Knowledge catalogue
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
It is honestly shocking how little code can get you so far within this base primitive. create_agent is one of the most fun things for me to show people who are trying to get started here - because it
✈️ @LATAMAirlines is landing at Interrupt. The largest airline in Latin America built two production agents that handle trip planning and agency coordination. Building them was easy. Operating at scal
many sensitive Agent Workloads today require some sort of human feedback LangGraph supplies the runtime primitives for LangChain + Deep Agents and makes it easy to durably pause, resume, and replay ag
most of the time, you want an agent loop to run uninterrupted. that's where the utility comes from! but some decisions shouldn't be delegated to the agent. two situations come up consistently: 1/ befo
new mode for LangChain's human in the loop middleware: respond instead of running a tool, you can return the human's interrupt response directly as the tool's output handy for 'ask user' stubs or head
one future trend i'm very excited by: models getting good enough where they can power agents that browse the web deepagents + @browserbase is a glimpse of that future See the full example here: https:
One thing I love about LangChain is how the OSS pieces build on each other You can build robust workflows directly with LangGraph, our orchestration framework. We also use it as the foundation for Dee
This post discusses the use of sandboxes for AI agents, featuring insights from Florian Jue, co-founder and CTO of Listen Labs, during an appearance on the Max Agency podcast. The discussion likely co
shoutout to the man @kylejeong for cooking this up 🔥 every agent can now get a dedicated browser subagent in seconds to navigate sites, fill out forms, click through things, you get it Deep Agents x B
This is the year of browser use agents!!! I'm so excited Build agents with LangChain + @browserbase. Give your Deep Agents search, fetch, and browser subagents to access the full web. All with full ob
A Deep Dive into Deep Agents 🚀DeepAgents deploy is a simple, configuration driven way to get an agent harness deployed to the cloud deepagents.toml is the file that configures it. It has four sections
LangChain's DeepAgents framework is receiving frequent updates and deployments, with documentation available at their official docs site. The project maintains an active development cycle with updates
Claude Code is tuned for Claude. Codex is tuned for OpenAI models. Until now, 𝚍𝚎𝚎𝚙𝚊𝚐𝚎𝚗𝚝𝚜 had fixed harness defaults, which meant it couldn't take advantage of the provider-specific optimizations that
Deep Agents is a framework or library developed by LangChain that enables the creation of sophisticated AI agents with enhanced reasoning capabilities. The project likely provides tools and abstractio
🚀DeepAgents deploy is a simple, configuration driven way to get an agent harness deployed to the cloud deepagents.toml is the file that configures it. It has four sections: - agent - sandbox - auth -f
Exciting direction - can see immediate gains for many teams trying to prevent lockins for cost/legal reasons - my mental model on this is shaped by the meta-harness paper that we need to think of harn
Finally this theme is taking off 👇 For the record : it’s not only a matter of cost. This applies both to agent design (particularly when testing more innovative, substrate-first archis...) and to issu
[frontend] UI/UX is a surprisingly big part of building agents! It takes a while to hook up streaming, thread history, all the endpoints We do it for you! Get a frontend you can expose to users out of
resharing this note, find it helpful given all the great open evals work + teams building vertical agents Evals are a proxy for the behavior we want our agent to exhibit in production Model+Harness pu
serving multiple users from a single agent deployment introduces three distinct problems. luckily, langsmith's agent server has a solution for each! 1. data isolation: your @auth.authenticate handler
the folks @MadrigalPharma are some of my favorites to work with, they're always at the cutting edge! check out this case study on how they're building a multi-agent deep research system, powered by de
tweeted about this yesterday and Cursor already dropped the alpha today! 🚀very cool to see how us, them, and others have converged on good design patterns in Agent + Harness Engineering: 1. Tuning dif
This post discusses the LangSmith UI dashboard that becomes available after deploying a project, which provides visualization and tracking capabilities for monitoring deployments. The dashboard allows
After switching into the new directory (`cd lc-docs`) I'm going to set the name of my agent, and the model to use This is done in deepagents.toml I'm going to use @Zai_org GLM5 model, served via @base
The `deepagents deploy` command is used to deploy applications, leveraging Docker as its underlying technology, requiring Docker to be pre-installed on the system. This appears to be part of DeepAgent
agents are going and are already changing every industry & vertical, awesome blog from the @MadrigalPharma team on using Deep Agents, Skills, & LangSmith at the frontier of biopharma it’s an awesome t
As part of this deployment, you can interact with the agent in several ways We expose the agent via MCP and A2A, as well as with as a 'remote graph' API, which means you can interact with as you would
Been talking to a lot of devs about LangGraph's Postgres checkpointer lately, the tricks each team applies to get it production-ready are always different. Here's how we handled it at @tiendanube: cus
big theme of 2026 - cost of closed models is too high! really excited to make deepagents work exceptionally well with OSS models Switched out Sonnet 4.6 for GLM 5.1 through @FireworksAI_HQ while doing
DeepAgents Deploy is great, you get - a fully open agent harness (no hidden stuff, inspect everything) - managed infra - can use any cocktail of models including Open Models - and everything is traced
DeepAgents Deploy is the easiest way to bring agents to production, with just a few markdown and configuration files (no code!) 🧵Here's me building an agent that connects to LangChain Docs. Powered by
Durable execution is a hallmark of well-built distributed systems, it's been core to LangGraph since the beginning. In the agents-as-software era it is non-negotiable, because agents fail in all sorts
Final steps before deploying: 1/ Delete the skills folder the scaffolding created (not needed for now) 2/ Add environment variables. Need a Baseten API key (for the model) as well as a LangSmith API k
First I'm going to install deepagents cli. This is a CLI which will help me set up and then deploy my agent `uv tool install deepagents-cli` DeepAgents CLI is open source - see it here: https://github
LangChain is offering multiple harness variants that are custom-fitted to specific use cases and models, recognizing that effective harness implementation requires tailored configuration rather than o
This post likely discusses best practices and techniques for optimizing DeepAgent performance across diverse model architectures and families, covering strategies for compatibility and effective integ
The post discusses LangSmith's agent interaction capabilities in the studio environment, highlighting how users can access a user-friendly interface for agent testing and collaboration. It emphasizes
.@MadrigalPharma is living in the (agentic) future. If you haven't heard of Madrigal, they are a pioneering biopharma company. Some months ago, they set out to solve the problem of integrating, search
.@MadrigalPharma’s multi-agent research and intelligence platform for pharma is powered by LangChain + LangSmith. When users ask research questions, the orchestrator breaks them into sub-tasks. Multip