Search: “harrison-chase--x”

“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

Harrison Chase discusses the concept of 'memory lock-in' in AI agent frameworks, arguing that the switching cost doesn't occur at the point of adoption but rather accumulates over time as agents build

Memory makes your agent smarter over time. The agent harness is key to the memory layer. You can't bolt one onto the other after the fact. Every decision the harness makes - what goes in context, what

Memory features in AI platforms represent a strategic mechanism for increasing user retention and platform lock-in, as personalized context and learned preferences become increasingly difficult to mig

Model providers don’t lock you in with the API. They lock you in with your own data. Memory is the moat. If you don’t own your agent’s harness, you don’t own your agent’s memory. And switching means s

Most people building AI agents don't realize this until it's too late. Your agent's memory isn't a feature. It's your moat. 1. Closed harness = they own your memory 2. Switch models → lose all context

Harrison Chase, co-founder of LangChain, likely discusses the concept that AI agents and systems lacking persistent memory cannot truly learn, adapt, or maintain continuity across interactions — makin

Harrison Chase, co-founder of LangChain, discusses a trend in the AI industry where agent harnesses are becoming increasingly closed and proprietary, with memory systems locked behind vendor-specific

Harrison Chase, associated with LangChain, shared or was cited in a post on X criticizing OpenAI's server-side memory compaction and Anthropic's handling of context as problematic precedents for AI me

Really enjoyed reading the post - I'm exploring Deep Agents and Pi harness's today also motivated by @DiegoARRG post. Harness, Memory and Skills in house - LLMs if you cans afford too except for compl

Harrison Chase, co-founder and CEO of LangChain, made a post on X affirming a point about where value lies in AI systems, specifically arguing that value is found 'in the harness' — referring to the s

Harrison Chase, the creator of LangChain, shared or was referenced in a post praising a piece of writing attributed to him for its clarity and accessibility, notably avoiding technical jargon. The con

The differentiating factor between a prototype and an autonomous system is no longer solely the underlying model weights, but the sophistication of the orchestration layer and its capacity for continu

the most important abstraction in AI agents isnt the model — its the harness it orchestrates tools, memory, prompts. this is where all the alpha is deepagents is our take: built-in tools, memory, smar

This 1-min clip from the creator of LangChain + this 10-min read from him will teach you more about what actually matters in AI agents than everything you've scrolled past this year. Watch this. Then

Harrison Chase, the co-founder and CEO of LangChain, shared or endorsed a post on X (formerly Twitter) expressing strong confidence in a particular approach, tool, or method, stating it 'works very we