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LangChain Academy released a new free course on autonomous agent improvement using LangSmith Engine, covering the agent development lifecycle from identifying issues to monitoring regressions.
The blog argues that as AI generates code faster, understanding the combined execution becomes difficult, and proposes using graph engineering with a compiler to create deterministic orchestrators.
LangChain's Interrupt NYC conference on September 24 will feature Apollo's talk on migrating their AI Assistant to a production multi-agent system using LangGraph, alongside other industry speakers.
LangChain announces a resource for improving AI agents.
OpenWiki is a CLI tool from LangChain that writes and maintains agent wikis for codebases or personal knowledge, supporting all common LLM providers.
LangChain rebuilt its data stack around an AI agent that handles ~40x the request volume of its 3-person data team, enabling self-serve analysis and shifting the team's focus to models, context, and guardrails.
Announces a new book 'RAG from First Principles' by Jia Huang, a code-first guide to engineering retrieval-augmented generation systems using Python, LangChain, and LlamaIndex.
A tutorial by Darsh on how to trace and monitor local AI agents using LangSmith, LangChain, Ollama, and Qwen, enabling inspection of model and tool calls, latency, and usage.
A tutorial showing how to serve a multi-user local AI agent as a REST API using FastAPI and Streamlit, built with LangChain, Ollama, and Qwen.
Matt Dancho shares his 2026 AI data science stack including free tools like Python, Pandas, Scikit Learn, LangChain, and LangGraph, plus a low-cost OpenAI API, and promotes a free live teaching session.
Jensen Huang and Harrison Chase discuss why companies need open agent systems and introduce the NemoClaw blueprint for LangChain Deep Agents.
An inside look at LangChain from developer nickhollon10.
LangChain released an Eval Engineering Skill that automatically generates executable Harbor evaluations by mapping agent repositories and production traces, with an iterative user interview process to refine evals.
LangChain highlights IO-HMM from GetCandidly, a design that separates user behavior (observable signals) from agent behavior (controllable inputs) in conversation turns.
OpenWiki now supports Google's Gemini AI Studio and Vertex AI, including the newly released Gemini 3.6 Flash and 3.5 Flash Lite models, thanks to community contributions.
This tutorial demonstrates how to build a repeatable evaluation harness for AI agents using rule-based checks and an LLM-as-a-judge, leveraging LangChain, Ollama, and Qwen to test local agents with clear pass/fail results.
LangChain released a tracing plugin that converts every Cursor AI agent session into a structured trace in LangSmith, including model runs, tool calls, and nested subagent work, part of a series comparing coding-agent traces.
LangChain announces a summer AMA series on the agent development lifecycle using LangSmith, covering building, deploying, monitoring, improving, and governing agents.
The developer created a self-hosted visual builder for LangChain/LangGraph agents and is requesting feedback from the community.
LangChain introduces IssueBench, a detailed evaluation suite for Engine, a continual learning agent in LangSmith that automatically improves agents based on traces.