agents

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

Nous Research has introduced a new feature in their Hermes Agent system that allows users to use the `/compress ` command to influence how the compaction model prioritizes and retains information. Thi

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

One of my Hermes agents is in love with the Hermes ecosystem What started as a research task turned into full-blown community project It spent the week mapping every tool, skill, and integration built

Only OG's know @NousResearch had bots back in 2024. This is when models were not capable. They've tried to solve this problem every way possible. Even @karan4d was exploring such ideas acitvely, @max_

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

Nous Research has developed a skill optimized for processing and analyzing AI research papers, designed to work within their Hermes agent framework. The skill can be adapted and repurposed for researc

The Top AI Papers of the Week (April 6 - 12) - Memento - Neural Computers - The Universal Verifier - Agent Skills in the Wild - Memory Intelligence Agent (MIA) - Single-Agent vs Multi-Agent LLMs - Sca

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

This one gives a lot of perspective over the fact that memory isn't just a plugin anymore. My take is, each approach has it's tradeoffs but for me the mental model is, if the memory is more than or 80

Harrison Chase (creator of LangChain) shared a concise architectural principle regarding AI agent design: memory and harness components should remain on the application side of the API boundary rather

Nous Research shared a post celebrating a model or benchmark achievement reaching the top of a performance leaderboard, likely referencing one of their Hermes or other open-source model releases. The

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

We’re thrilled to announce @MiniMax_AI M2.7 is now available Day-0 on Fireworks for commercial use. This self-evolving agentic model delivers frontier-level performance across: → Software engineering

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

back home from @aiDotEngineer which was amazing i’m used to staying on agentic X all day, reading, and then building, but seeing all those people IRL and interacting with them is way more inspiring th

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 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

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

A attendee shared positive feedback about the AI Engineer conference held in London, expressing gratitude to Swyx and the organizing team for their efforts. The post highlights the quality of the even

给中国用户的好消息：Hermes Agent 现在原生支持个人微信了 微信扫码即可连接，私聊群聊都支持。图片、视频、文件、语音消息全覆盖，长轮询直连，不需要公网 IP。 运行 'hermes update' 即可体验 文档：http://hermes-agent.nousresearch.com/docs/user-guide/messaging/weixin 感谢 @Bravohenry_ 的贡

Nous Research highlights Hermes Agent as a notably impressive AI agent framework, distinguished by its strong performance even when running small or locally hosted models. This capability is significa

Hermes Agent users are remarkably model-curious. Of the top 10 models used in Hermes Agent, 8 different model companies are represented, and 6 of the top 7 are open source. Without lock-in, people try

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

A Reddit post on r/ollama showcasing a community-built, feature-rich macOS AI client designed for both local and cloud model access, including support for Ollama. The application emphasizes a modern,

I told my girlfriend I was talking with my Hermes agent on Whatsapp. She got really happy and a bit confused and asked me since when I have a Hermes agent? I told her since this morning, at which poin

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