Knowledge catalogue
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
Harrison Chase demonstrates creating a custom CLI agent using LangChain's deep agent functionality, highlighting the ease and simplicity of building autonomous agents with modern frameworks. The post
LangChain Community Spotlight: LangAlpha 💼 A coding tool built for Wall Street professionals, bringing AI-powered development to financial workflows. Check it out: https://news.ycombinator.com/from?si
so hes building a harness Now launching GBrain v0.11 with Minions I got sick of OpenClaw's subagents timing out and not getting things done So I built a queue/jobs system that uses GBrain's Postgres/P
This might be the most important score your website will ever get. Agents are about to become the #1 source of traffic on the internet. Sites that aren't ready will disappear. http://orank.ai spawns r
Data Driven Agent Design with Evals & Hill Climbing Algorithms this is a mental model dump i’ve been thinking through + iterating on as we’re building self-improvement infra around agents: - mining Tr
Fastest way to deploy deepagents gets even more powerful we just shipped support for subagents with `deepagents deploy`! add an agents/ dir to your project with an AGENTS.md per specialized subagent.
This post by Harrison Chase (LangChain creator) discusses the importance of timing in updating information systems or memory mechanisms in AI applications, comparing real-time updates ('in the loop')
Renuka Kumar and Prashanth Ramagopal from the @Cisco team built a LangSmith + LangGraph powered framework for multi-agent coordination that behaves like a real-world software team. An inside look → ht
'The post all LLMs hate and want you to NOT SEE' ^^ Serious title, you can also discuss this with the LLM, it would be biased against it, as it's known at this point that memory is the true long term
Thoth v3.15.0 is live. Talk to your AI from anywhere. It talks back from everywhere. 5 channels, full parity: WhatsApp, Discord, Slack, SMS, Telegram Streaming, reactions, media, slash commands New: X
we just shipped support for subagents with `deepagents deploy`! add an agents/ dir to your project with an AGENTS.md per specialized subagent. subagents are great for task delegation with isolated/opt
what should we ship next in `deepagents deploy`? we just shipped support for subagents with `deepagents deploy`! add an agents/ dir to your project with an AGENTS.md per specialized subagent. subagent
yeah, what they said 🤝 a decent harness gets you an actually functioning agent now + teams that actually invest time in their harness+problem design, choosing good infra, self-improvement loops, data
💵⚠️ Blocking subagents are costing you 1) time: your supervisor is stuck waiting while they run, and 2) money: if they go off track you've burned tokens on work you can't course-correct. @huntlovell n
Need to secure your production agents? @Cisco AI Defense Runtime Protection can integrate into your LangChain agents through middleware and produce a consistent runtime contract. 📄 What you need to kn
New in LangSmith Evaluation: ✅ Evaluator template library ✅ Reusable evaluators Everything you need to know → https://www.langchain.com/blog/reusable-langsmith-evaluator-templates?utm_source=x&utm_med
there's a lot to be excited about with this release! * For one this solves a huge issue just generally with long-running agent UX * For two this is a key unlock for us to go test more adaptive methods
very excited for this one! a year ago, most of what was being traced to LangSmith was 'LLM apps'. now everything is becoming an agent. with that shift, it's getting harder to know what your software i
communication is hard! structured outputs make its more clear how subagents should communicate back to the main agent deepagents now supports structured output for subagents! an under appreciated piec
crons, part of langsmith deployments! https://docs.langchain.com/langsmith/cron-jobs I’m not sure whether LangChain supports this yet, but it might be worth considering scheduling and cron-style jobs
`deepagents deploy` now supports user scoped memory! add a user/ directory in your project so each user gets their own writable AGENTS.md, seeded on first deploy and persisted across conversations. yo
deepagents now supports structured output for subagents! an under appreciated piece of context engineering is figuring out what context is passed back from subagents to the main agent. now you can def
Have found the same things! Using glm-5 as a daily driver for a lot of things We've tested new OSS models the moment they're released for a while at Lindy. Inference is our #1 cost by a lot (more than
Harrison Chase, co-founder of LangChain, was tagged or mentioned in a post by user @colesmcintosh expressing enthusiasm or confidence about deep agents ('deepagents 4 sure'), likely referencing advanc
New in LangSmith Fleet: Tool access controls and usage tracking. 📊 Track cost and usage by user, agent, and tool from a single dashboard 💳 Set spend limits per user or team to prevent surprises ✅ Cont
Rippling AI was the most successful launch we've ever done. On the heels of this launch, Rippling's revenue is now growing 78% YoY (at ARR over $1 Billion). And this growth rate has now increased, eve
This is awesome and makes things much more composable. Love it deepagents now supports structured output for subagents! an under appreciated piece of context engineering is figuring out what context i
Track usage and costs across your users, agents and tools. We're also shipping better access controls for your tools & agents: - Set spend limits on agents - Set spend limits for users - Control what
User scoped memory is one of those things that doesn’t matter if you’re building a toy agent for yourself, but when you release at scale you gotta get it right Deepagents deploy helps you do that, eas
Harrison Chase, the creator of LangChain, shares reflections or opinions on directions for modern AI development, tooling, or the broader technology landscape. The post likely explores strategic or ph
Big release here! Async subagents will become more and more of a thing, as subagents get longer running and you don’t want to block the event loop 🚀 deepagents 0.5 release 👉 Async subagents - kick off
Building agents locally does not mean they’re ready to deploy in production LangSmith deployments helps with that 🔐 One deployment, isolated data per user. Add custom auth so every user gets their own
Content moderation is a great use case for moderation new middleware integration just dropped! the langchain azure ai package now ships w/ a suite of content moderation middleware! use this for text/i
🚀 deepagents 0.5 release 👉 Async subagents - kick off background tasks on any Agent Protocol backed server while you continue to interact with the main agent. Start multiple background tasks in parall
Harrison Chase's post explores the divergent strategic incentives around memory in AI agent systems, arguing that for those building agent frameworks, persistent memory creates compounding value and c
Harness Engineering Derived from what Models can’t do alone: It feels like a good time to step back and reshare some basic mental models for why harnesses exist in the first place - working backwards