Skip to content
LangChain Blog
  • Website
  • Docs
  • Case Studies
  • Harrison's In the Loop Series
  • Try LangSmith
Sign in Subscribe
Autonomous context compression

Autonomous context compression

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

4 min read

Featured

How we built LangChain’s GTM Agent

How we built LangChain’s GTM Agent

10 min read
Evaluating Skills

Evaluating Skills

7 min read
LangSmith CLI & Skills

LangSmith CLI & Skills

2 min read
You don’t know what your agent will do until it’s in production

You don’t know what your agent will do until it’s in production

11 min read
The Anatomy of an Agent Harness

The Anatomy of an Agent Harness

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

9 min read
How Coding Agents Are Reshaping Engineering, Product and Design

How Coding Agents Are Reshaping Engineering, Product and Design

EPD (Engineering, Product, and Design) at software company is about creating good software. Separate roles exist, but the end goal is functional software that solves

Harrison's In the Loop Series 8 min read
How we built LangChain’s GTM Agent

How 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

10 min read
Evaluating Skills

Evaluating Skills

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

7 min read
LangSmith CLI & Skills

LangSmith CLI & Skills

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

2 min read
LangChain Skills

LangChain Skills

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

2 min read
You don’t know what your agent will do until it’s in production

You don’t know what your agent will do until it’s in production

You can't monitor agents like traditional software. Inputs are infinite, behavior is non-deterministic, and quality lives in the conversations themselves. This article explains what to monitor, how to scale evaluation, and how production traces become the foundation for continuous improvement.

11 min read
How we built Agent Builder’s memory system

How we built Agent Builder’s memory system

A key part of Agent Builder is its memory system. In this article we cover our rationale for prioritizing a memory system, technical details of how we built it, learnings from building the memory system, what the memory system enables, and discuss future work.

9 min read
Agent Observability Powers Agent Evaluation

Agent Observability Powers Agent Evaluation

You can't build reliable agents without understanding how they reason, and you can't validate improvements without systematic evaluation.

13 min read
How to Use Memory in Agent Builder

How to Use Memory in Agent Builder

By Jacob Talbot Agent Builder gets better the more you use it because it remembers your feedback. Every correction you make, preference you share, and

4 min read
New in Agent Builder: all new agent chat, file uploads + tool registry

New in Agent Builder: all new agent chat, file uploads + tool registry

Today, we're expanding what you can do with LangSmith Agent Builder. It’s an big update built around a simple idea: working with

4 min read
monday Service + LangSmith: Building a Code-First Evaluation Strategy from Day 1

monday Service + LangSmith: Building a Code-First Evaluation Strategy from Day 1

Learn how monday Service developed an eval-driven development framework for their customer-facing service agents.

Case Studies 8 min read

Page 1 of 31

Load More Something went wrong with loading more posts

© LangChain Blog 2026