Knowledge catalogue
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
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