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Harrison Chase, the creator of LangChain, shared an article discussing agent harness design and memory architecture for AI systems. The post highlights approaches to structuring how agents manage and

Harrison Chase shared a post on X (formerly Twitter) describing a resource as a very helpful overview that also presents a clear framework for understanding Anthropic's recent positioning toward OpenC

very memgpt / sarah wooders coded. memory isn’t a layer, it is the system. most teams think they’re choosing a model, but they’re really choosing where their memory lives and like ben thompson says, o

without memory, ux with an agent is really bad memory is what makes an experience with an agent feel personal and optimized i'm doing a bunch of research re how folks are building memory into their ag

a big part of agent harnesses is how they interact with context memory is just context its therefor impossible to separate harness from memory - as @sarahwooders says, 'memory isn't a plugin (it's a h

check out thoth - agent harness with sota memory built on langgraph Memory is core to your system. The Assistant or Harness needs to be built around it. Local, Internal & Eternal. SOTA Memory is integ

Harrison Chase, co-founder of LangChain, wrote a post in a similar style to a piece by Sarah Wooders, focusing on the concept of 'your harness, your memory' in the context of AI agents and memory syst

For AI agents, when you want every last bit of what is possible, open source is turning out to be the only way Hot take: I can't see any startup building their critical core operations on Claude Manag

For once I actually agree with the constructive argument here. There are more approaches that allow you to own your own destiny. Memory is just context after all and you can provide that at many place

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 the API line It’s more important

Great breakdown of how model providers are platformizing their AI/agents. A lot of people will take the convenience of going all in on a provider, but they will be locked in and giving up data control

Great piece. The lock-in point is the one nobody talks about enough. If your agent’s memory lives behind someone else’s API, you don’t have a product. You have a dependency. Learned this early buildin

Hot take: I can't see any startup building their critical core operations on Claude Managed Agents or any proprietary harness as investable. The past weeks have shown why it's critical to build on top

Harrison Chase, the creator of LangChain, shared a post on X (formerly Twitter) likely related to AI agent development, LangChain updates, or the broader landscape of large language model applications

@hwchase17 Agent harnesess will simplify multi-agent orchestration and it is finally the next big thing, albeit it looks different from what we had imagined 3 year ago (AutoGen & CrewAI), 2 year ago (

Harrison Chase, co-founder of LangChain, discusses the concept of the agent 'harness' as the core architectural component where an agent's decision-making logic and identity reside. The post expresses

@hwchase17 @sarahwooders like a the old children's school flipbook - each one wakes up, takes in the context(memory) via harness does its thing, then rests.... next flip book page, the harness manages

i agree there should be managed agents, i just think they should be built on open harnesses and open memory standards @hwchase17 Good article! I half agree :) the problem is that as agents become used

If you don't own the memory, you don't own the agent: - memory is what makes your agent get smarter over time - without it, anyone with the same tools can copy your agent overnight - with it, you buil

if you've read software history everything in your bone tells you open models have to win we are just in a weird anthropic fanboy moment we're seeing that open source models are getting good at file o

Harrison Chase, co-founder of LangChain, shared enthusiasm for a blog post about agent harnesses, describing it as one of the clearest and most practical explanations of why they are a lasting paradig

LangSmith MCP is invisibly underrated One hour to improve an agent from zero observability to 78% cache hit rate - Prompt costs down 70% The single most useful MCP tool for building agents, ever 🔥 htt

Memory + harness, or as @matt_slotnick put it, memory + intent. Something I describe a lot about @usebrief, that *why* you remember something is more important than pure recall. A moment that’s trivia

Harrison Chase, co-founder of LangChain, shared thoughts on AI memory and the risks of vendor lock-in when major AI providers like OpenAI and Anthropic control user memory systems. The post highlights

memory isn't a retrieval widget. it's write policy. the harness decides what survives compaction, what gets promoted, and what becomes reusable state. https://x.com/hwchase17/status/204297850056760973

Most illuminating graph I have seen for definition of harness. Memory and context are deeply coupled with harness. In my humble opinion, how they are injected and managed are one of most important pie

Open Everything 🤝 Own your intelligence 🤝 Builder Choice We’re in the v0.1 of deploying agentic intelligence across the economy. Agents are data generating beasts! Experiential Memory: Each piece of g

Relying on model providers' stateful APIs or harnesses creates lock-in: switching models means losing your agent's memory -- a cost that only grows as agents get better at learning a big part of agent

Harrison Chase, co-founder and CEO of LangChain, received positive feedback from a follower referencing a distinction he made at a Daytona conference, suggesting he regularly shares accessible and wel

the fact that http://pi.dev agent is so good, with virtually no sophisticated harness whatsoever, is a testament to the fact token vendor (codex/claude) agents are overrated. highly. today's moat of c

The most important post I've read all week by @hwchase17 . Memory, particularly memory consistency is the biggest performance inhibitor to AI agents today. As we interact with different harnesses and

the team spent a lot of time totally revamping docs with care❤️ ofc for LangChain+deepagents DX but lots of ppl use them as general learning guides on patterns across Agents, Context Eng, Infra, Prod,

this is a good point around taking advantage of model/api provider features i agree that prompt caching is great! we make sure to use it in deepagents! but that alone doesnt lock in - you can switch p

This is the right frame. We’re currently designing our agent memory platform, and the hardest part isn’t storage — it’s deciding what to remember, when to retrieve, and how to keep context clean. All

I was unable to retrieve the content of that specific X (Twitter) post, as web search results did not surface the tweet or its content. X.com posts are generally not indexed in a way that makes the...

This post is so so good. We are at this interesting point where we’ve started to really figure out ‘memory’ with these LLM-based systems. And @hwchase17 is totally right - memory is basically just con

time to help builders own their intelligence, open everything 🤝 the data produced and Experiential Memory gained from every agent interaction is the one of the most valuable things you can own to impr

What if owning the memory layer goes far beyond escaping lock-in? What if memory becomes fully editable intelligence? You could debug bad patterns, reinforce good ones, and crucially synthesize new on

LangChain's concept of 'your harness = your memory' explores how the surrounding infrastructure and framework around an AI agent effectively functions as its memory system. The post likely discusses h

Agent harnesses are spark LangSmith is databricks harnesses seem to be the abstraction that encapsulates all of the 'business logic' or 'business connections' into a coherent unit that you can iterate

Agent skills are great. I wanted to share some of my favorites from Jesse Vincent's 'Superpowers' skill pack 𝚠𝚛𝚒𝚝𝚒𝚗𝚐-𝚙𝚕𝚊𝚗𝚜 skill produces much better plans than any harness' built in plan mode that I'

The specific tweet (status ID 2042726390617764234) could not be retrieved directly, as it does not appear in available search results and X/Twitter requires authentication to access individual post...

**deepagents** (github.com/langchain-ai/deepagents) is an open-source, MIT-licensed agent harness built on LangChain and LangGraph, equipped with built-in task planning, a filesystem backend, subag...

As agent systems scale, the control layer becomes the main source of complexity. What starts simple turns into coordination and composition problems. I built langchain-middleware-stack to better struc

LangChain launched **Deep Agents Deploy** in beta as an open-source, model-agnostic alternative to Anthropic's Claude Managed Agents. It is designed to be the fastest way to deploy a model-agnosti...

Deep Agents deploy gets you the best AI infrastructure operators in the game! cc @h_dhanushkodi @WHinthorn Deep Agents deploy gets you: - Deep Agents harness - Sandbox of your choice (@daytonaio , @mo