Own your intelligence. Makers of LangSmith, @LangChain_OSS, and @LangChain_JS.
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LangChain retweeted
jev was really exciting to us since it’s a reprisal of how we think a lot of systems with AI should be built!
think about it this way: when starting a project you don’t start with big behemoth k8s clusters (à la frontier models/ harnesses), you start with smaller units (jev) and iterate as needed
we learned A LOT when simple decisions from LLMs were the paradigm (which is what langgraph was built around), to see this type of model stack rank next to models of today (*on subset of tasks) makes us really excited for the future
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LangChain retweeted
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
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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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LangChain retweeted
@LangChain Interrupt26 in NYC was amazing! 🗽
The energy of a sold out venue of agent builders from enterprises all over the world was so inspiring. @craigirwin and I spent the day demoing @CopilotKit and answering questions.
The favorite topic: how your app can have automatic learning built in 🤯
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the biggest lesson that i took away from our jev webinar w/ @allietheicon is that jev actually enables REAL TIME inference
this is because jev is fast and cheap. you can:
- do content filtering on a stream in real time
- add guardrails that don't really impact ux
- monitor (and steer) agent trajectory w/o prohibitive cost
there's a lot more to say here but i find these examples in particular very compelling
curious what others are doing in real time w/ jev!
webinar here icymi: events.langchain.com/on-dema…
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Join our fall AMA series for practical walkthroughs of the latest LangSmith capabilities:
📍 9/30 - Improving Agents w/ Tuned Evaluators
📍10/7 - Evaluate Agent Behavior w/ Trajectories
📍10/21 - Build and Deploy Deep Agents w/ Managed Infrastructure
events.langchain.com/fall-pr…
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LangChain retweeted
I built a starter app for @LangChain's new Managed Deep Agents product
Allows you to perform due diligence on @Shopify and get a cited research report
Langchain is moving fast - and the product is super easy to use.
Give it a try!
Managed Deep Agents from @LangChain launched today with built-in web search powered by Parallel.
Add one MCP server to your tools file. LangSmith manages the credentials, runs the calls, and traces every search: no extra account or API key.
Bonus: It’s free during the public beta of Managed Deep Agents!
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LangChain retweeted
Parallel is now built into @LangChain managed agents.
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
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The talks may be over, but the Interrupt Reception sponsored by @tavilyai has just begun!
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LangChain retweeted
Try out LangSmith Fine-Tuning today with our dedicated CLI smithtune github.com/langchain-ai/smit…
A huge part of post training is finding and processing the right data. Our CLI handles pulling, selecting, processing trajectories from LangSmith, training on @FireworksAI_HQ and @baseten, then evaling with LangSmith.
Let me know what you think!
Introducing LangSmith Fine-Tuning and the smithtune CLI.
LangSmith now handles the entire fine-tuning process. Use your traces to train specialized models that cut cost and latency.
Now in Public Beta. langchain.com/blog/langsmith…
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Final sessions of Interrupt NYC
🎙️ Fireside Chat w/ @RogoAI CEO & Co-Founder @gabestengel
🎙️ Evaluating Agentic AI at Scale with @linkedin
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LangChain retweeted
🔢 at @LangChain we've been putting a lot of thought into safe storage of user memories.
🔐 We wanted a secure way for a distributed agent to manage memory without it leaking outside of its intended context.
Here's a quick overview of the system and how to add user and agent memory to your managed deep agent - it's a single line of Python or TypeScript
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Before Interrupt wraps up, stop by the LangChain Agent Arena!
🕹️ Play 30 seconds of Tetris to train your style
👉 Choose the model that plays it for you
👀 Watch it battle the reigning champion
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A few things you can build:
1️⃣ Annotation queues: Review and label one (or many) runs at a time
2️⃣ Experiment comparisons: Compare outputs and metrics across experiments side by side
3️⃣ Trace reviews: Build trace or thread history views to inspect app behavior
Introducing LangSmith Custom Apps
Create any interface from your agent data with a prompt. If you can think it, LangSmith can build it.
Now GA. langchain.com/blog/langsmith…
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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 🫡
Introducing LangSmith Fine-Tuning and the smithtune CLI.
LangSmith now handles the entire fine-tuning process. Use your traces to train specialized models that cut cost and latency.
Now in Public Beta. langchain.com/blog/langsmith…
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LangChain retweeted
Proud to be a launch training partner for @LangChain's LangSmith Fine-Tuning and smithtune.
LangSmith traces → managed SFT on Fireworks → eval → deploy. One CLI, no training infra.
Get a Fireworks API key: fireworks.ai/api-keys
Then run smithtune: github.com/langchain-ai/smit…
Introducing LangSmith Fine-Tuning and the smithtune CLI.
LangSmith now handles the entire fine-tuning process. Use your traces to train specialized models that cut cost and latency.
Now in Public Beta. langchain.com/blog/langsmith…
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