📰 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

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

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

 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.

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.

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

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

By Vivek Trivedy TLDR: Agent = Model + Harness. Harness engineering is how we build systems around models to turn them into work engines. The model contains the

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

By Robert Xu Recently at LangChain we’ve been building skills to help coding agents like Codex, Claude Code, and Deep Agents CLI work with our ecosystem: namely

We’re releasing a CLI along with our first set of skills to give AI coding agents expertise in the LangSmith ecosystem. This includes adding tracing to agents,

We’re releasing our first set of skills to give AI coding agents expertise in the open source LangChain ecosystem. This includes building agents with LangChain

February gave us only twenty-eight days, but we made them count. From new Agent Builder capabilities to production monitoring insights, here's your monthly roun