Search: “harrison-chase--x”

New Max Agency with @tryramp Talked to @ramplabs Head of Applied Research Alex Shevchenko on the Max Agency podcast to learn how @Ramp Sheets was built, their internal agent Inspect, and so much more.

one of the features i'm most excited about in our upcoming langgraph release is delta channels! the langgraph runtime lets you 'checkpoint' agent progress at every step (model call, tool call, hooks).

Open Models Make Agentic Batch Processing Economically Viable A lot of world’s work looks like “Do X for EVERY Y” - read every trace - respond to every email - deep dive into every document - enrich e

Open Models x Headless Agent Execution 🔥 your daily reminder that open models are plenty capable for a lot of coding work. easiest place to feel that out is deepagents! swap the model and go. i've bee

Ramp Labs is doing a ton of cool work all the way from launching products to hardcore research on LLM internals, this is a fun one 🔥 Talked to @ramplabs Head of Applied Research Alex Shevchenko on the

Talked to @ramplabs Head of Applied Research Alex Shevchenko on the Max Agency podcast to learn how @Ramp Sheets was built, their internal agent Inspect, and so much more. YouTube: https://www.youtube

this is the part of the deep agents production series i've been most excited to get to: sandboxes without an execution environment, a production agent is only as capable as its fixed toolset. give an

This appears to be a promotional or event announcement post shared by Harrison Chase on X (formerly Twitter) linking to a Luma event page, likely advertising an event happening in New York City. Witho

300 million agents. @clay runs all of them through LangSmith. This is what production-grade AI looks like at one of the fastest moving startups! .@Clay uses LangSmith to manage 300M agent runs a month

A great conversation between Noah Kravitz from the @nvidia team + @hwchase17. “Every enterprise needs a claw strategy.” How did @LangChain go from a weekend project to 1B+ downloads in 3 years? We sat

A TLDR on Harness Profiles: ✅ Model-specific profiles to adjust prompts, tools, and middleware. 📦 Profiles for @OpenAI, @Anthropic, and @Google models out of the box. 📈 A 10–20 point jump on a subset

Anthropic is building out their managed agents platform, adding Dreaming (memory) and Outcomes (rubrics). The idea I'm wrestling with: how differentiated are these platform features really? I initiall

.@Clay uses LangSmith to manage 300M agent runs a month, with an average 10-30 steps each. @hwchase17’s conversation with Clay’s Head of AI @jeffbarg on how they run this at scale → https://youtu.be/c

“Every enterprise needs a claw strategy.” How did @LangChain go from a weekend project to 1B+ downloads in 3 years? We sat down with CEO and co-founder Harrison Chase (@hwchase17) to talk deep agents,

It feels like agent harness evolution runs on two axes that usually get conflated. There’s the temporal axis: simplify as models improve, stripping components that compensated for limitations the new

.@ListenLabs Co-Founder & CTO @florian_jue on the 2 types of engineers who build great agents: 1/ Those who know what LLMs can and can't do, and can feel when something's off 2/ Product engineers who

really cool work by the Harvey team, excited to partner with them to push forward research on designing + understanding agents across Long Horizon Legal work the first peak I got at LAB was 'woah this

small workflow note that adds up. /staged-pr is a skill (via slash command) i run when wrapping up a PR. it takes my staged code changes and drafts a concise PR title & description, based on my prefer

Strong Opinions, Loosely Held on Agent + Harness Engineering: 1. You can outperform any default harness+model (including codex & claude code) on pretty much any Task by engineering the harness around

the deeper point: model choice and harness choice aren't independent variables. benchmarking a model without specifying the harness is like benchmarking a chip without specifying the compiler. the num

this deepagents deploy https://docs.langchain.com/oss/python/deepagents/deploy (or at least directionally where we want to take it) what's missing? give us feedback! can someone PLEASE launch OS claud

Harrison Chase expresses enthusiasm about collaborating with Harvey on their Long Horizon Legal Agent Benchmark, highlighting the value of developing industry-specific benchmarks for AI evaluation. Th

Harrison Chase expresses support for expanding AI benchmarking efforts and highlights appreciation for work being done by Harvey (likely Harvey AI, a legal AI company). The post indicates enthusiasm f

we're continuing to see clear examples where a model's harness is a major determinant of overall performance. with the same model, running on same task, it's easy to observe very different scores depe

A few days ago i wrote a post about how we use the LangGraph checkpointer to optimize storage. Today, LangGraph released v1.2 with a really nice feature: DeltaChannel, a new channel type that stores o

Agent observability enables monitoring of agent behavior, but to effectively improve agents through feedback loops, developers must actively collect observability data and generate synthetic examples

Come and join us, we're hiring across almost all roles 🚀https://www.langchain.com/careers Companies I'd consider going to if I ever had the urge to try something new. 1 Thinking Machines Lab 2 OpenAI

everyone would have a deeper appreciation for Agent Products that rock because of great Context/Harness Engineering if they… talked to: - LLM Base models - Post-Trained models with no harness (no tool

Great new drop by @LangChain’s @hwchase17 “If you do not know what the agent saw, what it did, and what happened next, you cannot reliably know what to improve” Traces give agents this insight and tha

I detected a bad Agent action, what do I do about it? this is pretty much the main question that will power the future’s Human+Agent driven improvement loops Gather data -> Mine Errors -> Find out whi

if you haven't read this one by @Vtrivedy10, it's a must read! great overview of what components a harness needs to support an agent for long running, long context tasks https://www.langchain.com/blog

Large language models are becoming increasingly costly to develop and operate, creating a need for open-source alternatives that reduce dependency on expensive proprietary models and make AI more acce

New from the LangSmith Signal: The vector space rapidly diversified since December. ✅ Managed cloud surged. MongoDB Atlas, Pinecone, + Qdrant tripled their combined share to 21%. ✅ Only 3 vector store

Not quietly. Loudly! 📣📣📣 deepagents-cli🔥 deepagents-cli is quietly becoming the best place to start coding with open weight models. we've been investing heavily in making it a harness that's truly mod

Observability helps power the agent improvement loop But it's not just observability! It's also feedback! You should be trying to get as much feedback (direct, indirect, generated) into your agent obs

Open models should compete on cost and specialization, not frontier benchmarks @natolambert puts it well: the right benchmark is savings in compute and time, especially for repetitive agent tasks deep

open-weight LLMs have come a long way on agent tasks! but the harness you wrap them in matters just as much as the model itself, and arguably the interface you use to drive that harness matters even m

.@Redisinc is returning to Interrupt! Say hello to their team at the expo hall to learn about their fast memory layer for chatbots and AI agents as well as their ready-to-use tools for building AI app

so well explained ! this is like applying Richard Sutton’s Bitter Lesson to agent systems. leverage comes from feedback loops, not handcrafted prompts. attaching feedback to traces is the missing piec

There’s a bunch of conflicting stances I don’t fully understand in the debate of Proprietary RL’d vs Open Harness, Model intelligence, and Agent Labs building harnesses for bespoke tasks Not all of th

Agent observability is enhanced by storing feedback alongside execution traces, transforming them from simple logs into a learning system that enables continuous improvement. This approach allows deve

'Traces everywhere. Feedback loop? Nowhere' @hwchase17 This is the part that gets skipped. Traces everywhere. Feedback loop? Nowhere. Building agents for clients and the hardest sell is 'we need to lo

Want to run the same harness across multiple interfaces? Try ACP. Deep Agents ships with it out of the box. open-weight LLMs have come a long way on agent tasks! but the harness you wrap them in matte

We’re hiring! https://www.langchain.com/careers Companies I'd consider going to if I ever had the urge to try something new. 1 Thinking Machines Lab 2 OpenAI 3 Anthropic 4 Cursor 5 Applied Intuition 6

CopilotKit has secured $27 million in funding to develop an agentic frontend stack that integrates AI agents with human-facing user interfaces, reflecting the belief that all future UI will incorporat

This post likely discusses Harrison Chase's preferred language model or AI system for handling coding tasks, potentially comparing different models' capabilities for programming work. As the creator o