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LangChainToolsBuilding Production Agents with Jev and LangGraph See how LangGraph orchestrates Jev, TypeSafe AI's decision model, to build faster, cheaper production agents.

LangChainToolsLangSmith Custom Apps: Build custom interfaces around your agent data LangSmith Custom Apps lets you build the interface you want with your LangSmith data, publish it into your workspace, and skip the hosting, auth, and permissions work. Learn more.

LangChainToolsNew in LangSmith: Engine v2, Managed Deep Agents, Fine-Tuning, and more LangChain announced new updates to LangSmith. Updates include Engine v2 with red teaming and automatic testing, a new version of Managed Deep Agents, trajectories and more.

LangChainToolsNew in LangSmith Engine: red teaming and automated testing LangSmith Engine now includes Red Teaming to proactively detect agent issues and automated agent testing. Learn more about the Engine v2 release.

LangChainToolsIntroducing LangSmith Fine-Tuning LangChain introduces LangSmith Fine-Tuning and SmithTune, a CLI built for post-training models. Train specialized models without building data pipelines by hand.

LangChainToolsManaged Deep Agents delivers a better user experience for agents in production Managed Deep Agents is the simplest way to build, deploy, and run agents in production. The 0.8 release adds support for user-owned credentials, user-level memory, HTTP channels, file transfer in…

LangChainToolsTrajectories now in LangSmith: A readable view of every agent session Trajectories in LangSmith provide a conversational view of an agent session. Trajectories make trace data easy to navigate and speed up debugging for long-running agents.

LangChainToolsWhat Is Jev? A Guide to TypeSafe AI’s System One Model What is Jev? Learn how TypeSafe AI’s System One model makes fast, structured decisions, where it fits in the agent loop, and how to use Jev with LangChain

LangChainToolsThe Reliability Layer for Healthcare AI: Common LangSmith Use Cases See how LangSmith helps healthcare AI teams turn clinical review into reusable evaluators, datasets, and release gates for safer AI in production.

LangChainToolsJev is now available in LangSmith Evals Use Jev as a judge for LangSmith evals to evaluate agent traces with faster, cheaper structured feedback across production runs, datasets, and regression tests.

LangChainToolsCan Jev Be a Better Agent Evaluator? We tested using Jev-as-a-Judge against LLM judges on accuracy, repeatability, latency, and cost to see whether System One models could offer a new approach to agent evaluation.

LangChainToolsHow Included Health Built Federated Healthcare Agents with LangGraph and Deep Agents See how Included Health used Deep Agents, LangGraph, and LangSmith to build Dot, a federated healthcare navigation agent with human handoff and clinical oversight.

LangChainToolsBuilding an Agent Harness for Life Sciences: Introducing Deep Life Sci Deep Life Sci is LangChain's open source agentic assistant for clinical and lab scientists. It pulls from 600K+ ClinicalTrials.gov studies, 29M PubMed abstracts, and 12M PubMed Central full-text…

LangChainToolsScaling Agents in Healthcare & Life Sciences: Lessons from Madrigal Pharmaceuticals, Abridge, and Vizient Agent programs in healthcare and life sciences are being built under a different set of constraints than those in most industries. There’s plenty of upside if the constraints can be resolved.

LangChainToolsHow We Built LangChain’s Paid Media Agent How LangChain built a paid media agent to analyze campaign performance, optimize ads, propose changes, and turn marketing data into action.

LangChainToolsScaling Agents in Europe & The Middle East: Lessons from Schneider Electric, Vodafone, and monday.com A guide on scaling agents in Europe & the Middle East to see how Schneider Electric, Vodafone, and monday.com are approaching production AI at scale, from establishing shared agent platforms and…

LangChainToolsHow Credit Genie keeps codebase docs fresh with OpenWiki See how Credit Genie uses OpenWiki to automate repo documentation, reduce tribal knowledge, and give engineers and coding agents searchable codebase context.

LangChainToolsConnections: managed credentials and per-caller identity for Managed Deep Agents Learn how Connections in Managed Deep Agents securely manage credentials, support per-user OAuth, and let agents act with each caller’s identity.

LangChainToolsHow we built LangChain’s GTM Agent Learn how we built a GTM agent that increased lead conversion by 250% while saving each sales rep 40 hours per month

LangChainToolsOrganizing Context in a Multi-Agent Harness Learn how context modes in deepagents help subagents fork a supervisor's context or start isolated — for faster, cheaper, more focused multi-agent work.

LangChainToolsMCP in LangChain: Stateless Protocol, Elicitation, and More! MCP support now lives in langchain.mcp, built on FastMCP for the 2026-07-28 spec, with elicitation handled as a LangGraph interrupt and tool lists cached.

LangChainToolsAugust 2026: LangChain Newsletter — Managed Deep Agents, LLM Gateway, and More Managed Deep Agents and LLM Gateway hit public beta, plus Deep Agents v0.7, Tuned Evaluators, Bring Your Own Cloud on AWS, and LangSmith Engine upgrades.

LangChainToolsLangSmith LLM Gateway: Runtime Controls for Agents LangSmith LLM Gateway is in public beta: spend caps, rate limits, model fallbacks and PII redaction for production agents, without provider lock-in.

LangChainToolsHow LangSmith and LangChain OSS Help You Meet EU AI Act Requirements The EU AI Act compliance deadline is August 2, 2026. Learn what the EU AI Act requires, and how LangSmith and LangChain OSS products help you meet each requirement.

On the calendar

予定 · Conferences and launch events, this week's stories from each corner of the industry, and what its press is covering — in English

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This week, from that corner the newest stories from the corner of the industry you picked

The wire their own posts, translated

How code moves in 2026 from the industry's own reports

Sources

Fetched every three hours, linked to the original

About

The name is the button.

An agent writes the code now, and a human thumb hovers over Accept all a hundred times a day. The honest question of this decade is what, exactly, you just accepted. So I read the diff.

Eight years as a backend engineer in Stockholm, back in Tokyo since 2023, and I still ship something small most nights, usually badly. My one rule: the only real review of a model is the thing you built with it. That is why there is no leaderboard on this site.

Around a hundred and forty sources come through — the labs' own posts, the papers, the tools, the runtimes and languages nobody is watching yet, the chip under the demo, and the newsletters people actually forward. What is moving gets counted so the week reads at a glance, and every few days I explain one mechanism in plain words.

A story here is a headline, a short excerpt, one image and the link to whoever wrote it. We never reproduce the post; the images belong to their authors. A post written in Japanese appears in Japanese, exactly as it was published, and the link goes to the original.

— Yuki Halvorsen, Tokyo