Search: “harrison-chase--x”

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

open weight models are having a moment @masondrxy is making deepagents cli a coding harness that works amazing with open weight models what is missing? what should he add? try it out! deepagents-cli i

production agents need to feel responsive in real time. two primitives make that possible: streaming and concurrent request control. on streaming: streaming is a first-class primitive in LangSmith's a

shipped Promptloop 🌀 Claude Code but for your prompts. a CLI agent that runs the full prompt-eval loop in one chat session: create test cases → run across models → generate reports → propose prompt →

Start today → https://docs.langchain.com/oss/python/deepagents/cli/overview deepagents-cli is quietly becoming the best place to start coding with open weight models. we've been investing heavily in m

This is probably better messaging than just “own your harness.” Yes, open models will need custom harnesses, but that is a means to an end. The end is utilizing models without being handcuffed to Anth

This is why I keep going on about LangGraph 1.2 alpha is looking great: node-level error handlers, timeouts, improved performance, and stronger typing LangGraph is the runtime layer you want underneat

Use open models in Fleet! 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

we're on an Open Model mission to help builders create world class agents >20x cheaper than what they have today a couple things have become evident recently: 1. The age of the token subsidy is being

This post discusses self-review capabilities in AI systems, where an AI model can evaluate and refine its own outputs before presenting them to users. According to Florian Jue from Listen Labs, this f

Ha love to see this port of flue to python Harness engineering is a fun time! Need more people exploring! 🎉Introducing PyFlue: The Python-Native Agent Harness Framework.🧰 💡Flue for Python: Fred K. Sch

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 they have similar performance (because they’re less coup

🎉Introducing PyFlue: The Python-Native Agent Harness Framework.🧰 💡Flue for Python: Fred K. Schott @FredKSchott CEO of HTML has launched Flue: The Agent Harness Framework for TypeScript. It brings prog

it's always funny to me when these bot posts say we *just* launched deepagents... like nah man we've been carving this stone for close to a year now! Long-running agents almost always fail because the

JP Morgan's investment research team just shared exactly how they built their multi-agent system 'Ask David', and it's the same architecture pattern showing up everywhere: - supervisor agent orchestra

This post likely discusses open source AI and language models, potentially covering developments in model accessibility, licensing, or comparisons between proprietary and open source approaches. Given

our docs are a great place to get started! we try to make all conceptual guides applicable to a wide range of experience levels. if you need a more structured learning format, check out langchain acad

Thanks @hwchase17 for deepagents, cool harness to play with. Indeed, Harness Engineering is a fun time. Ha love to see this port of flue to python Harness engineering is a fun time! Need more people e

the lock-in isn't the harness -- it's the context pipeline feeding it. whoever owns how repo state gets pulled, ranked, and compressed into the attention window owns the developer, regardless of model

the same model in a different harness can yield much different performance! we've seen this on a few different occasions now - we took gpt-5.2-codex from 52.8% to 66.5% on Terminal-Bench 2.0 (Top 30 t

This post from Harrison Chase (founder of LangChain) indicates that the LangChain team remains open to user feedback and continues iterating on their products and services based on community input. Th

while exploring harnesses, i came across my first and favourite agent orchestration framework @LangChain has launched deep-agents last year. it’s a wonderful harness, that has a lot of tools: - write_

great intro to agent harnesses and harness engineering! Is agent harness engineering the new prompt engineering? @LangChain co-founder and CEO, @hwchase17, joined us at #GoogleCloudNext to explain why

This post discusses how combining Qwen 3.6 with sub-agents and parallel tool calling capabilities in frameworks like LangChain and DeepAgents demonstrates significant performance benefits for AI agent

The Harness is a Context Manager on Behalf of the Model What happens when the context window fills up and who decides? This decision is external to the model - The Harness designer must have some opin

Harrison Chase, the creator of LangChain, commented on significant improvements made to the LangChain documentation, indicating that the docs have substantially improved compared to a previous version

The year of the harness 🤝 Harnesses are our most direct layer to turn a model into a great product experience for users by shaping model behavior towards a goal This matters today because models have

A small, but very useful change! We added this based on our learnings while developing agents in Fleet, so you know it’s a useful addition new mode for LangChain's human in the loop middleware: respon

LangChain announced job openings and highlighted company growth through a careers page link, indicating the project was actively expanding its team. This post demonstrates LangChain's scaling phase an

This post announces the ability to build AI agents using LangChain integrated with Browserbase, enabling agents to perform web searches, retrieve content, and control browser subagents to access and i

create_agent - how we build Deep Agents on the simplest harness primitive underlying all of the harness engineering, research, and API design in Deep Agents is a very simple primitive in LangChain cal

deep agents need the web, and it’s super exciting to team up with @LangChain to make that happen! Build agents with LangChain + @browserbase. Give your Deep Agents search, fetch, and browser subagents

Fleet can now generate and render SVGs and Mermaid diagrams inline! Ask it to make you a diagram, and watch it work its magic. I'm using a mix of our GitHub tools, and @cognition's DeepWiki to make th

here's a deep dive on how middleware lets you customize your agent harness, excellent writeup by @Vtrivedy10 !! deepagents offers a powerful base harness that you can customize for your use case! crea

Is agent harness engineering the new prompt engineering? @LangChain co-founder and CEO, @hwchase17, joined us at #GoogleCloudNext to explain why the 'harness' is the secret to moving AI agents from a