Improving agents The old way: Manually reading traces, looking for patterns, writing evals, and creating fixes. The better way: Letting LangSmith Engine run that cycle for you
@langchain
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Open AI Models Gaining Traction Among AI Teams
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The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 nine months ago. The overall number of teams using open weights grew 3x. We’re seeing newer users choose open models at a
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LangSmith Gateway enforces spend limits and redacts PII before model requests
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LangSmith LLM Gateway lets you enforce spend limits and redacts PII before requests reach the model. Not after the fact.
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20-minute intro to Managed Deep Agents by Runkle and Moreira16
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.@sydneyrunkle + @VictorMoreira16 with a 20 minute intro to Managed Deep Agents.
— LangChain (@LangChain) 29 mai 2026
Watch the full Interrupt keynote: https://t.co/jDvoDG6wek pic.twitter.com/hcBAIcUHpL.
@sydneyrunkle + @VictorMoreira16 with a 20 minute intro to Managed Deep Agents. Watch the full Interrupt keynote: https://
youtu.be/LdQpoK2TzSo -
Deep Agents v0.6 Enhances Harness Profiles for Cost-Effective AI Model Performance
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Deep Agents v0.6 makes harness profiles a first-class abstraction. Now, you can get production-grade performance from models like @Kimi_Moonshot
, @Alibaba_Qwen
, and @DeepSeek_ai at 20x+ lower cost than closed frontier APIs. More on tuning: -
LangSmith LLM Gateway for Redacting Sensitive Data in LLM Requests
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Before LangSmith LLM Gateway: An agent processes a request that includes a SSN. It now sits in LLM provider logs, in trace data, + possibly in downstream systems that consumed the response. With LangSmith LLM Gateway: Data is redacted from requests before it hits a model or
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Deep Agents v0.6 Introduces ContextHubBackend for Agent File Management
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New in Deep Agents v0.6: ContextHubBackend A versioned home for the files that power agent behavior, backed by LangSmith Context Hub, enabling context improvements from one run to the next.
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New LangChain Academy Course on Scaling Deep Agent Deployment
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New LangChain Academy Course: Intro to LangSmith Deployment
— LangChain (@LangChain) 28 mai 2026
In this course, you’ll learn how to scale a single-user desktop Deep Agent all the way to a multi-tenant deployment running on managed, elastic infrastructure. pic.twitter.com/pFXZRhTZDHNew LangChain Academy Course: Intro to LangSmith Deployment In this course, you’ll learn how to scale a single-user desktop Deep Agent all the way to a multi-tenant deployment running on managed, elastic infrastructure.
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Managed Deep Agents: Features for Agent Creation and Workflow Support
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Managed Deep Agents lets you create a managed Deep Agent without standing up a custom agent server. Our runtime supports: Durable threads Streaming runs Checkpointing Human-in-the-loop workflows
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LangSmith Sandboxes Now Generally Available with Technical Details
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A TLDR on LangSmith Sandboxes: Hardware-virtualized microVM Kernel-isolated from your services + other sandboxes. Same SDK and API key as the rest of LangSmith Any framework or custom code Now GA