📰 LangChain Blog

Agent harnesses are becoming the dominant way to build agents, and they are not going anywhere. These harnesses are intimately tied to agent memory. If you used

By Vivek Trivedy, Product Manager  TL;DR: We can build better agents by building better harnesses. But to autonomously build a “better” harness, we need a stro

TL;DR: We’ve released new minor versions of deepagents & deepagentsjs , featuring async (non-blocking) subagents, expanded multi-modal filesystem support, and m

Arcade is the MCP runtime for production agents, delivering secure agent authorization, reliable tools, and governance. This integration gives your agents acces

Most discussions of continual learning in AI focus on one thing: updating model weights. But for AI agents, learning can happen at three distinct layers: the mo

I built a self-healing deployment pipeline for our GTM Agent. After every deploy, it detects regressions, triages whether the change caused them, and kicks off

 TL;DR: 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 — a

It feels like spring has sprung here, and so has a new NVIDIA integration, ticket sales for Interrupt 2026, and announcing LangSmith Fleet (formerly Agent Build

Build production AI agents on MongoDB Atlas — with vector search, persistent memory, natural-language querying, and end-to-end observability built in.

A practical checklist for agent evaluation: error analysis, dataset construction, grader design, offline & online evals, and production readiness.

Discover how Kensho, S&P Global’s AI innovation engine, leveraged LangGraph to create its Grounding framework–a unified agentic access layer solving fragmented

 TLDR: The best agent evals directly measure an agent behavior we care about. Here's how we source data, create metrics, and run well-scoped, targeted experime

Agent harnesses are what help build an agent, they connect an LLM to its environment and let it do things. When you’re building an agent, it’s likely you’ll wan

Fleet now supports shareable skills, so you equip agents across your team with knowledge for specialized tasks.

Moda uses a multi-agent system built on Deep Agents and traced through LangSmith to let non-designers create and iterate on professional-grade visuals.

If you're attending Google Cloud Next 2026 in Las Vegas this year and working on agent development, here's what we have planned. Visit Us at Booth #5006 We'll b

LangSmith Fleet introduces two types of agent authorization: Assistants, which use the end user's own credentials, and Claws, which use a fixed set of credentia

Agent Builder is now Fleet: a central place for all of your teams to build, use, and manage agents across the enterprise.

Debugging agents is different from debugging anything else you've built. Traces run hundreds of steps deep, prompts span thousands of lines, and when something

Spin up a sandbox in a single line of code with the LangSmith SDK. Now in Private Preview.

Built on Deep Agents and LangGraph, Open SWE provides the core architectural components for internal coding agents.

Comprehensive agent engineering platform combined with NVIDIA AI enables enterprises to build, deploy, and monitor production-grade AI agents at scale Press Rel

We’re excited to introduce the deploy cli, a new set of commands within the langgraph-cli package that makes it simple to deploy and manage agents directly from

TL;DR: We've added a tool to the Deep Agents SDK (Python) and CLI that allows models to compress their own context windows at opportune times. Motivation Contex