Search: “harrison-chase--x”

One of my favorite features we’ve shipped in Fleet recently is the presentation viewer. Fleet agents are very good at writing code, so we thought why not have a built in way for them to design slides,

Round 2 for Max Agency! The Listen team know what they're doing. Check it out 👇 🎙️ Talked to @ListenLabs co-founder + CTO @florian_jue in the latest Max Agency. Really enjoyed hearing about the archit

running 3 async deepagents in parallel for real time investigation for manufacturing plant downtime. I believe these are the most capable agents in the world right now @LangChain @hwchase17 @lguaman.

🎙️ Talked to @ListenLabs co-founder + CTO @florian_jue in the latest Max Agency. Really enjoyed hearing about the architectural decisions behind their agents, their self-reviewing feedback subagents,

The file browser is a huge unlock, but even better is Fleet's presentation renderer You can have Fleet build complex, beautiful presentations in code, then use the slideshow viewer to show them off! N

tldr: claude code changed some harness settings which degraded perf. these small harness tweaks can matter a lot! 1. default reasoning high -> medium 2. bug that accidentally evicted thinking blocks o

Try out Fleet's presentation feature, and impress your colleagues with y̶o̶u̶r̶ Fleet's slide design skills today! http://langchain.com/langsmith/fleet One of my favorite features we’ve shipped in Fle

a bunch here where I’m saying ok Garry’s kinda right?! 👀…in some ways :) we’re making this loop much easier to close out of the box soon If more people get into evals & traces to ground self-improving

I predicted in January that 'harness' would be the AI buzzword of H12026 - seems accurate so far. Everyone is talking about harnesses but I'm not confident that the majority of people (including me!)

LangChain is introducing new testing tools and capabilities that will help developers with test guidance, organization, and completion criteria—moving beyond just providing testing infrastructure to a

This post likely clarifies the distinction between testing platforms and agent platforms, suggesting that a particular tool or framework (possibly LangChain, given Harrison Chase's role) is better cha

OpenAI is shutting down text-embedding-3-small?!? I strongly believe that if you shut down a closed-source embedding model that you should open-source. Imaging the trillions of tokens that will no lon

This post likely identifies and discusses 15 technology companies that are considered to have exceptional concentrations of talented employees, based on analysis or data from Paraform Talent. The list

The Never Ending Lore of Harness w/@Vtrivedy10⚡️ Viv brought some amazing perspective around harness design, evals, file systems, RL Envs and so much. 0:00:00 - INTRO 0:03:30 - PhD at Temple Universit

🔒`deepagents deploy` now supports custom auth: turn a single deployment into a multi-tenant platform. per-user resource and thread isolation, access control, and your own auth provider, all without sp

Every docs team I've been on ends up punting on the same project: making code samples in docs testable in CI. We know it's important but the implementation cost is too great. Skills + deep agents fina

fun off-the-cuff harness + agent engineering sesh with @dexhorthy @vaibcode @GeoffreyHuntley - a lot of good harness/agent design we’re doing today is still good context engineering, remember tool cal

This episode of 'AI That Works' discusses practical approaches to harness engineering in AI systems, likely focusing on techniques for effectively prompting and controlling AI model behavior beyond ma

This is an informal social media post expressing enthusiasm about an upcoming development or project, tagging several notable figures and companies including Harrison Chase (LangChain co-founder), And

I agree, and I will add to this. Companies should own their memories, skills and other resources agents need. Use Claude code, codex, copilot, etc for intelligence but your execution layer should plug

I have done no promotion at all for our @LangChain for Beginners course and I just checked and it has 371 stars on GitHub🌟 Working on updating some of our docs today and at some point will do a video

The CoALA paper proposes a classification system for agent memory that distinguishes between semantic memory (facts and concepts), episodic memory (specific experiences and events), and procedural mem

Interrupt is 3 weeks away really excited for our 2nd agent conference. great lineup including @AndrewYNg, @Cisco, @box, @clay, @MongoDB, + more. tickets are going fast! we sold out last year, and it w

LangChain received Google Cloud's 2026 Partner of the Year Award in the Marketplace: Agent Platform category, recognizing its contributions to the cloud ecosystem. The award highlights LangChain's pro

Lyft built 8 agents that resolve 35% of customer issues end-to-end. That stat sounds crazy, but it's the kind of numbers you see when teams actually close the evals feedback loop. Looking forward to I

memory ended not being valuable in ChatGPT possible this exact implementation isn't valuable either but some memory implementation will be valuable, will create a bunch of lock in. and everyone is rus

The post likely discusses the importance of open standards in AI and software development, advocating for transparency and interoperability rather than proprietary solutions. Harrison Chase, co-founde

This post by Harrison Chase (creator of LangChain) likely discusses the concept of statements or propositions that are 'probably true' and advocates for keeping such discussions or determinations open

Check out our repository below, self host your own version of claude code for investment in just three commands! https://github.com/ginlix-ai/LangAlpha LangChain Community Spotlight: LangAlpha 💼 A cod

developing an agent is a harness problem. deploying an agent is a runtime problem. here's a guide to the runtime capabilities that keep agents running in production and the infrastructure that enables

Going from local to production with agents can be tricky, especially when you go from a single user (you, the developer) to multi-tenant systems. Great blog from Sydney on best practices for doing thi

good point about another benefit of open memory @hwchase17 Memory being open isn't just about lock in, it's about auditability. If a bad memory gets injected, you need to be able to see it, revoke it,

This post likely discusses the infrastructure and practical considerations required for deploying AI agents capable of handling long-horizon tasks—those requiring multiple steps and extended planning

LangSmith Insights Agent helps Credit Genie interpret feedback from customers at scale, and continuously improve their product. https://www.langchain.com/blog/credit-genie-insights-agent-financial-ass

memory will be the great lock in and everyone knows it, so are rushing to get there first memory should be open! Last week, we released a preview of memories in Codex. Today, we’re expanding the exper

opus 4.7 dropped last week with two new features: xhigh effort: more compute when you need it task budgets: the model knows its limit and wraps up gracefully, this is a great example of giving the mod

some interesting trends in terms of LLM adoption 👀 📊 The latest from the LangSmith Signal: We looked at API call volume and developer adoption trends across LLM providers using LangChain open source t

The LangSmith Signal report analyzes API call volume and developer adoption trends across major LLM providers, leveraging LangChain's open-source data and observability platform to track usage pattern

there’s clearly some confusing conflicts + double-think going on in AI between: 1. Closed labs saying use our harness, it’s naturally post-trained and gets the best out of models by being “in-distribu

this is probably the best writeup we have on what makes agents production grade - there’s a lot of failure modes for agents we’ve learned over the years, most of which are articulated here Also it’s b

This guide addresses the infrastructure and operational requirements for deploying long-running AI agents in production environments. It likely covers deployment strategies, monitoring, scaling, and b

we want to help you navigate all the stuff that comes with shipping an agent to production - so we wrote a guide! this guide packages learnings across our + customer deployments, building open source

This post discusses the integration of LangChain's Deep Agents framework into Toad, noting that the implementation functioned without requiring significant modifications or troubleshooting. The entry

Awesome post by @LangChain team on “How We Made Our Docs Test Themselves”. Devtools companies that grok how important *technically accurate* docs are both to humans and AI coding agents outperform on

LangChain Community Spotlight: Saving $1M in LLM Costs 💰 Gustaf, an AI Engineer, shows how he reduced a production RAG chatbot's costs by 90% and improved latency by 82% through caching, intelligent r

New version of Toad has landed. Added @LangChain Deep agents and https://crow-ai.dev/ The diff view has some additional settings to play with. You can enable annotations and line wrapping if you don't