Knowledge catalogue
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
Model-Harness-Task fit is very real. The world's best agents carefully tailor the harness around the model to take advantage of each model's unique intelligence & capabilities 🧠 Today we released Harn
Most pharma agent pilots never make it to production. We’re partnering with @Axtria to close this gap. Built on LangSmith, Axtria’s pharma-native AgentOps framework will give life sciences enterprises
New in Deep Agents: Harness Profiles. https://www.langchain.com/blog/tuning-deep-agents-different-models ✅ Model-specific profiles to adjust prompts, tools, and middleware. 📦 Profiles for @OpenAI, @An
Open Models are really smart Open Models are often way cheaper Open Models are fast for the CTOs & CFOs in the back, no need to fight, Open Models mean you can both be happy :) - potentially cutting c
own your agent harness 🤝 own your intelligence 🤝 own your evals 🤝 open ecosystems this doesn’t mean start from scratch, build on an open base harness where you can edit/extend existing primitives like
So excited for this - in the past four months I've written all my code using Deep Agents with: - Opus 4.6 - GPT-5.4 - GLM-5 - Kimi 2.6 - Opus 4.7 - GPT-5.5 Currently using 5.5 as my driver with Opus s
That's a quick tour through building and deploying a simple agent with DeepAgents deploy Was just a markdown file, a json file, and toml file Easy to set up, but robust deployment by the end Try out d
This entry documents the initial setup process for a DeepAgents project, specifically demonstrating how to use the `deepagents init` command to create a new project and assign it a name (in this examp
This is so good @hwchase17 walks through the complete setup of a Deep Agents deployment step-by-step It’s a great example of getting started fast with Deep Agents from the CLI! DeepAgents Deploy is th
LangChain is actively hiring for an unspecified position or role (#11), as confirmed by Harrison Chase on their official X account. The company directs interested candidates to their careers page at l
when you take an agent to production, you almost certainly need guardrails! deepagents makes it easy to add guardrails via middleware. guardrails can be used for 1. control flow, like model/tool retri
At @ListenLabs, agents analyze data from conversations by building their own tables — adding columns like 'user sentiment,' filling in values automatically, then charting the results. Co-Founder & CTO
Both are great models and neither wins everywhere. I use both Opus and 5.5 depending on the task. LangSmith Fleet lets you choose the model for each agent, so you can match it to the work https://www.
first up in the 'going to production' series: durable execution! long running agents need to be able to 1. survive crashes 2. resume after an indefinite pause durable execution solves this by checkpoi
free agents from Harrison? I’m in 🫡 drop the agent of your dreams below and it’ll (prob) magically exist soon i want an agent that helps me riff on ideas for tweet-length visual essays and researches
Harrison Chase discussed deep agents and LangChain's SDK for building autonomous agents on the Talk Python podcast (episode 543). The conversation likely covered LangChain's tools, capabilities, and b
Harrison Chase expresses his passion for working with early-stage companies and startups focused on AI development, indicating his entrepreneurial mindset and enthusiasm for the AI innovation space. T
If you're going to be at @_odsc, head over to @sydneyrunkle's talk Deep Dive on Deep Agents. Today @ 11:05 AM ET speaking at odsc east today!! DM me if you're in boston and want to chat about deep age
Harrison Chase announced plans to create multiple deepagents examples using the deepagent deploy tool and solicited community input on which examples would be most valuable. This post likely seeks fee
.@jakebroekhuizen joined LangChain as one of our first deployed engineers. In just over two weeks, he’s adding MC to his resume as he takes the stage at our agent conference Interrupt. A quick convers
Open Models & a potential looming Haiku-apocalypse? 🚀 chart comparison UI courtesy of @OpenRouter there's a large class of problems where you don't need frontier intelligence. you want cheap, fast, an