SO MUCH THIS I believe that one of jev's greatest benefits to automation will come from resurrecting architecture best practices: state management, encapsulation, abstraction !!!! and combining them with ML's best practices: measure/evaluate, use calibration/uncertainty (adding screenshot b/c I don't know how to quote 2 posts 🤦)

LangGraph maps extremely well onto Jev. Not because it was built for decision models specifically, but because its core abstraction is already about putting decisions into state, then routing based on them. I wonder if over time we’ll think about LangGraph less as agent orchestration and more as just the way you build software with composable models.

today: come hang and work/tinker at Builders and Brews NYC with @tavilyai ! i'll be here all day with @LangChain engineers and our Education team and PMs! tinkering + coffee + matcha. 👉

jev and langgraph are a great combo! modeling agents as complex systems, with AI imbued in them, makes a ton of sense langgraph is the best way to model them as such, and using Jev inside langgraph to turbo charge all the small decisions that need to be made is awesome combo

Ok, this is insanely cool @langchain is launching a model-tuning service w/@FireworksAI_HQ & @baseten You can fine-tune your own custom model with the LangSmith Traces you already have - then compare & deploy! 👀 Hello SmithTune 🫡

we have seen many companies launch managed agent products recently. today, we released 𝚖𝚊𝚗𝚊𝚐𝚎𝚍-𝚍𝚎𝚎𝚙𝚊𝚐𝚎𝚗𝚝𝚜 0.8 I have been asked a few times why managed agent frameworks are useful in an era where "code is free". here is my take: generating code is easy, this is a fact. but as a developer, you still need to decide how your system should behave and apply those decisions consistently across your codebase: whose credentials does the agent use? what information can it share? what changes when another person joins the conversation? your auth, memory and channel integrations need to follow the same rules, including as you add features. this is hard to scale without a solid, opinionated foundation I do not think every team needs to solve these problems from first principles. there is value in building on decisions that other people have thought through, implemented and tested MDA is our managed service built on our open source Deep Agents harness. we build those decisions into managed primitives with sensible, configurable defaults, and handle the supporting infrastructure that is why I think Managed Deep Agents should be the default starting point for new agents. start with a foundation that we've thought through, and take on more of the system as your requirements demand it if you hit a limit with MDA, dm me. I want to understand what is missing and see if we can fix it. either way, MDA is built on our open source Deep Agents framework, so if you still need more control, you can move off MDA and use Deep Agents directly with your own integrations and infra re: diagram ~ this is one of those decisions we made: user memory is available in slack dms, but not in shared channels by default. fully configurable btw

Trajectories make it easier than ever to analyze long agent runs. They are now a first-class citizen within LangSmith. We also spent a lot of time ensuring our trajectory format handles things like deferred tool calling and context compaction well.

After lunch, here’s what’s up next at Interrupt: 🎙️How @airbnb built Agentic AI on LangGraph 🎙️Deploying mission-critical agents to production w/ @VictorMoreira16 🎙️LangSmith for Mental Health AI w/ @SpringHealth

Excited to announce LangSmith Fine-Tuning in partnership with @FireworksAI_HQ @baseten. Take your LangSmith data and post train open weight models seamlessly. Pumped about this one!

𝚖𝚊𝚗𝚊𝚐𝚎𝚍-𝚍𝚎𝚎𝚙𝚊𝚐𝚎𝚗𝚝𝚜 0.8 is here and I think it is the best thing since sliced bread things we shipped: 🧠 agent and user memory with access policies 🌐 connect webhooks with the new HTTP channel ✋ improved human-in-the-loop support 💬 Slack channels now support images and files 🔑 improved OAuth connection options 🔒 authenticate sandbox requests via a secure proxy 🔎 search the web with Parallel (thanks @p0) 📁 a runtime sandbox files API released for js and py!

Excited to partner with @p0 on Managed Deep Agents! You now have parallel built into your agents so your agents search the web fast and efficiently! @travers00 @hwchase17 @VictorMoreira16

jev is a new type of model - "decision models" there are a few open source alternatives, one is SemIf (based on qwen3.5) we're hosting SemIf and offering it for free through LangSmith Gateway for the next week!