Knowledge catalogue
feel very aligned with our vision & Ronak + the awesome Trajectory team’s on practically tackling Continual Learning at scale 🚀 there’s a very good reason why teams are partly building “Observability
I cannot provide an accurate summary because the URL appears to be invalid or the tweet is no longer accessible (the status ID seems implausible for the current date). To create a reliable knowledge b
I watched the LangChain keynote expecting product releases. Got something better — the clearest breakdown I've heard of why most agents never make it past demo. One thesis lands hard. Here's what sepa
This post from LangChain's Harrison Chase contrasts traditional manual methods of improving AI agents (tracing execution, identifying patterns, writing evaluations, and implementing fixes) with a more
LangSmith LLM Gateway is a feature that enables proactive cost and privacy controls by enforcing spending limits and redacting personally identifiable information (PII) before API requests are sent to
llm spend is starting to get really high... a key part of our LLM gateway is spend visibility and spend control sign up for the private beta to try it out today! https://www.langchain.com/langsmith-ll
open models are having a moment! The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 nine months ago. T
open models will become a huge force for coding. The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 ni
Production traffic from frontier models is a golden data asset. If you can efficiently mine the traces, filter for quality, and fine-tune smaller models on them, you get specialized performance at a f
Start creating agents using everyday language with LangSmith Fleet. Learn how to build no-code agents for real work. Take our free LangChain Academy course today: https://academy.langchain.com/courses
The latest finding in the LangSmith Signal: Open Models are having a moment. 1 in 3 AI teams ran an open-weights model in April 2026, up from 1 in 5 nine months ago. The overall number of teams using
There are still a few spots left for @hwchase17 + @traversal_ai founder @_anish_agarwal's technical fireside chat in NYC on June 2nd. Learn how Traversal builds, ships and improves their agents. Enjoy
Tonight, I'm having dinner with a bunch of high-performing @Rippling team members (something we do every year). So I asked Rippling AI to give me a dossier on everyone I'm seated with, and the quality
a hot (cold at this point?) take that lead us to build this: every agent in the future will need a sandbox to connect to writing/executing code is not just for coding agents! is useful for all sorts o
A TLDR on LangSmith Sandboxes: ✅ Hardware-virtualized microVM ✅ Kernel-isolated from your services + other sandboxes. ✅ Same SDK and API key as the rest of LangSmith ✅ Any framework or custom code ✅ N
Almost everyone is building agent harness systems the wrong way. The default move: pick LangChain or LangGraph or the OpenAI Agents SDK, accept the loop, the tools, the memory, the orchestration, the
As agent harnesses become more standardized, we’re going to see a lot more “managed agent services” Managed Deep Agents lets you create a managed Deep Agent without standing up a custom agent server.
Harrison Chase shared a positive experience attending Boston Tech Week and highlighted interaction with Blitzy AI. The post captures participation in a technology industry event in Boston and engageme
Evals shape agent behavior. Every eval is a vector that shifts the behavior of your agentic system. More evals ≠ better agents. Instead, build targeted evals that reflect desired behaviors in producti
Governance should lead to more building, not less Agent development is so much more fun when people don't need to ration tokens because one runaway agent might blow up the bill Understand spend, cap t
join us for a behind the curtains look at LangSmith Engine (our agent that helps make your agent better) One of the best ways to learn what LangSmith Engine is capable of is to talk to the team that b
Managed Deep Agents lets you create a managed Deep Agent without standing up a custom agent server. Our runtime supports: ✅ Durable threads ✅ Streaming runs ✅ Checkpointing ✅ Human-in-the-loop workflo
Deep Agents v0.6 introduces ContextHubBackend, a versioned storage system for agent behavior files integrated with LangSmith Context Hub. This feature enables agents to access and manage the files and
One of the best ways to learn what LangSmith Engine is capable of is to talk to the team that built it. Join @bentannyhill for a live session on June 11th and see how your team can automate the agent
A highlight of new deepagents release is delta channels Drastically improves how we store checkpoints for agents Deep Agents v0.6 brings Delta channels, reducing checkpoint storage by up to 100x for l
Agent Lake = Agents + high-scale data processing Watch our full conversation with Geng Sng, Co-Founder & CTO of @cogent_security ⏯️ YouTube: https://www.youtube.com/watch?v=D6XWu54oG4g&feature=youtu.b
Completely agree, @hwchase17 ! This is the meta-level breakthrough we've all been waiting for. Self-optimizing loops finally feel production-ready because LangSmith Engine turns evaluation from a manu
completing the loop is paramount most unrecognized step in the development lifecycle is iteration on the process itself everyone is talking about self-optimizing loops in software & agents. but what d
Computer use in Fleet is now in public beta! You can now give your agents in Fleet access to secure virtual machines allowing them to own engineering tasks for you. Use these via Fleet agents in the U
This post highlights an open-source project built using LangChain and Langgraph that implements thoughtful memory management and organization features. The project demonstrates best practices for stru
Cost controls are one of the main benefits of langsmith LLM gateway We’re seeing in a bunch of use cases adoption is getting to a point where cost really starts to matter Your agents can burn through
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. With
Excellent talk on engine The interest and adoption of engine has been far greater than we anticipated If you don’t know what it is, this is a great starting point Improving your agent has been a manua
This post likely announces or discusses an open-source AI agent framework that incorporates memory and continual learning capabilities, enabling the system to retain information across interactions an
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
Fleet agents now come with a computer! They can write and execute code, which is helpful for general purpose tasks beyond coding Fleet agents can now securely write and run code. With computer use in
📹 How to manage context the right way with LangSmith Context Hub Context Hub gives your team and your agent a shared place to store, edit, version, and retrieve context from It can be used for skills,
LangSmith has introduced automated tools to streamline agent improvement, eliminating the manual workflow of reading execution traces, identifying patterns, writing evaluations, and implementing fixes
Boston Tech Week featured a session hosted by Mason Dry and Sydney Runkle with the Blitzy AI team focused on building deep agents. The event was promoted via Luma Events and appears to be an education
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 age
Managed deep agents is the easiest way to build and deploy long horizon agents Private preview, dm me if you want access Managed Deep Agents is built for agents that need to work over long time horizo
Model-Harness-Task fit! it’s clear that RL post-training produces a model-harness fit via tool shapes and prompting as models are trained with the harness in the loop. Mentioned this in a previous Lan
the future of Continual Learning will rely on building to systems to ingest, understand, & apply knowledge from Agent Traces at scale had a blast presenting LangSmith Engine with @bentannyhill to show
this is a fantastic example of a robust agentic memory system built on langgraph! there's 4 key parts 1. retrieval (rag) 2. storage, fragmented by memory type 3. reasoning* 4. learning (often called d
We launched Context Hub as a way to manage skills, AGENTS.md files, and other context files an agent might need You can easily use it as a virtual filesystem in deepagents See this video for more info
LangSmith's LLM Gateway is a cost control and monitoring tool designed to prevent unexpected high spending on LLM API calls by providing observability and likely rate-limiting or budget-cap features.
.@AdamRLucek on how we use traces to build evals for production agents. Trace data is literally worth its weight in gold these days, if you know what to do with it! As has been established, creating e
As an upcoming parent, I keep imagining all the tiny routines ahead 😊👼 One I am especially excited for: using AI for making up bedtime stories. So I built an app that creates one on demand: story, ill
Building agents (like software) is a deeply iterative process --> this is why we try to supply as much easy to use tooling and infra as possible so builders can start on their agent improvement journe
create_agent is the easiest way to get started building an agent! it provides an incredibly flexible interface for building agents designed around your use case! we just revamped the create_agent docs
everyone is talking about self-optimizing loops in software & agents. but what does that actually mean? in my mind, it's a system that observes it's own outputs, evaluates them, and uses that signal t
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 writ
langsmith engine is an agent that helps you spin the optimization loop for your own agents everyone is talking about self-optimizing loops in software & agents. but what does that actually mean? in my
Ridiculously cool open source example using @LangChain_JS The Multi-Modal children’s bedtime story generator is an excellent example of LangChain streaming UI & stream namespace subscriptions - great
the importance of traces! Trace data is literally worth its weight in gold these days, if you know what to do with it! As has been established, creating effective agents requires shipping early, obser
Trace data is literally worth its weight in gold these days, if you know what to do with it! As has been established, creating effective agents requires shipping early, observing behavior, and iterati
we just revamped the create_agent docs! the new agents page shows how to build a custom harness for your use case w/ create_agent as an easy entrypoint start w/ your prompt and tools, then add middlew
nice write up from the HuggingFace folks aggregating works on defining agents, harnesses, environments, RL, etc. The more we can roughly have a shared vocabulary the better…I still find it confusing (
Agents shouldn’t have direct visibility into env vars or credentials that can expose sensitive systems and data. Keeping secrets outside the agent’s context helps secure the env while still allowing a
“It is built in Rust and leverages the Apache DataFusion query engine” Any new database these days We built SmithDB: the database purpose built for agent observability workloads that now powers many p