out of the box
@langchain
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LangSmith Sandboxes GA: Agents Get Isolated Runtime Environment
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ICYMI: LangSmith Sandboxes are GA Agents get a real filesystem, shell, and package manager. Isolated from your infra. Works with Deep Agents, Open SWE, or your own code. Auth with the same API key you already have. No new runtime to build or manage.
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Deep Agents integrates with NebiusAI Token Factory for agent workloads
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Deep Agents now integrates with @nebiusai Token Factory. Now, you can run agent workloads on production-grade AI infrastructure with open-source models, dedicated endpoints, real-time search, and full control over cost and data.
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LangSmith Engine automates agent debugging and fixes
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TLDR: LangSmith Engine is built to accelerate the agent development lifecycle.
— LangChain (@LangChain) 19 mai 2026
1️⃣ Works autonomously to find patterns in your agents failures
2️⃣ Drafts code fixes
3️⃣ Proposes eval coverage
💻 Go beyond sifting through manual traces → https://t.co/wojV1EKd40 pic.twitter.com/0qr9JucjjCTLDR: LangSmith Engine is built to accelerate the agent development lifecycle. Works autonomously to find patterns in your agents failures Drafts code fixes Proposes eval coverage Go beyond sifting through manual traces → https://
langchain.com/blog/introduci
ng-langsmith-engine
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LangChain Engine to Automate Agent Error-to-Fix Workflow
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Most teams building agents are tracing their agent and reviewing the outputs, but the flow from error identification to merged fix is manual and slow.
— LangChain (@LangChain) 19 mai 2026
Engine is built to close that gap. https://t.co/wojV1EKd40 pic.twitter.com/2HwAvmLp3BMost teams building agents are tracing their agent and reviewing the outputs, but the flow from error identification to merged fix is manual and slow. Engine is built to close that gap. https://
langchain.com/blog/introduci
ng-langsmith-engine
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Event: Deploying Long-Running AI Agents at Toronto Tech Week
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We’ll be @ #TorontoTechWeek on 5/27 w/ • Haider Zaidi, Deployed Engineer @LangChain • Jasen Mackie, Senior Principal AI Engineer @Questrade RSVP: https://
luma.com/axp0tudp We'll walk through what it actually takes to deploy long-running agents & the runtime capabilities that -
LangSmith introduces SmithDB to improve trace data
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The next phase of LangSmith is more than faster trace loading. We’re making trace data more useful. SmithDB is the foundational layer that makes this possible. A deeper dive from Co-Founder @ankush_gola11
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SmithDB: purpose-built data layer for agent observability
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ICYMI: SmithDB is our purpose-built data layer for agent observability + eval workloads.
— LangChain (@LangChain) 18 mai 2026
Supporting increasingly complex query patterns at low latency, over large traces, with self-hosting + multi-cloud requirements needs a fundamentally new architecture.
That’s why we built… pic.twitter.com/BQ4J1sxc23ICYMI: SmithDB is our purpose-built data layer for agent observability + eval workloads. Supporting increasingly complex query patterns at low latency, over large traces, with self-hosting + multi-cloud requirements needs a fundamentally new architecture. That’s why we built
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LangChain Launches Fleet for Building AI Agents
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Doesn't get much better than free LLMs and sandboxes in everyone's favorite agent builder!
— LangChain (@LangChain) 14 mai 2026
Use Fleet today: https://t.co/C3DrTJjc9y https://t.co/S6WNC2CynODoesn't get much better than free LLMs and sandboxes in everyone's favorite agent builder! Use Fleet today: http://
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LangChain Labs launches focused on continual learning
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JUST IN: We're launching LangChain Labs. A new applied research effort focused on Continual Learning.