Knowledge catalogue
'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
This r/MachineLearning post discusses the landscape of frameworks and tools used to support benchmarking of LLMs and agentic AI systems, covering how to systematically evaluate model capabilities beyo
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
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
Have an idea for an experiment? Hermes can now write conference-grade research papers alongside you. Media introducing Autoreason, a reasoning method inspired by @karpathy's AutoResearch which extends
Nous Research announced Hermes Agent, an AI agent system that represents the culmination of approximately nine months of development work despite appearing to emerge suddenly. The release highlights 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
A community-built, free, open-source CLI coding agent shared on r/ollama, specifically optimized for local LLMs with 8k context windows to help developers work within the more constrained token limits
A Reddit post from the r/ollama community demonstrating a practical experiment in which a user ingested the narrative content of *The Godfather* into a structured knowledge graph and then queried it u
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
I taught AI agents to 20,000 people for free, and for the next 12 hours, you can watch that exact same workshop replay. It’s 2026. You need to know how to build AI agents. I got you. If you’re a mom r
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
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
Little quality of life update: Skills should be called more proactively now! ~20% more likely to load the right skill for the job! And, of course, in Hermes Agent fashion - it did the prompt improveme
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
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