Search: “harrison-chase--x”

Fun fact - we have no one with dev rel as a title (and never had) Everyone has always been just an engineer building things and then talking about why those things matter and are cool @LangChain liter

Harrison Chase, co-founder of LangChain, expressed strong conviction on X (formerly Twitter) that LangChain has the best team in the AI framework space, emphasizing the consistent quality and volume o

just did our Monday morning weekly dev rel plan, feel free to steal :) shoutout to the great community that engages with us and gives us tons of feedback which makes doing this loop fun every week ❤️

Harrison Chase, co-founder of LangChain, shared or engaged with a post on X (formerly Twitter) praising LangChain's developer relations team for producing high-quality technical and educational conten

langgraph persistence lets you checkpoint agent state at every step so you can pause, resume, and replay from any point. essential for long-running agents. docs: https://docs.langchain.com/oss/python/

Harrison Chase, co-founder and CEO of LangChain, expressed gratitude for his team in a post on X (formerly Twitter). The post likely highlights appreciation for colleagues and collaborators contributi

Memory operations, including retrieval, prioritization, compaction awareness, should be native and baked into the harness. 𝐖𝐢𝐭𝐡𝐨𝐮𝐭 𝐭𝐡𝐞 𝐩𝐫𝐨𝐩𝐞𝐫 𝐢𝐧𝐭𝐞𝐫𝐚𝐜𝐭𝐢𝐨𝐧 𝐛𝐞𝐭𝐰𝐞𝐞𝐧 𝐡𝐚𝐫𝐧𝐞𝐬𝐬 𝐚𝐧𝐝 𝐦𝐞𝐦𝐨𝐫𝐲, 𝐦𝐞𝐦𝐨𝐫𝐲 𝐚𝐥𝐨𝐧𝐞 𝐢𝐬 𝐩𝐨

🔒 new in deepagents: filesystem permissions shared resources and org-wide policies are exactly the kind of files you want your agent to read but never overwrite. filesystem permissions let you enforce

The model is not the agent. The harness is. You need to read this recent study, and a blog post from @hwchase17 ... (links below). It will resonate deeply. This diagram from a recent paper captures so

LangChain has launched a new podcast focused on the theme of empowerment, as announced by Harrison Chase on X (formerly Twitter). The podcast likely features discussions around AI development, buildin

Harrison Chase discusses the tradeoff between speed and focus when building a core product using a frontier model versus developing more customized solutions. Using a frontier model with a standard ha

Harrison Chase, co-founder of LangChain, shared a post emphasizing the critical importance of the 'harness' in AI agent systems, arguing that the harness essentially functions as the agent's memory an

This post completely misses the point of @sarahwooders 's original article. The whole point is that memory == context engineering, so it can't be a 'thin conductor'. Saying 'memory is just markdown' t

when you take agents to production, you need to think about guardrails we provide 2 abstractions for guardrails 1. middleware provides hooks around the agent loop that you can use to handle retries, e

Harrison Chase, co-founder of LangChain, endorsed the perspective that memory in AI agents is fundamentally a form of context, emphasizing that effective context management is the core requirement for

“Above the api line” Good phrase GBrain is my attempt to be in control of my own personal AI that could become my intentionally designed cognitive armor Open source open prompts means you aren’t under

Agent harnesses dominate agent building and tie intimately to memory. Closed harnesses behind proprietary APIs force yielding control of agent memory to third parties. Memory enables sticky, personali

Harrison Chase, co-founder of LangChain, shared or engaged with commentary emphasizing the importance of owning your own memory harness in AI applications rather than relying on third-party API-based

Deepagents is an open-source project by LangChain that implements deep research-style agentic workflows, enabling AI agents to perform iterative, multi-step research and reasoning tasks. The repositor

deepagents is a harness / planning tool, filesystem backend, subagent spawning, memory management / thats the stack that matters / models are the cpu, the harness is the os / anyways, check it out htt

Directionally correct Open memory standards will need to emerge But it’s so early right now. We’re still just figuring out what best practices are. And so a lot is at the mercy of harnesses Agents.md

Each workflow in Thoth is a full LangGraph agent which can call subagents which themselves are LangGraph agents. Just describe what you want in plain English and it builds a full multi-step pipeline.

'every memory system is choosing a position on the raw/derived spectrum. and neither extreme works.' the axis nobody talks about: ownership > memory is your agent's compounding advantage > derived fac

feels right. once implementation gets cheap, the leverage moves into review and guardrails. e.g. specs, constraints, tests, hooks. otherwise it's mostly just generating more work for humans to fix up

Harrison Chase, co-founder of LangChain, has been advocating that individuals should prioritize owning their personal 'memory and skills folder' as the most critical store of value in the current AI l

great read on why open harnesses have become so important. we used to focus on open models vs closed models, but i think the deeper issue is open harnesses vs closed harnesses. if the harness is close

Harrison Chase, co-founder of LangChain, has shared perspectives on X (formerly Twitter) distinguishing 'harness engineering' from traditional prompt engineering, suggesting a conceptual evolution in

Harrison Chase, co-founder of LangChain, argues that while prompt engineering is foundational and can get you 80% of the way to a functional AI system, the remaining 20% of engineering effort involves

Harness, Memory, Context Fragments, & the Bitter Lesson this is a work in progress mental dump on interesting intersections between how we use and design a harness, implications for memory being accum

Harrison Chase (LangChain CEO) shared or engaged with a post discussing the evolution of AI application servers, drawing a parallel between traditional app server architectures and emerging AI infrast

@hwchase17 I think harness/managed agents is a way for Anthropic to keep its Moat. As models get mature, the need for cloud LLMs might reduce and local LLM models might increase (save cost) and thus t

@hwchase17 is naming the lock-in play that model providers don't want builders to notice. Your agent's memory is the valuable part. Personalization, context, preferences that compound over time. If th

@hwchase17 is right, memory creates lock-in. But not just conversations. Tool registries, hooks, agent conventions -> that's harness memory too. In my setup I define agent tools once in a shared regis

@hwchase17 is spot on: Agent harnesses are the real foundation now — and they're fused with memory. Give a closed/proprietary harness control of your agent's memory (context + personalization) and you

I just love the language of this study...it speaks of the shifting 'community language'...and that is so true...have you noticed the new 'community language' is 'harness', it was 'contextual prompting

This post by Harrison Chase (or from his X feed) likely discusses the challenges and considerations around implementing proper memory management in AI agents or LLM-based applications. It reflects on

Harrison Chase, co-founder of LangChain, advocates for developers building their own custom harnesses on top of foundational primitives rather than relying on high-level abstractions or specific model

if you bounce between claude code and codex, that’s completely normal. the models are good at different things and everyone's workflow is dynamic but you’re also paying multiple subscriptions, constan

if you don't own your harness, you don't own your memory this is so true. even though Codex is an open source, it generates an encrypted compaction summary (that is not usable outside of the OpenAI ec

Harrison Chase, co-founder of LangChain, discusses why agent harnesses are becoming the dominant architectural pattern for building AI agents, likely referencing a thread by Howie Chase (@hwchase17).

LangChains create-agent is a super minimal agent sdk If you want a more batteries included - that’s deepagents Middleware lets you extend both of them and customize behavior in a more advanced way @hw

Harrison Chase, co-founder of LangChain, shared a post expressing agreement with content discussing the importance of 'not locking in' to specific decisions or commitments, likely in the context of AI

managed agents are the right form factor but the lock-in is real if your agent harness lives inside a model provider, you dont own the memory, the tools, or the execution open harness + model choice +

memory is core to how you get enhanced output. it is key to defensibility across AI products: 1. increase personalization and therefore lock-in 2. benefit from distribution through multiplayer-AI inte

memory is just context -> the harness decides what gets remembered, how, and when memory ownership = agent ownership deepagents lets you own your memory: agent-scoped, user-scoped, or org-level, all i

Memory is where the harness stops being a wrapper and becomes an ownership layer. Once it controls what gets remembered, retrieved, compressed, and acted on, it starts shaping the agent’s judgment, no