LangChain raises $125M to build the platform for agent engineering We raised $125M at a $1.25B valuation to build the platform for agent engineering.
Reflections on Three Years of Building LangChain by Harrison Chase Almost exactly 3 years ago, I pushed the first lines of code to langchain as an open source package. There was no
Securing your agents with authentication and authorization Agents can take action which makes proper authentication and authorization critical. Read on for how to implement and evolve agent auth.
Not Another Workflow Builder By Harrison Chase One of the most common requests we’ve gotten from day zero of LangChain has been a visual workflow builder. We never
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
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
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
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,
Introducing Open SWE: An Open-Source Asynchronous Coding Agent The use of AI in software engineering has evolved over the past two years. It started as autocomplete, then went to a copilot in an
Deep Agents Using an LLM to call tools in a loop is the simplest form of an agent. This architecture, however, can yield agents that are “shallow”
