Search: “harrison-chase--x”

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

You need feedback to help power the data flywheel! This 'feedback' can come from many sources: - direct user feedback (thumbs up/down) - indirect user feedback (code suggestions accepted) - llm as jud

changing an agent's data model mid-deployment is one of those problems that breaks things silently the langgraph runtime (which powers deepagents) has built-in support for backward compatibility and s

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 model-agnostic, without compromising perform

deepagents you can run with a 'virtual filesystem' lets do lots of great context engineering tricks, without requiring an actual sandbox environment! @hwchase17 Where there's struggle is all of these

fleet is built on top of deepagents, a model agnostic base harness! try building multi-model agents in fleet today, or build your own deep agent with the underlying sdk! Not every step in an agent wor

@garrytan frames personal ai as the counter to extractive institutions: freedom to write your prompt and own your data. @hwchase17 made the same point from the dev side last month: if you don't own yo

Great conversation on harnesses from @hwchase17 at #GoogleCloudNext Is agent harness engineering the new prompt engineering? @LangChain co-founder and CEO, @hwchase17, joined us at #GoogleCloudNext to

i'll be speaking in NYC this thursday on memory for agents! i'll be talking about filesystem backed memory, owning memory with an open harness, background memory consolidation, and more! https://luma.

LangChain, a framework for developing applications with large language models, is advancing its capabilities and features according to insights from Harrison Chase, one of its key figures. This post l

LangGraph is battle tested against production weirdness. For example you can update an agent while it is running without breaking active threads Deep Agents inherits this resilience because it runs on

langgraph is the runtime that powers langchain and deepagents! we've been cooking on some new features: 1. node level error handlers 2. static + dynamic node timeouts 3. delta (diff based) channels fo

No longer are Fleet agents constrained to a single model. With multi-model support, you can build more efficient agents at scale http://langchain.com/langsmith/fleet Not every step in an agent workflo

Not every step in an agent workflow needs the same model. Fleet now lets you customize which model each sub-agent uses, so you can route simple tasks to fast/cheap models and keep stronger models for

Open sources harnesses powered by open source models I hear this take (usually from the model labs), but that will just mean more people turn to open source models, no? They’re already cheaper, if the