Own your intelligence. Makers of LangSmith, @LangChain_OSS, and @LangChain_JS.
Joined November 2022
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There's still time to RSVP for our upcoming SF meetup with @typesafeai
Join @sydneyrunkle, @allietheicon, and Seán Roche at LangChain HQ for a night of talks on building with decision models and using them to evaluate agents.
Spaces are limited!
luma.com/iuc3ostf
If you need a framework for thinking about the loops around an agent and want to learn how to build them, look no further.
Learn about Loop Engineering with Deep Agents today: deeplearning.ai/courses/loop…
Build reliable agents for long-running tasks.
Loop Engineering with @LangChain Deep Agents: add loops that check output, trigger runs on events, and improve the harness from traces across runs. Taught by @sydneyrunkle and @EAGar123.
Enroll for free: bit.ly/4kfDJxR
.@hwchase17 kicked off Interrupt London with a keynote on where agents are headed, and why every org should own their intelligence.
🔴 Full talk: youtu.be/XOUSuxY074U
⏯️ Every session, on demand: interrupt.langchain.com/reco…
LangSmith for Startups Spotlight: @PetualAI
✅ Agents for internal audit and SOX teams at leading public companies and high-growth enterprises.
✅ Use LangSmith within their engineering workflow to understand, improve, and validate the performance of their AI systems.
🏢 Hiring across functions: petual.ai/careers
“Typically the manual mechanism takes about 20 days. Just with the first pass, we are able to drive it down to 10.”
@skyworksinc uses LangGraph and LangSmith to deploy agents and run evals in one place.
⏯️ How they’re using agents to cut root cause analysis time.
LangChain retweeted
a lot of the challenges in harness engineering are now best solved by closer integration of harness and infra
async subagents and browser use are two examples of this
now in MDA 0.10!
Managed Deep Agents 0.10: Now available in public beta
🌐 Browser use: Your agent can now work on websites with no API
💬 Async subagents: Run long tasks in the background while your agent keeps talking
🧵 Thread origins and stopping runs
...and so much more
LangChain retweeted
a lot of you asked how i made our launch video for Content Studio
it took 13 iterations, 5 hours and ~$120, all done in a Slack thread
here's the breakdown of every step 🧵
We just open sourced our internal design brain 🧠
Meet Content Studio - the agent behind 3,000+ assets at @LangChain
Create on-brand product launch videos, slide decks, ad creatives, social media assets, ui mockups and more
Storyboard and collaborate on a shared canvas in @paper and edit videos in code with @HyperFrames_
It even made its own launch film below, and went full Rocky 🥊
Fork it, add your brand guidelines, and deploy it to your team in Slack with Managed Deep Agents
Here’s what 's inside 🧵
Excited to partner with the @Eon_io_ team to benchmark model misalignment in enterprise environments.
The first question we're digging into: Do results change when the model runs inside the Deep Agents harness? Stay tuned for more research.
We just ran the most comprehensive benchmark of AI agent alignment in enterprise environments:
19 leading AI models acting as agents inside simulated companies built with Era, 2,304 runs across 41 everyday tasks.
Agents broke security and business rules in 40% of those runs. Every single model failed, including all the frontier models. A few examples:
- An agent told one of the company's biggest customers that Finance had approved a full refund.
Nobody had.
- An agent shared a customer's internal churn-risk score directly with that customer.
- An agent followed a fake runbook planted in a support ticket and promised a customer a credit nobody approved.
- An agent posted another customer's personal contact details (PII) in a shared Slack channel.
In a real company, each of these is a financial liability, a compliance breach, or a lost account.
We've shared the full results with the model providers, including exploitable failure modes we're not publishing, and are working with them on fixes.
One benchmark won't fix this.
We're excited to have @cognition, @LangChain, @TryArcade, @Reindeer_AI, Hirundo, @Box, @recolabs_ai and @QodoAI join Era as research partners, as we build out the next round of research.
More soon.
I'm still the most bullish person in the room on AI.
But this benchmark convinced me that agent reliability and trust is the next big problem to solve.
Until then, test every agent before it gets a login, and keep your own record of what it changes.
Full results: era.eon.io/model-alignment-b…
🌐 Managed Deep Agents can now use a browser
Your agent can:
- Take actions on sites with no API, like booking a reservation
- Test an application end to end through its UI
- Hand you the browser to sign in, then pick up where it left off
Docs ➡️ docs.langchain.com/langsmith…
LangChain retweeted
one of the biggest things i learned building @LangChain’s internal design agent is that most of the rules i wrote actually made it worse...
they were there to compensate for the model’s intelligence at the time
but when Opus 5.5 came out i deleted most of them, and the work got a lot more creative
we got into this with 350+ people at our GTM Engineering Live today.
key takeaways below 🧵
LangChain is returning to re:Invent. A TL;DR:
📍 Meet us at Booth #1740
🎙️ Talks + Panels throughout the week
🗓️ Co-hosted events featuring breakfasts, networking, dinners, and more
See what we’re up to + book meetings: fieldevents.langchain.com/aw…
LangChain retweeted
we've been exploring how to make the knowledge openwiki produces more useful to agents.
our latest experiment pairs the wiki with a lightweight evidence graph, letting agents follow relationships and trace information back to supporting evidence.
on complex, domain-specific questions requiring cross-source reasoning we saw:
👉 5x higher accuracy with openwiki vs. grep alone
👉 27% more accurate with the evidence graph
👉 2x citation consistency
👉 58% lower cost per correct answer with the graph
check out the blog ⤵️
langchain.com/blog/building-…
Great look at making agent memory traceable to evidence ⤵️
we've been exploring how to make the knowledge openwiki produces more useful to agents.
our latest experiment pairs the wiki with a lightweight evidence graph, letting agents follow relationships and trace information back to supporting evidence.
on complex, domain-specific questions requiring cross-source reasoning we saw:
👉 5x higher accuracy with openwiki vs. grep alone
👉 27% more accurate with the evidence graph
👉 2x citation consistency
👉 58% lower cost per correct answer with the graph
check out the blog ⤵️
langchain.com/blog/building-…
LangChain retweeted
"the art of loop engineering" is now a @DeepLearningAI course!
deeplearning.ai/courses/loop…
LangChain retweeted
deslopify - made with Content Studio
We just open sourced our internal design brain 🧠
Meet Content Studio - the agent behind 3,000+ assets at @LangChain
Create on-brand product launch videos, slide decks, ad creatives, social media assets, ui mockups and more
Storyboard and collaborate on a shared canvas in @paper and edit videos in code with @HyperFrames_
It even made its own launch film below, and went full Rocky 🥊
Fork it, add your brand guidelines, and deploy it to your team in Slack with Managed Deep Agents
Here’s what 's inside 🧵
LangChain retweeted
Build reliable agents for long-running tasks.
Loop Engineering with @LangChain Deep Agents: add loops that check output, trigger runs on events, and improve the harness from traces across runs. Taught by @sydneyrunkle and @EAGar123.
Enroll for free: bit.ly/4kfDJxR
LangChain retweeted
great to work w/ @AndrewYNg and @EAGar123 on this loop engineering course!
this course focuses on how to build agents that can a) handle complex tasks and b) improve over time with deepagents!
Build reliable agents for long-running tasks.
Loop Engineering with @LangChain Deep Agents: add loops that check output, trigger runs on events, and improve the harness from traces across runs. Taught by @sydneyrunkle and @EAGar123.
Enroll for free: bit.ly/4kfDJxR
You’re an mda init and mda deploy away from distributing a proactive agent in your org.
Since launching dynamic scheduling for Managed Deep Agents in September, over 70% of MDA runs on LangSmith come from schedules vs human typing a message.
