Your agents can burn through $10k overnight before you notice. LangSmith LLM Gateway stops that. The platform where you already observe, evaluate, and deploy your agents now has a governance layer.
@langchain
-
Managed Deep Agents for Long-Horizon AI Tasks and Tool Use
By
–
Managed Deep Agents is built for agents that need to work over long time horizons, use tools, preserve context, and produce artifacts. A few examples of what teams are building: Support + triage agents Research agents Coding agents Data analysis agents Internal
-
LangChain Labs Announced for Continual Learning in Agents
By
–
ICYMI: We announced LangChain Labs at Interrupt. – An applied research effort focused on continual learning for agents – Early research partners: @NVIDIA
, @harvey
, @PrimeIntellect
, @Fireworks_AI
, @Baseten -
Fleet AI Agents Now Capable of Secure Code Execution and Analysis
By
–
Fleet agents can now securely write and run code. With computer use in LangSmith Fleet, agents get isolated execution environments. Analyze data, transform files, generate & write code, and run shell commands all within a secure virtual computer. Now in public beta.
-
Lyft Enhances AI Agent Development with LangGraph and LangSmith
By
–
@Lyft accelerated agent development from 6 months to just a few weeks with LangGraph and LangSmith. Hallucinations decreased by 20% AI Resolution rate up by 16%
-
Lyft’s AI Assist: How Ops Teams Ship Agents and Iterate with Prompts
By
–
Today, Ops teams, VoC leads, and PMs are now writing prompts, shipping agents, and iterating. No MLEs required. Read @Lyft
’s guest blog to see how they improved AI Assist, why they treated prompts like product specs rather than code comments, + what’s next. -
LangSmith Engine Automates AI Agent Improvement Cycles
By
–
Improving your agent has been a manual process of:
— LangChain (@LangChain) 27 mai 2026
✅ Reading traces
✅ Looking for patterns
✅ Writing evals
✅ Creating fixes
Now, LangSmith Engine runs that cycle for you. pic.twitter.com/YsFn37mtA3Improving your agent has been a manual process of: Reading traces Looking for patterns Writing evals Creating fixes Now, LangSmith Engine runs that cycle for you.
-
Deep Agents v0.6 Introduces Delta Channels for 100x Checkpoint Storage Reduction
By
–
Deep Agents v0.6 brings Delta channels, reducing checkpoint storage by up to 100x for long-running agents, without sacrificing observability or resilience. Here’s a 200-turn coding agent session.
Without Delta Channels: 5.3GB of checkpoint storage
With Delta Channels: 129mb -
Using Traces to Build Production Agent Evaluations
By
–
.
@AdamRLucek on how we use traces to build evals for production agents. -
LangChain Academy: Build AI Agents with LangSmith Fleet Essentials
By
–
LangChain Academy Course: LangSmith Fleet Essentials Learn how to build your own agents with LangSmith Fleet. Anyone can now build, use, and manage an agent fleet for complex daily tasks, without writing code. In this quickstart course, you'll learn how to build and improve