LangChain Blog

Previewing Interrupt 2026: Agents at Enterprise Scale

This year, we're doing it again. Interrupt 2026 is May 13–14 at The Midway in San Francisco, and the lineup, the format, and the scale have all leveled up.

3 min read

Featured

The Anatomy of an Agent Harness

9 min read

How we built LangChain’s GTM Agent

10 min read

Deep 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

4 min read

Human judgment in the agent improvement loop

AI agents work best when they reflect the knowledge and judgment your team has built over time. Some of that is institutional knowledge that’s already documented and easy for an agent to use as-is. But most great organizations also rely on tacit knowledge that lives inside their employees’ minds.

11 min read

Better Harness: A Recipe for Harness Hill-Climbing with Evals

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

8 min read

Deep Agents v0.5

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

4 min read

Arcade.dev tools now in LangSmith Fleet

Arcade is the MCP runtime for production agents, delivering secure agent authorization, reliable tools, and governance. This integration gives your agents access to Arcade’s collection of 7,500+ agent-optimized tools through a single secure gateway.

3 min read

Continual learning for AI agents

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:

Harrison's In the Loop Series 4 min read

How My Agents Self-Heal in Production

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 an agent to open a PR with a fix, with no manual intervention needed until review time.

Engineering 6 min read

Open Models have crossed a threshold

💡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

6 min read

March 2026: LangChain Newsletter

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 Builder).

By LangChain 4 min read

Announcing the LangChain + MongoDB Partnership: The AI Agent Stack That Runs On The Database You Already Trust

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

Partner Post 6 min read

Agent Evaluation Readiness Checklist

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

17 min read

How Kensho built a multi-agent framework with LangGraph to solve trusted financial data retrieval

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

Case Studies 4 min read