Own your intelligence. Makers of LangSmith, @LangChain_OSS, and @LangChain_JS.
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1️⃣ LangSmith Engine v2
2️⃣ Managed Deep Agents v0.8
3️⃣ LangSmith Trajectories
4️⃣ LangSmith Fine-Tuning
5️⃣ Custom Apps
Everything we announced at Interrupt NYC ⤵️
langchain.com/blog/langsmith…
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Experience the best of Interrupt NYC, now on demand.
interrupt.langchain.com/reco…
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LangChain retweeted
user memory is what helps agents improve over time!
for example, our content studio agent (built on MDA, made the video below, s/o @caspar_br) uses user level memory.
it can:
- save my preferences when i give it feedback (like slowing down text animations)
- remember our past conversations, including previous content artifacts
so not only does the content improve over time but it's also easier/faster to generate!
x.lingyaoai.com/caspar_br/status/21060…
User memory in Managed Deep Agents 0.8: your agent can remember the people it works with langch.in/mda
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LangChain retweeted
User memory in Managed Deep Agents 0.8: your agent can remember the people it works with langch.in/mda
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💻 Build the interface you want w/ your LangSmith data
✅ Publish it into your workspace
🙅 Skip hosting, auth, + permissions work
Get started with Custom Apps today.
langchain.com/blog/langsmith…
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Scope agent memory to a single user or your entire team.
Managed Deep Agents now supports user-level memory out of the box. Set it up with one file and deploy with a single command.
User memory in Managed Deep Agents 0.8: your agent can remember the people it works with langch.in/mda
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Validate agent fixes before shipping them with LangSmith Engine v2.
Engine now tests and validates fixes automatically:
✅ Replicates the issue with the same deployment environment and same inputs
✅ Builds a fix, tests it, and iterates on improving it until it has a satisfactory solution
✅ You review the fix and deploy it with a ready-made PR
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Ilya Meyzin, SVP, AI Solutions & Data Science at @DunBradstreet is speaking at Interrupt, The Agent Conference by LangChain.
Catch Ilya’s talk + more: interrupt.langchain.com/lond…
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LangSmith for Startups Spotlight: @corridor
@Corridor uses LangSmith to develop + run novel agentic security evaluations, build + trace inference pipelines, and manage memory graphs that provide specialized context across various product surfaces.
Get a demo: corridor.dev/demo
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LangChain retweeted
some much needed TLC for langchain mcp!
@𝚕𝚊𝚗𝚐𝚌𝚑𝚊𝚒𝚗/𝚖𝚌𝚙-𝚊𝚍𝚊𝚙𝚝𝚎𝚛𝚜 2.0 is out! 🧵
Using MCP with your TypeScript agents up to MCP servers just got a lot simpler: support for the latest stateless version of MCP, tools that can check in with your users when they need something, much easier auth integrations, and a ton of QoL updates.
Here's what's new 👇
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🧵 @langchain / MCP-adapters 2.0
@𝚕𝚊𝚗𝚐𝚌𝚑𝚊𝚒𝚗/𝚖𝚌𝚙-𝚊𝚍𝚊𝚙𝚝𝚎𝚛𝚜 2.0 is out! 🧵
Using MCP with your TypeScript agents up to MCP servers just got a lot simpler: support for the latest stateless version of MCP, tools that can check in with your users when they need something, much easier auth integrations, and a ton of QoL updates.
Here's what's new 👇
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LangChain Academy Course Update: Introduction to Deep Agents.
Deep Agents makes it easier to build agents, Managed Deep Agents lets you deploy them with a single CLI command. We introduce Managed Deep Agents and show you how to deploy an agent in Slack.
Check it out today: academy.langchain.com/course…
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LangChain retweeted
model routing - "no one knows what it means, but its provocative, gets the people going"
we try to shed light on how to do it properly
1. understand tasks
2. understand the models
3. build the router **in the harness**
4. track outcomes
lower costs, with no performance hit
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LangChain retweeted
great piece from @sydneyrunkle on moving model routing upstream into the harness -
great way to think about managing routing more efficiently and building your harness to avoid context bloat.
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LangChain retweeted
we built a model router for our coding agent that cut median cost per task by 64%, with no measurable drop in quality!
most tasks don't need top tier intelligence!
here's how we built it, why we think it belongs in the harness, and how to add one to your own agent :)
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Adding routing to your agent? Here's where to start:
1️⃣ Understand the tasks
2️⃣ Understand the models
3️⃣ Build the router in the harness
4️⃣ Track task outcomes
An inside look at how to build a model router in the harness, and how we cut our median cost per thread by 64% with no change in quality.
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LangChain retweeted
we want every team to own this cycle:
- log every agent action in Trajectories
- use agents to help humans understand that data at scale
- turn that data into Evals & Environments
- do lots of Agent + Human review
- Fine-tune much cheaper and faster open models that rock at those tasks
Last week, we launched LangSmith Fine-Tuning and the smithtune CLI.
Turn your agent’s own trajectories into a fine-tuned model without building a training pipeline by hand.
Your traces already show your agent doing the job well. Now, that signal becomes training data.
Quick demo from @jakebroekhuizen
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LangChain retweeted
A huge part of post-training is picking and wrangling the right data. `smithtune` handles the entire lifecycle of agent traces -> fine-tuned model
We spent a lot of time streamlining the process to go from your agent's trajectories to an SFT'd model on your training/inference provider of choice so you don't have to stitch together the steps manually.
The data curation phase is definitely the most important, and we've had great results from working with training copilots in the form of model councils and a coding agent guided by the CLI and its packaged skills. This + the good ol' fashioned 'look at the data' ring true for any well-designed eval & post-training pipeline, and we're really excited to enable builders to use this in the process of owning your own intelligence!
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