Search: “harrison-chase--x”

Most agent tutorials stop before the hard parts. This one builds them step by step. Deep Agents from Scratch is a five-notebook LangGraph course for builders who want to understand how advanced AI age

we are rolling support for the new MCP spec in langchain and need feedback on our new API! with this new pattern you connect langchain agents to multiple MCP servers, hook up oauth, use elicitation w/

The article argues that Benchling is poised to become the leading AI agent for scientific work, contrasting with OpenAI’s stance on a multi‑agent ecosystem where each agent is domain‑specific. It high

a question we were interested in answering for openwiki 0.4.0 was: how do we make openwiki available to more devs? our answer was simple: coding agent integrations! you can now drive openwiki init and

Most users never rate a bad interaction. They just stop. Perceived Error is LangSmith's new managed judge that reads thread evidence and flags where your agent went sideways. Trained in-house, outperf

We’re excited to announce that 𝗛𝗮𝗿𝗿𝗶𝘀𝗼𝗻 𝗖𝗵𝗮𝘀𝗲 @hwchase17, Co-Founder & CEO of @LangChain, will be speaking at VapiCon 2026! 🎙️ Harrison co-founded LangChain with the goal of making it easier to use LL

Big step forward for Engine! Some things I'm excited about in this release that we've shipped for our most avid users: - Engine now points more obviously to the issue in a trace. Engine identifies and

this is a practical guide (+ updated skill) on how we use real world data like traces + human feedback to make synthetic environments + evals so we can measure, harness engineer, and post-train our ag

Agree 100%. We started building agents and quickly realized we needed to build the system of record too. Agents need all the context and permissions you’d give an employee to complete work end-to-end.

last night i spent some time building a browser agent with stagehand from @browserbase and deepagents from @LangChain i wanted to build something that required pretty complex web navigation to prove t

Open bot! 🎉 Introducing 𝙾𝚙𝚎𝚗 𝙱𝚘𝚝 An open source Grok Bot that works with ANY agent harness, designed for real companies. It includes: - AI Coworkers - Generative UI - Computer use (remote/local) - Age

What's new in LangChain? 🚀 🧯 Standard exceptions: chat models now raise standard exception types across providers, so you can distinguish retryable errors (timeouts, rate limits, etc.) in a consistent

🎉 Introducing 𝙾𝚙𝚎𝚗 𝙱𝚘𝚝 An open source Grok Bot that works with ANY agent harness, designed for real companies. It includes: - AI Coworkers - Generative UI - Computer use (remote/local) - Agent-human h

'Agents = model + harness + context' is a much better mental model than treating the LLM as the entire product. The model provides capability, but the harness determines tools, permissions and behavio

LangSmith’s new Tuned Evaluators look pretty interesting. Perceived Error can flag agent mistakes in production by picking up on user corrections and unresolved conversations. What’s interesting is it

New Tuned Evaluators from @LangChain Versioned judges you just 'turn on' in a tracing project The 'Perceived Error' Judge flags conversations where the agent probably messed up (proxy for user feedbac

Online evals are difficult to set up correctly. Even if you know what you want to look for, it's an optimization question of which model to choose, how to tune the prompt, and how to think about the c

Perceived Error is one of the clearest signals that your agent is giving users a great experience. We tuned the model and prompt around signals that tend to carry across domains, like a user correctin

teams want to understand what their agents are doing but it’s hard to sift through all that trace data at scale but you can basically find any signal by fine-tuning a small, cheap model on data showin

the real unlock is not cheaper evaluation it is making specialized evaluators cheap enough to run continuously on production traces so evaluation stops being a launch checklist and becomes a permanent

🚀Today we launched LangSmith Tuned Evaluators, starting with Perceived Error. Tuned Evaluators run on production traces to catch undesirable agent behavior and attach feedback that you can use in your

😮 wow that’s huge 💰 🚀Today we launched LangSmith Tuned Evaluators, starting with Perceived Error. Tuned Evaluators run on production traces to catch undesirable agent behavior and attach feedback that

totally agree! here's how we architected deepagents to enable this deepagents runs connected to a 'backend'. this backend needs to expose filesystem like operations, but it does not have to be a files

@hwchase17 @LangChain I built an open-source CLI that scaffolds, runs, and deploys production LangChain agents — frontend and backend in one command. Every agent project starts the same way: wire up a

Have been thinking about building a custom harness because nothing out there quite meets my needs for scaling a fully hybrid human + AI company, especially when it comes to compounding learning loops.

new langchain oss release! What's new in LangChain? 🚀 🔌 Support for OpenAI's 3.0 SDK (using httpx2) ✨ Support for gemini-3.7-flash Plus a wave of core reliability fixes from external contributors: 🛠️

notes on 'How to do data' from th Zai team that others should steal as they build their own environments - 'Research agents collect task patterns from real work and turn them into runnable long-horizo

What's new in LangChain? 🚀 🔌 Support for OpenAI's 3.0 SDK (using httpx2) ✨ Support for gemini-3.7-flash Plus a wave of core reliability fixes from external contributors: 🛠️ Tool calling & structured o

I had a blast recording this @hwchase17, hopefully folks hear something valuable from how we’re building our agent harness! New Max Agency with @unifygtm CTO & co-founder @HeggieConnor. Some of my fav

i sit next to @HeggieConnor everyday. he is a simultaneously hilarious deskmate and a wicked smart CTO. i watched him absolutely CRANK on this launch for months all while keeping a smile on his face :

New Max Agency with @unifygtm CTO & co-founder @HeggieConnor. Some of my favorite tidbits: - how they cut 90-95% of costs two weeks before launch - why their subagents are just a function call. 🎧 Appl

our outbound harness is focused on delivering the best economics we can, we're continuing to push on how our agents work to get another 90%+ cost savings for customers great talk by @hwchase17 and @He

You don't stand up an agent. You upload a folder. Claude Code is a harness for your laptop. Managed Deep Agents is a harness for production. Want Slack? Add a file. Want a daily run? Add a file. Want

the @aiDotEngineer World Fair always one of the best events every year to talk to builders at the frontier of Research, Agents, Evals, Systems, etc A few weeks ago I gave a talk on - Continually Impro

really excited to see this release AND integrate it with deepagents!! https://www.langchain.com/blog/switchyard-agent-routing-benchmark Lightning strikes for continuous and long-run agents! Nemotron 3

we recently trimmed the deepagents harness base prompt by 65% (including tool info) it shows — deepagents is cheap! We ran DeepSeek V4 Flash through 4 more agent harnesses (Hermes Agent, Pi Agent, Pri

Stripe just published how their company-wide AI agent works. The bar for building one just dropped to one engineer and one week It is called Kai. Their own words: a coding agent for non-engineers. You

the term “RLM” (recursive language model) got a lot of buzz this week, but this idea is not new! @a1zhang wrote the og RLM paper 10 months ago! thats like 5 agent-years! would highly recommend followi

I think of it like this: If you are building a simple agent with custom tools in your app -> langchain create agent If you are simply doing some LLM analysis or synthetic data creation -> langchain in

RIP to every 'which framework should I use' thread. harness on top, framework in the middle, runtime at the floor. one stack, three heights, fully composable. paste these four images into claude and a

Love the latest string of releases and the moves that the Langchain team has been making. Langchain's React implementation was the the first ever agent i built though their framework couldn't compete

Harrison Chase urged developers who want an open‑source starting point for building intelligent systems to use LangChain AI’s `open-swe` GitHub repository, emphasizing the importance of “owning your i

great write up, evals are hard! here are 2 broad buckets we use to evaluate agents: 1. Measure the State of the World 2. Agent as a Judge on the Trajectory 1. Measure the state of the environment befo

A post of ours on vendor lock in and LLMs. We don't like the growing trend of AI companies quietly hiding your data while stripping away your control. We think that's bad for users and bad for the eco

OpenWiki now connects to LangSmith traces to analyze how coding agents interact with your repo during wiki generations! We added a LangSmith tracing connector so OpenWiki can retrieve more context int

.@UseApolloio will be at Interrupt NYC. Apollo's AI Assistant is one of the earliest production multi-agent systems on LangGraph. Their team will share how they migrated it from a hand-rolled supervis