Knowledge catalogue
LangChain released Deep Agents v0.6, described as their largest release to date. The announcement was made by Harrison Chase on X (formerly Twitter), indicating a significant update to the Deep Agents
In case you're not following the @LangChain_OSS team, here's who to follow! @masondrxy and @Vtrivedy10 are doing an awesome push on harness profiles @huntlovell doing cool stuff w code interpreter / R
LangSmith Engine automates the full agent fix loop — detecting failures, diagnosing causes and drafting PRs. But multi-model enterprises say a neutral observability layer still wins. http://venturebea
LLM apps fail in ways normal logs can’t explain. Same input, different outputs. Subtle drift. Silent hallucinations. That’s the problem space LangSmith by @LangChain was built for: observability + eva
The post discusses positive features and improvements included in version 0.6 of DeepAgents, with Harrison Chase praising a write-up by Sydney that explains these updates. This likely covers new capab
@masondrxy and @Vtrivedy10 are doing an awesome push on harness profiles @huntlovell doing cool stuff w code interpreter / REPL @bromann leading the charge on streaming our OSS team is stacked! lots o
Nebius and @LangChain have partnered to integrate Nebius Token Factory with LangChain's Deep Agents. The integration, combined with LangChain's existing Tavily integration, gives teams building on Lan
Today in AI Engineering (May 17) • Nous Research ships Hermes Agent v0.14.0: Grok subs, Codex runtime, Windows beta • LangSmith Engine releases trace issue clustering, drafts PRs and evals from prod t
when evaluating long running agents, all of your evals don't need to be end to end. i'm working on a proper blog about this, but in our evals for our agents that run for 30-60 minutes, we have two set
This post outlines a development roadmap for an AI agent system, prioritizing initial version building with internal testing (dogfooding), implementing tracing and observability for data collection, a
Harness profiles! https://docs.langchain.com/oss/python/deepagents/profiles I like what Langchain recently released in their deepagents harness, which is an adapter to modify the syntax of primitive f
LangSmith Engine feels like the missing CI/CD loop for AI agents , automatically detecting failures, clustering issues, proposing fixes, and generating evals from production traces. Agent engineering
LangSmith Engine is how we’re spinning the always-on, self-improvement loop for every agent - Tracing is on for every single agent - Purpose built infra with SmithDB to handle data at agent scale (mor
This is one of the first real continual learning systems for agents in production. Not just monitoring. Actually getting better over time. LangSmith Engine is how we’re spinning the always-on, self-im
This post likely discusses how developers often create unnecessarily complex evaluation environments when testing AI agents, and suggests simplifying these setups for more effective testing. The advic
I like what Langchain recently released in their deepagents harness, which is an adapter to modify the syntax of primitive file system commands depending on the model Claude likes “Bash”, Gemini likes
just got back from my second langchain interrupt conf and honestly it's probably the best AI engineering conference right now. what makes it different is that it's deeply practical. less 'AI will chan
“Dependably for LLM agent failures” @hwchase17 Started on this and finding it awesome; also LangSmith engine sparked an idea. The 'Dependabot like for LLM agent failures'. LangSmith Engine gives you t
This appears to be a humorous exchange from X/Twitter creator Harrison Chase referencing train cars named after AI/ML tools and services (Devin, Langsmith, and Mistral), likely making a joke about the
Excited to see SmithDB announcement at Interrupt, our purpose-built distributed database for agent observability! SmithDB is built on top object storage, written in Rust and leverages Apache DataFusio
Had the honor of sharing the stage with the one and only @sydneyrunkle at Interrupt 2026 to talk about Deep Agents. Sign-up for the Managed Deep Agents waitlist today to get early access! https://www.
@hwchase17 Started on this and finding it awesome; also LangSmith engine sparked an idea. The 'Dependabot like for LLM agent failures'. LangSmith Engine gives you the smoke detector. The natural next
ICYMI: 1️⃣ LangSmith Engine 2️⃣ SmithDB 3️⃣ Managed Deep Agents 4️⃣ LangSmith Sandboxes: Now Generally Available 5️⃣ Context Hub 6️⃣ LangSmith LLM Gateway 7️⃣ Sandboxes, Prebuilt agents, + free model
it almost never makes sense to use real api's for your evals. with how good coding agents have become, i will pretty much always opt to create a fake mock server for my agent to hit. the workflow is u
it’s kind of awesome that continual learning has now come to the agent & harness level. i remember when online learning was the big craze in traditional ml a couple of years ago, that transitioned int
Key ideas I picked up at the @LangChain conference this week: 'AI engineering is data science. Look at your data.' — @sh_reya & @HamelHusain 'Data strategy *before* agents.' — @AndrewYNg 'Coding agent
@LangChain’s Interrupt 2026 conference was a blast!! such a pleasure to with @VictorMoreira16 about deep agents! ICYMI: we just dropped v0.6, which is focused on performance at the model, harness, and
SmithDB lets you see traces in seconds instead of minutes. This quote from our friends at @cogent_security says a lot: “At Cogent, our background agents can produce a huge volume of traces all at once
This quote from our friends at @cogent_security says a lot: “At Cogent, our background agents can produce a huge volume of traces all at once. We need live observability into those systems, and SmithD
turns out that building evals is super super challenging even now. i thought a lot of it was table stakes but turns out it has only become harder since agents are now more complex than ever! going to
Wow! What a week! Lots of new things to learn - new ideas to form The complete Agent Development Life Cycle 🎯 ❤️ SF but can’t wait to get home and back to building! Thanks for the warm welcome and fun
Abridge: 100M+ medical conversations, real-time prior auth, and the clinical intelligence layer https://www.latent.space/p/abridge @AbridgeHQ is building the clinical intelligence layer for healthcare
Agent observability is a means to an end: making your agent better. But observability and evals tools have traditionally failed to connect traces to meaningful actions. Agent engineering teams are lef
big launches today! 🚀Launching: LangSmith Engine LangSmith Engine is an agent that sits on top of your traces It runs in the background and automatically identifies issues It then proactively suggests
deepagents v0.6 is our biggest release yet!!! it’s all about perf - at the model layer w harness profiles, agent layer w code interpreter, and at scale w streaming and delta channels context hub backe
DID SOMEONE SAY FREE TOKENS IN FLEET???? Yes it's true. Fleet now has a built in model powered by @FireworksAI_HQ that's free for all Developer & Plus plan users. Try it out today (or before @hwchase1
everything we build at LangChain is provider/model agnostic for these reasons! our harness is open source and easily customizable! On topic: there is a mistake I saw large companies make in late 2025/
great agents need great infrastructure. proud to be @LangChain's Deep Agents Inference Partner at Interrupt 2026 in SF. great to spend time with builders at our day 1 happy hour. excited for day 2 tod
Great move. If you’re building agents, so much time is spent engineering your way through traces. And then comes the hard to rapidly iterate and figure out the changes needed. 🚀Launching: LangSmith En
i recently left @Bloomberg after two great years. incredibly lucky to have joined @LangChain in time to work on this 🚀 Spend less time on triaging Ship fixes faster Catch regressions earlier Introduci
I was going to say LangChain Labs is the most exciting thing we’ve announced recently, but with so many launches it’s hard to justify… but still incredibly thrilled to see this get started!! Checkout
This post references a LangChain Interrupt event where Brace Sproul and Caroline shared insights about applied AI leadership, with the author suggesting that effective heads of applied AI need signifi
Introducing LangSmith LLM Gateway: The runtime governance layer for your agents. 💸 Enforce cost limits 🔒 Detect PII ✅ Act on violations …All without leaving LangSmith. Now in Private Beta https://www.
Jake will be working with a lot of folks on continual learning - if that’s interesting, reach out to him! An exciting time to be working on Continual Learning and everything around it - if you’re work
LangChain Labs is a new applied research initiative announced by LangChain focused on advancing continual learning capabilities. The effort represents the company's commitment to developing systems th
LangChain 在 Interrupt 大会上发布了底层数据库 SmithDB 和自动化排障引擎 LangSmith Engine。 Agent 运行会产生海量 trace（执行轨迹），把旧数据库撑到了瓶颈。新底座 SmithDB 放弃了本地磁盘，全面转向对象存储，将核心查询速度拉高了 15 倍。 底座换新后，LangSmith Engine 顺势接管了查 Bug 的体力活。它在后台持续监控生
LangChain Labs has launched as a new initiative from the LangChain team. The announcement was made by Harrison Chase, the creator and lead developer of LangChain, on X (formerly Twitter). LangChain La
LangSmith Engine, 6 months later imo, reading and analyzing long agent traces is a huge bottleneck and obstacle for progress (especially as these systems grow in time horizon). Imagine reading a langs
LangSmith Engine feels like the missing piece for reliable agents. Clustering failures and suggesting targeted fixes turns raw traces into actionable improvements. This is how we scale agent developme
LangSmith Engine is a phase shift because traces are no longer just records to be manually inspected, they’re now the catalyst for recursive agent self-improvement Engine looks at your traces, finds w
LangSmith Fleet has introduced a free model option powered by Fireworks AI for users on Developer and Plus plans, lowering the barrier to entry for getting started with the platform. This update makes
Model. Harness. Context. The 3 main components of agents. As you build more agents, context increasingly lives AGENTS.md, skills, policies, examples, + generated research files. Context needs its own
okay okay, hillclimbing agent in the background. love it. great job @hwchase17 @Vtrivedy10 and team. 🚀Launching: LangSmith Engine LangSmith Engine is an agent that sits on top of your traces It runs i
our engineers had an awesome time attending Interrupt:2026 by @LangChain learning about observability, agent evals, and best practices from the industry leaders! can't wait to see what they cook up wi
right now Engine only works with LangSmith traces but it's super easy to trace to LangSmith, we accept OTEL and integrate with 30+ frameworks! if you want to migrate to LangSmith to try out Engine, we
SmithDB is a feat of engineering. A new database for a new data shape Huge work from @ankush_gola11 and team We built SmithDB: the database purpose built for agent observability workloads that now pow
SmithDB is the perfect example of how far performance can be pushed by having full control over the storage layer. The DataFusion + @vortexdotdev stack seems to be emerging as THE way to build next ge
suuuuper excited to be collaborating with the excellent LangChain Labs team on this effort prod agent tracing is the seed that lets you close the loop for continual learning. too much data gets collec
The Building AI Agents with MongoDB and LangGraph Skill Badge can be yours on May 28th. Here's how you earn it 👉 Join our LIVE workshop, build a real agent with MongoDB, Claude Sonnet, and LangGraph,
the future intelligent agents will learn and grow with your team over ultra long timescales 🕰️ that research direction centers around Continual Learning LangSmith is the data layer that helps us under