LangChain Blog
blog.langchain.dev/
How to turn Claude Code into a domain specific coding agent
Authored by: Aliyan Ishfaq Coding agents are great at writing code that uses popular libraries on which LLMs have been heavily trained on. But point them to a custom library, a new version of a library, an internal API, or a niche framework – and they’re not so great. That’
Monte Carlo: Building Data + AI Observability Agents with LangGraph and LangSmith
See how Monte Carlo built its AI Troubleshooting Agent on LangGraph and debugged with LangSmith to help data teams resolve issues faster
Agent Middleware
LangChain has had agent abstractions for nearly three years. There are now probably 100s of agent frameworks with the same core abstraction. They all suffer from the same downsides that the original LangChain agents suffered from: they do not give the developer enough control over context engineering when needed, leading
Building LangGraph: Designing an Agent Runtime from first principles
In this blog piece, you’ll learn why and how we built LangGraph for production agents—focusing on control, durability, and the core features needed to scale.
Standard message content
TLDR: We’ve introduced a new view of message content that standardizes reasoning, citations, server-side tool calls, and other modern LLM features across providers. This makes it easier to build applications that are agnostic of the inference provider, while taking advantage of the latest features of each. This feature is
LangChain & LangGraph 1.0 alpha releases
Today we are announcing alpha releases of v1.0 for langgraph and langchain, in both Python and JS. LangGraph is a low-level agent orchestration framework, giving developers durable execution and fine-grained control to run complex agentic systems in production. LangChain helps developers ship AI features fast with standardized model abstractions