LangChain

Own your intelligence.

Makers of LangSmith, @LangChain_OSS, and @LangChain_JS.

[LangChain @LangChain]

Continual learning is about to get boring, in a good way.

@willcb from @primeintellect took the stage at our recent Learning Loop event to make the case.

If you're thinking about how continual learning fits into the way your team already ships software, this full 21-minute session is a good place to start.

[Amal Irgashev @amal_irgashev]

taste is the judgment that separates work that's correct from work that's good

models can already write html, css, chart specs and animation code. what they don't have is your context, so they reach for the same familiar patterns (i.e distributional convergence)

our design agent at @LangChain did every ai slop pattern imaginable: edge-bar cards, a label above every title, words · like · this, and dot-grid wallpaper behind everything

so we updated the system so that anything measurable is a check in code and judgment lives in skills and a design wiki. design principles like one focal point per frame, a guardrail for each slop pattern, and the reasoning behind every note our designers give

anything someone teaches it in slack gets saved to memory in the same turn

we also tried prebuilding taste with 115 UI components so the model had fewer decisions to make but it just it kept repeating the same layouts and animations...

so when opus 5.5 was released, we actually deleted most of those rules and kept the principles. more variety, way less repetition

we are going deeper on all of it live oct 28 in our GTM Engineering series with a special guest, our goated brand designer, @shreyatgkr

register here 👇
https://t.co/X5RtBh87n7

[LangChain @LangChain]

We're hiring!

Check out our open roles @ https://t.co/xJOcm9utQU

[Amal Irgashev @amal_irgashev]

i think the best part of building internal agents is that you get to sit with people who've done this stuff for years and just... absorb

at @LangChain i get to work with some of the best GTM people i've ever met. people who've run huge launches, built massive brands, closed hundreds of deals and know exactly what great looks like

a big part of the job is basically an apprenticeship. you shadow them, interview them, figure out how they make the calls they make, and translate that judgment into context agents can use - an insanely fast feedback loop

and it won't work right away. at first it's this slightly alien thing in slack they have to babysit, and it can take hundreds of iterations before people really start trusting it

but then one day it just clicks. someone asks for something new and it comes back exactly how they would've done it, with zero notes. from then on it's just part of their day, and you get to watch it change the way they work

and the most fascinating part is that the agent ends up being the best written record of how the business actually works

honestly one of the most rewarding jobs i've had

[LangChain @LangChain]

🎓 New from the LangChain Academy team

How agents remember to retrieve the right info at the right time ⤵️

[Jess Weng @jess_weng18]

I present to you 🥁🥁🥁
Context Engineering: The Prequel (Retrieval)

https://t.co/BdX1jWbyrE

[LangChain @LangChain]

The best of Interrupt, now on demand.

Access our archive today and start watching our sessions from Interrupt San Francisco, NYC, and London. https://t.co/hL8WUWHbjG

[LangChain @LangChain]

Next stop on the LangSmith Roadshow? Chicago!

Hear about the current state of agent development, and get ready for two hands-on workshops focused on Deep Agents and LangSmith Engine.

Interested in attending? RSVP today!
https://t.co/Zxiteh1QFS

[LangChain @LangChain]

We have several LangChain-led and community-led meetups coming up! RSVP and bring a friend!

🇩🇪 10/27 Munich
🇺🇸 10/29 Washington, DC
🇲🇽 10/29 Mexico City
🇦🇪 10/31 Dubai
🇨🇦 11/4 Toronto
🇺🇸 11/5 San Francisco
🇬🇧 11/12 London
🇺🇸 11/12 Boston
🇺🇸 11/18 Seattle
🇸🇪 11/25 Stockholm
🇩🇪 12/4 Hamburg

RSVP: https://t.co/sillsOrXfh

[LangChain @LangChain]

Join us, @ExtendHQ, and @tryramp this Thursday for a private screening of You Can See Everything in San Francisco.

Seats are limited, save yours now! https://t.co/HVymrwE3CP

[Caspar @caspar_br]

Content Studio is a truly epic example of a company harness built on Managed Deep Agents. It is multiplayer by nature, I work with it in a public channel so others can weigh in on the design process

[LangChain @LangChain]

On October 28th, go behind the scenes of our design agent.

We’ll demo the agent live, walk through how we built it, and share what we’ve learned from using an agent for creative production across the company.

💻 RSVP: https://t.co/ryXCZVSikO

[Amal Irgashev @amal_irgashev]

our design agent at @LangChain has shipped 3,000+ assets since august, and posts with its work pulled ~4.5M views on socials (it made this promo too)

it’s become the design brain for the whole org. decks, diagrams, ad creatives, launch films and more, all made from slack

we encoded our designers’ judgment into its skills, a design wiki and brand checks that run on every render, so it knows what good looks like for us

then we built a custom media harness around it: a headless photoshop, a shared visual canvas (thanks @paper), video tools like @HyperFrames_ plus our own animation engine, our brand assets and a library of design references

it runs on Managed Deep Agents, which abstracts away a ton of infra including sandboxes, agent memory, slack integration, scheduling and more.

we are going through the architecture, use cases and impact live at our oct 28 webinar 👇

https://t.co/QCSu0sIMIs

[LangChain @LangChain]

Want to learn more about agent memory?

We're hosting a virtual workshop on November 3rd! https://t.co/6wHWw3ygyi

[LangChain @LangChain]

Context Engineering explained in 5 minutes by @jess_weng18.

[Ankush Gola @ankush_gola11]

amazing to see such a successful example of using agent traces in LangSmith to fine tune a model! Was awesome working with the @corridor team on this

[Jake Broekhuizen @jakebroekhuizen]

Hats off to @Aditi and the team at @corridor for the release of Argos 1! 🎩

This is an awesome example of agent trajectories in LangSmith -> post-trained model in practice, with 𝚜𝚖𝚒𝚝𝚑𝚝𝚞𝚗𝚎 helping make it possible.

They sft'd qwen3.5-9B and ended up with a model that beats gpt-6, opus 5.5, and gemini 3.6 flash on both quality and response time when selecting relevant security guidance for coding agents.

This kind of process is a great way to build alpha into your agent, and we're super bullish on giving teams the ability to use their own data to drive improvements at the model layer.

[LangChain @LangChain]

.@corridor used smithtune’s trace-based workflow to build the training data for Argos, a fine-tuned 9B model that scores the highest of the 14 models they tested on their security context benchmark.

🧵 Full thread ⤵️

[Corridor @corridor]

Introducing corridor-argos-1, Corridor’s first security model.

A Qwen 3.5 9B fine-tune that outperforms frontier models including GPT-6 Astra and Claude Opus 5.5 on security context selection, with the lowest time to first token of the 14 models we tested.

Thread 🧵

[LangChain @LangChain]

Context Engineering explained in 5 minutes by @jess_weng18.

[Jess Weng @jess_weng18]

https://t.co/z2Du7R6jkS

[Divya Santhanam @divyasan0]

Agent evals raise a lot of questions. We rounded up the most common ones and answered them! Take a look 👀 https://t.co/rJzHaM4d5u

[LangChain @LangChain]

Agents are being asked to take on increasingly complex work, from research and coding to workflows that span multiple tools and steps.

The challenge is giving them the structure to do that work reliably.

Join our APAC + ANZ Agent Harnesses & Deep Agents 101 Virtual Workshop to explore the building blocks behind agents designed for longer-horizon tasks and learn how to apply those patterns in practice.

https://t.co/8YHpHNNcCw

[Harrison Chase @hwchase17]

great points by caspar on what makes building a "company harness" (companyOS? org harness?) hard

and how MDA solves them!

[Caspar @caspar_br]

How do you build a company harness?

We've built internal agents at LangChain across marketing, sales, and engineering. The results have been incredible but these agents were NOT easy to get right. We spent a ton of time solving the problems that come with a multiplayer harness. Every agent team landed on the same core primitives:

- It lives where people already work (for us that's Slack)
- It knows who's asking and acts OBO that person for some services and with its own identity for others
- It has memory, scoped globally and/or at the user level
- It runs code in a sandbox where authenticated CLIs and scripts just work (gh, curl, your internal APIs)
- Large % of its runs are scheduled rather than explicitly invoked by someone (ambient agents!!)

It became clear we needed one shared place for these primitives, so we built Managed Deep Agents. In my humble opinion it is the easiest way to build a company harness today

Our docs are very agent-friendly, point them here to get started: https://t.co/fhRoaDMABD