The letter

One letter a week, starting soon.

The week's signal, what shipped, what is worth running tonight, and the editor's note. No tracking, no ads, nothing else.

We keep your address, your language and the date you joined, nothing else. Every letter has a one-click unsubscribe link that deletes the record.

LangChain

25 stories

LangChainToolsHow We Optimized Browser Use for Deep Agents We compared seven browser toolsets for Deep Agents, picked Stagehand from Browserbase, and tuned it with LangSmith traces and evals. Here's what we learned.

11.03

LangChainToolsLangSmith BYOC is now available on AWS, Azure, and Google Cloud LangSmith BYOC now runs on AWS (GA), Azure and Google Cloud (Beta). Your traces, evals, and agent deployments stay in your cloud account while LangChain runs it.

11.03

LangChainToolsWhy human insight is a strategic advantage for building better agents Explore 3 principles for effective human-in-the-loop workflows and how human-review is a strategic imperative for production-ready agents.

11.03

LangChainToolsManaged Deep Agents: the fastest way to ship a production deep agent Run deep agents in production with durable execution, sandboxes, tool access, and LangSmith observability, without building the runtime yourself. Now in private…

11.02

LangChainToolsNew context engineering challenges in late 2026 LangChain CEO Harrison Chase shares insights into the biggest harness engineering challenges with the emergence of proactive agents.

11.02

LangChainToolsBrowser Use in Managed Deep Agents Managed Deep Agents now runs a browser for your agent, with tools tuned for context-efficient browser use and a handoff so you can sign in yourself.

11.02

LangChainToolsManaged Deep Agents v0.10: Browser Use and Async Subagents Managed Deep Agents v0.10 adds browser use, so agents can work on sites with no API and hand you the window to sign in, and async subagents for background work.

10.30

LangChainToolsHow We Built Our Design Agent How LangChain built Content Studio, an open-source design agent in Slack that makes on-brand videos, graphics and decks, and the harness that powers it.

10.29

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.

10.29

LangChainToolsThe Art of Loop Engineering Agents automate real-world work, but reliable performance requires more than a good model, it requires a carefully designed harness built for specific tasks.

10.29

LangChainToolsContext Engineering with Deep Agents: How To Remember Without Keeping Everything In Context Long-running agents degrade as context piles up. Learn how offloading, summarization, skills, subagents, and memory keep your agent's context window focused.

10.28

LangChainToolsHow to Build Proactive, Always-On Agents with the Schedules SDK in Managed Deep Agents Most proactive agents run on a simple cron. Learn what scheduled runs unlock, from memory to smart triage, and how to add them to Managed Deep Agents.

10.28

LangChainToolsHow to build evidence-grounded agent memory with OpenWiki and Graphs LangChain used OpenWiki, its open-source wiki compiler, and an evidence graph to build a persistent memory layer that preserves knowledge for agents to reuse across tasks. Learn how.

10.28

LangChainToolsThe five components of building a "company harness" Learn how LangChain built internal agents across marketing, sales, and engineering, and how they have completely changed how those teams work.

10.26

LangChainToolsContext Engineering (Agent Memory) for AI Agents Starts with Retrieval Agents waste time and context on clumsy searches, oversized results and stale data. Learn the retrieval techniques LangChain's Deep Agents harness uses to get the right context in.

10.26

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.

10.22

LangChainToolsHow Numan Built an Eval-First AI Health Coach with LangGraph and LangSmith How Numan built Nu, an AI health coach, on LangGraph and LangSmith with per-category evals, simulated conversations, and safety gates on a regulatory line.

10.19

LangChainToolsOctober 2026 LangChain Newsletter: Everything we shipped at Interrupt NYC & London What we shipped at Interrupt NYC and London: LangSmith AI Search, Engine v2, Trajectories, Custom Apps, Fine-Tuning, and Managed Deep Agents v0.9.

10.15

LangChainToolsThe Most Common Questions About Agent Evals, Answered Where to start with agent evals, offline vs online evals, evaluator types, LLM judges, benchmarks with Harbor, multi-turn evals, and keeping costs down.

10.15

LangChainToolsHow Orange Business Built Live Intelligence with LangChain and LangSmith Learn how Orange Business used LangChain and LangSmith to build Live Intelligence, a governed AI platform for agents, evals, observability, and production scale.

10.13

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.

10.13

LangChainToolsHow We Made Our Docs Test Themselves Stale code samples are a docs problem nobody prioritizes. Here's how we used the Deep Agents CLI to automate migrating inline code into testable, CI-ready…

10.13

LangChainToolsDeep Agents Deploy: an open alternative to Claude Managed Agents Today we’re launching Deep Agents deploy in beta. Deep Agents deploy is the fastest way to deploy a model agnostic, open source agent harness in a production ready way.

10.13

LangChainToolsOpen Models have crossed a threshold Open models like GLM-5 and MiniMax M2.7 now match closed frontier models on core agent tasks — file operations, tool use, and instruction following — at a fraction of the cost and latency.

10.13