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Your Nous Research Hermes agent has a 2,200-character memory. Its notes live inside the instructions it loads at session start, so the space never grows. Hit the cap and the next thing it learns gets rejected - to remember something new, it has to delete something it already knew.... show more

mem0

20,115 subscribers

18,852 просмотров • 2 месяцев назад •via X (Twitter)

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Комментарии: 14

Фото профиля mem0

mem02 месяцев назад

hermes memory setup → pick mem0. Built-in MEMORY.md and USER.md keep running alongside it by default. Three modes: Mem0 Cloud, self-hosted server via Docker, or fully OSS in-process with your own LLM, embedder, and vector store. Read more:

Фото профиля Art

Art2 месяцев назад

@NousResearch Been using mem0 for memory and lcm for compaction for several months now. It’s amazing!! Especially using turboquant compression plus a reranker model.

Фото профиля jhonny

jhonny2 месяцев назад

@NousResearch @grok what's the difference with between mem0 and honcho?

Фото профиля TSR ☀️

TSR ☀️2 месяцев назад

@NousResearch this looks promising... lemme try it

Фото профиля Claire Bennett

Claire Bennett2 месяцев назад

@NousResearch Fascinating approach to AI memory systems. The ability to manage context efficiently while maintaining useful knowledge is a key step toward more capable and reliable AI agents.

Фото профиля Jamie Yellow

Jamie Yellow2 месяцев назад

@NousResearch A 2,200-character memory that has to delete something old to learn something new isn't a bug. That's competition. A bigger box just moves the failure from eviction to retrieval noise. Retrieval accuracy is the first step. What stays isn't a capacity problem.

Фото профиля ShadowAguy

ShadowAguy2 месяцев назад

@NousResearch i've met humans with less memory and worse eviction policies.

Фото профиля CommanderROR

CommanderROR2 месяцев назад

@NousResearch How does this compare with Hindsight?

Фото профиля psam

psam2 месяцев назад

@NousResearch @Teknium would you recommend this?

Фото профиля Fiducial.ai

Fiducial.ai2 месяцев назад

@NousResearch Running Mem0 beside MEMORY.md creates a conflict path as well as extra capacity. Each recalled fact needs provenance and a rule for which store wins when they disagree.

Фото профиля nyk

nyk2 месяцев назад

@NousResearch 2,200 chars means every new memory is a delete decision.

Фото профиля Danielly Gomes

Danielly Gomes2 месяцев назад

@NousResearch @Teknium is this better than local honcho ?

Фото профиля Jessika Hyde

Jessika Hyde2 месяцев назад

@NousResearch Co lepsze to czy openviking?

Фото профиля Sattyam Jain

Sattyam Jain2 месяцев назад

@NousResearch The reject-at-cap behavior is the real bug, not the size. A silent write failure means the agent thinks it learned something it didn't. We hit this while building a Rust memory store, and the fix was to make eviction explicit and logged, so the agent can reason about what it lost

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Boris Cherny gave Claude Code a memory that has no database, no indexing, no setup, and 80% of Anthropic runs on it a CLAUDE.md "gets automatically read into context at the start of every session" 12:48 - CLAUDE.md loads into memory every session 13:15 - Keep memory short or it burns your context 15:48 - Memory stacks from you to your whole company 17:30 - The memory command shows what Claude remembers 19:33 - Hit pound and Claude remembers it forever the whole memory layer is files → drop a CLAUDE.md in your repo and Claude reads it in on every session → nest more in subfolders and it pulls them only where it works → stack them personal to project to enterprise so one config serves the whole team → hit the pound key mid task and Claude writes the new fact into memory itself the best coding agent's memory was never a database, it was markdown you can open and edit

Boris Cherny gave Claude Code a memory that has no database, no indexing, no setup, and 80% of Anthropic runs on it a CLAUDE.md "gets automatically read into context at the start of every session" 12:48 - CLAUDE.md loads into memory every session 13:15 - Keep memory short or it burns your context 15:48 - Memory stacks from you to your whole company 17:30 - The memory command shows what Claude remembers 19:33 - Hit pound and Claude remembers it forever the whole memory layer is files → drop a CLAUDE.md in your repo and Claude reads it in on every session → nest more in subfolders and it pulls them only where it works → stack them personal to project to enterprise so one config serves the whole team → hit the pound key mid task and Claude writes the new fact into memory itself the best coding agent's memory was never a database, it was markdown you can open and edit

NO1ennn

13,015 просмотров • 3 месяцев назад

My old Echo Dot run Hermes now, instead of Alexa. I built a Home Assistant integration that connects it to a Hermes profile, with its memory and tools. Code, setup skill and the hardware recovery notes are public. Nous Research

dippo

160,749 просмотров • 3 дней назад

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

THIS GUY CONNECTED HIS AI AGENTS TO HIS OBSIDIAN AND BUILT A BRAIN THAT LEARNS ON ITS OWN. HERE'S HOW TO BUILD IT Obsidian is just markdown files sitting in a folder. That turns out to be the perfect memory for an AI agent, because an agent can read and write those files directly. He wired his agents into the vault so they pull context from it, do the work, and write what they learned back. The notes aren't the point. The loop is, and it gets sharper every cycle How to build it: 1. Point an agent at your vault. The fastest way, no plugins, no API keys: open a terminal and run npx obsidian-mcp /path/to/your/vault. That exposes your Obsidian folder to Claude as a tool it can read, search, and write to. Add it to your Claude Code or Cowork config and restart 2. Confirm it can see the brain. Ask it: "list the notes in my vault and summarize what's in them." If it reads them back, the connection is live. Now it starts every task with everything the vault already holds instead of from zero 3. Give each agent one job and a write-back rule. Tell it: "research this, then save what you found as a new note in /brain with links to related notes." One agent researches, one summarizes, one plans. Each writes its output back into the vault 4. Close the loop. Add one line to every agent's instructions: "read /brain before starting, write your result back when done." Now each task leaves the vault richer, and the next run reads that before it works. It compounds instead of resetting 5. You only steer. Review what the brain produces, point it at the next thing. The agents handle the reading, writing, and connecting The edge isn't better notes. It's a brain that feeds itself, so the work gets sharper every cycle instead of starting over Bookmark this

Yarchi

58,643 просмотров • 5 месяцев назад

Have you checked out your Memory Graph in the Hermes Desktop? Team at Nous Research ships so much that even I miss some new features 😅 I think this has been out for a while, but the Memory Graph lets you see how your agent has developed, and how memories impacted skills. Check it out!

Have you checked out your Memory Graph in the Hermes Desktop? Team at Nous Research ships so much that even I miss some new features 😅 I think this has been out for a while, but the Memory Graph lets you see how your agent has developed, and how memories impacted skills. Check it out!

tonbi

136,038 просмотров • 3 месяцев назад

It is becoming f*cking dangerous: Claude Code has kept its own memory folder since February and you've probably never opened it 1. Open any project in Claude Code and type /memory 2. Check the auto memory toggle, it is on by default in local sessions 3. Pick the option to open the auto memory folder 4. Read MEMORY.md first, it is the index, one line per memory 5. Look at the files: one memory each, typed user, feedback, project or reference 6. Tell Claude "remember: always use pnpm, not npm" and watch it save a new memory 7. Edit or delete any wrong memory, it is plain markdown Only the first 200 lines or 25KB of MEMORY.md load each session, Claude opens the topic files when needed Ask Claude to remember what matters once, it stays until you or Claude edit or delete it Full guide in the article below

It is becoming f*cking dangerous: Claude Code has kept its own memory folder since February and you've probably never opened it 1. Open any project in Claude Code and type /memory 2. Check the auto memory toggle, it is on by default in local sessions 3. Pick the option to open the auto memory folder 4. Read MEMORY.md first, it is the index, one line per memory 5. Look at the files: one memory each, typed user, feedback, project or reference 6. Tell Claude "remember: always use pnpm, not npm" and watch it save a new memory 7. Edit or delete any wrong memory, it is plain markdown Only the first 200 lines or 25KB of MEMORY.md load each session, Claude opens the topic files when needed Ask Claude to remember what matters once, it stays until you or Claude edit or delete it Full guide in the article below

plusxbt

19,567 просмотров • 9 дней назад

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

HERMES AGENT LEARNS FROM ITS OWN MISTAKES. UPDATES ITS MEMORY. CREATES ITS OWN SKILLS. NO CLOUD. EVERYTHING STORED LOCALLY. THIS IS HOW THE SELF-IMPROVING LOOP WORKS. most agents start from zero every session. Hermes carries forward what it learned. THREE MEMORY SYSTEMS: 1. PROCEDURAL MEMORY (how to act) stored in ~/.hermes/skills/ as SKILL.md files. when the agent repeats a complex workflow, it saves the procedure as a reusable skill. next time the same task comes up, it follows the skill instead of figuring it out again. you can also create skills explicitly: "create a skill called video-prep that captures how I format my video scripts. spoken english, define jargon inline, no em-dashes, close with a catchphrase." the agent writes the SKILL.md. available as a slash command from that moment. Hermes ships with 90+ skills. the number grows the longer you use it. 2. SEMANTIC MEMORY (durable facts about you) stored in ~/.hermes/memory/memory.md the agent scans conversations for facts worth remembering. preferences, habits, corrections, project details. real example from the video: agent tried to scrape a YouTube channel. URL was wrong. it failed. it updated memory.md with the correct URL pattern so it never makes the same mistake again. you can also save explicitly: "save to memory that my favorite testing framework is pytest" the agent updates memory.md immediately. this file loads into context on every session. the agent knows you better every week. 3. EPISODIC MEMORY (chat history) stored in ~/.hermes/state.db (local SQLite). every conversation. every tool call. every result. searchable with FTS5 full-text search. "search our past sessions. what was the first thing I ever said to you?" the agent queries state.db and finds it. over time, auxiliary models consolidate episodic memory into semantic memory. distilling recurring patterns into durable facts. THE SELF-IMPROVING LOOP: every agent run follows this cycle: → you send a prompt → working memory loads: SOUL.md + memory.md + relevant skills + chat history → agent calls tools (terminal, browser, delegate_task) → agent completes the task, replies to you → AFTER the reply: agent checks "did I learn something worth saving?" → if yes: updates memory.md or creates a new skill → next session starts smarter than the last this happens automatically. you don't ask the agent to learn. it decides what to remember on its own. WHAT MAKES THIS DIFFERENT FROM CLAUDE CODE: Claude Code has memory too. but Hermes stores everything locally. no cloud. your data never leaves your machine. Claude Code doesn't auto-create skills from experience. Hermes turns repeated workflows into reusable procedures. Claude Code memory is instruction-based. Hermes memory is conversational and self-updating. over months of usage, Hermes builds a knowledge base of your preferences, your projects, your mistakes, and the procedures that work for your specific workflow. the agent that remembers your birthday also remembers why your last deploy failed. NO EMBEDDINGS. PLAIN TEXT. Hermes does not use embeddings or RAG for memory. skill and memory search runs on plain text keyword matching. simpler. faster. no vector database to maintain. works entirely offline on your local machine. DELEGATE TO CLAUDE CODE: Hermes can spawn a sub-agent that runs Claude Code in headless mode: "spawn a sub-agent using Claude CLI to build a Python script that fetches the top 5 Hacker News stories to markdown." Hermes delegates. Claude Code writes the code. result returns to Hermes. Hermes runs the script and delivers the output. use Hermes for orchestration. use Claude Code for heavy coding. both tools. not competitors. WHAT HERMES DOES NOT HAVE: no built-in eval or LMOps system. no LangSmith, no LangFuse integration out of the box. trajectory export and logs exist but there is no automated quality tracking. if you need eval, build it yourself or connect external tools. the loop is self-improving. measuring how well it improves is on you. comment LOOP and I'll send you the configs that control how fast Hermes learns and what it remembers. memory limits, skill auto-creation triggers, and the auxiliary model that runs the learning. Replace your entire team with 8 hermes agents👇

YanXbt

23,088 просмотров • 4 месяцев назад

Meet Hermes Agent, the open source agent that grows with you. Hermes Agent remembers what it learns and gets more capable over time, with a multi-level memory system and persistent dedicated machine access.

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Nous Research

4,002,245 просмотров • 8 месяцев назад

Big one for the Hermes Agent community. TinyFish is now a plugin in the Nous Research Hermes Agent. Your Hermes agent gets free Search and Fetch, so it can find and read the live web at $0. When a task needs real clicks, TinyFish Browser and Web Agent are one approval away.

Big one for the Hermes Agent community. TinyFish is now a plugin in the Nous Research Hermes Agent. Your Hermes agent gets free Search and Fetch, so it can find and read the live web at $0. When a task needs real clicks, TinyFish Browser and Web Agent are one approval away.

TinyFish

48,692 просмотров • 1 месяц назад

HERMES AGENT + OBSIDIAN IS A COMBINATION NOBODY IS TALKING ABOUT. Hermes ships with a bundled Obsidian skill. read, search, and create notes in your vault out of the box. why this combination is powerful: Hermes built-in memory is capped. MEMORY.md: 2,200 chars (~800 tokens). USER.md: 1,375 chars (~500 tokens). Obsidian vault has no cap. your agent writes research, session summaries, project context, and learned patterns as linked markdown notes. unlimited depth. the agent creates indexed notes by design. timestamps, backlinks, tags. every note connects to the knowledge graph. three ways to integrate: 1. BUNDLED OBSIDIAN SKILL (simplest) ships with Hermes. reads, searches, creates notes in your vault directly. hermes skills list | grep obsidian 2. OBSIDIAN MCP SERVER (deepest) 30+ tools: full-text search, tag lookup, note management, vault analysis, link analysis, orphan detection. add it via: hermes mcp 3. TELEGRAM + CRON → VAULT (always-on) set a cron job that writes daily summaries, research findings, or task reports directly into your Obsidian vault. your agent feeds the vault while you sleep. you review in Obsidian when you're ready. the unlock: Hermes memory handles what the agent needs to know per session (capped, injected). Obsidian handles everything the agent has ever learned (uncapped, searchable). short-term in Hermes. long-term in Obsidian. both accessible. both persistent. keep the vault scope narrow at first. start with one /Hermes folder. expand once you trust the workflow. 8 Loops Indise Hermes Agent👇

HERMES AGENT + OBSIDIAN IS A COMBINATION NOBODY IS TALKING ABOUT. Hermes ships with a bundled Obsidian skill. read, search, and create notes in your vault out of the box. why this combination is powerful: Hermes built-in memory is capped. MEMORY.md: 2,200 chars (~800 tokens). USER.md: 1,375 chars (~500 tokens). Obsidian vault has no cap. your agent writes research, session summaries, project context, and learned patterns as linked markdown notes. unlimited depth. the agent creates indexed notes by design. timestamps, backlinks, tags. every note connects to the knowledge graph. three ways to integrate: 1. BUNDLED OBSIDIAN SKILL (simplest) ships with Hermes. reads, searches, creates notes in your vault directly. hermes skills list | grep obsidian 2. OBSIDIAN MCP SERVER (deepest) 30+ tools: full-text search, tag lookup, note management, vault analysis, link analysis, orphan detection. add it via: hermes mcp 3. TELEGRAM + CRON → VAULT (always-on) set a cron job that writes daily summaries, research findings, or task reports directly into your Obsidian vault. your agent feeds the vault while you sleep. you review in Obsidian when you're ready. the unlock: Hermes memory handles what the agent needs to know per session (capped, injected). Obsidian handles everything the agent has ever learned (uncapped, searchable). short-term in Hermes. long-term in Obsidian. both accessible. both persistent. keep the vault scope narrow at first. start with one /Hermes folder. expand once you trust the workflow. 8 Loops Indise Hermes Agent👇

YanXbt

20,617 просмотров • 5 месяцев назад

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

you let your agent keep notes, because the alternative was explaining yourself every morning it stopped asking, and it started repeating things that had stopped being true every guide on this covers what to save and where to put it, private for one agent, shared for the team not one of them covers how a fact leaves the agent writes its own memory while it works, so a guess it made on a tired tuesday gets stored in the same shape as something you told it, and after that the two are indistinguishable i counted mine this morning: forty files, six of them about decisions we reversed months ago, all forty read into every session since you did not give it a memory you gave it a record with no way to be corrected

Lagosaur

13,908 просмотров • 2 месяцев назад

The barrier to deploying AI agents just hit zero. Akash Agents is a streamlined way to launch your OpenClaw or Hermes agent with a few simple clicks. Deploy instantly. No terminal, YAML, or cloud console required. Just your agent's logic, memory, and capabilities. Hermes by Nous Research ships with 40+ built-in tools, persistent memory, and subagent parallelization, making the experience best-in-class out of the box. → Try it for free at Built by our community member Sandeep Narahari

The barrier to deploying AI agents just hit zero. Akash Agents is a streamlined way to launch your OpenClaw or Hermes agent with a few simple clicks. Deploy instantly. No terminal, YAML, or cloud console required. Just your agent's logic, memory, and capabilities. Hermes by Nous Research ships with 40+ built-in tools, persistent memory, and subagent parallelization, making the experience best-in-class out of the box. → Try it for free at Built by our community member Sandeep Narahari

Akash Network

22,918 просмотров • 7 месяцев назад

you wrote a long careful note so it would remember the thing properly the note is the part that does not load what loads at the start of every session is the index line, the one sentence summary, and only the first two hundred lines of it or the first twenty five kilobytes, whichever runs out first the topic files underneath are read on demand, which means only if something in that one line makes it go and open them i checked mine: thirty nine index lines loading every session, thirty nine topic files loading none of the time the detail you were careful about is not context it is a footnote your agent has to choose to open and the sentence you dashed off in three seconds is the whole memory

you wrote a long careful note so it would remember the thing properly the note is the part that does not load what loads at the start of every session is the index line, the one sentence summary, and only the first two hundred lines of it or the first twenty five kilobytes, whichever runs out first the topic files underneath are read on demand, which means only if something in that one line makes it go and open them i checked mine: thirty nine index lines loading every session, thirty nine topic files loading none of the time the detail you were careful about is not context it is a footnote your agent has to choose to open and the sentence you dashed off in three seconds is the whole memory

Lagosaur

28,369 просмотров • 2 месяцев назад

I just built my second brain with Hermes Agent. It remembers people, companies, projects, and decisions. It pulls from meeting notes, docs, anything I feed it, and cites the source every time. It's this repo called Gbrain, open-sourced by Y Combinator president Garry Tan. install it by pasting this into Hermes: "Install Gbrain following the instructions at Set up the embedding provider, then run gbrain init and gbrain doctor to verify." Add the MCP server so Hermes can call it directly: "Add the Gbrain MCP server and reload." The graph builds itself. Pattern matching on the people and companies already in your notes. 31 points more precise than plain vector search, by their own benchmark. The data from the video is made up so I could show you how it works. Hermes' memory remembers our conversations. Gbrain remembers my world. Open source. Runs local, no server, no Docker.

I just built my second brain with Hermes Agent. It remembers people, companies, projects, and decisions. It pulls from meeting notes, docs, anything I feed it, and cites the source every time. It's this repo called Gbrain, open-sourced by Y Combinator president Garry Tan. install it by pasting this into Hermes: "Install Gbrain following the instructions at Set up the embedding provider, then run gbrain init and gbrain doctor to verify." Add the MCP server so Hermes can call it directly: "Add the Gbrain MCP server and reload." The graph builds itself. Pattern matching on the people and companies already in your notes. 31 points more precise than plain vector search, by their own benchmark. The data from the video is made up so I could show you how it works. Hermes' memory remembers our conversations. Gbrain remembers my world. Open source. Runs local, no server, no Docker.

Alvaro Cintas

16,975 просмотров • 3 месяцев назад

I built Caduceus for Hermes Desktop Nous Research Two snakes, one staff Caduceus runs parallel AI agents in perfect sync, on any model you already have It sizes up every task and picks the path: • Easy → answered instantly • Hard → a live to-do plan + agents fanned out in parallel, verified before it calls it done Caduceus has a UI right inside Hermes so you watch every agent build your project in real time Full demo 👇

I built Caduceus for Hermes Desktop Nous Research Two snakes, one staff Caduceus runs parallel AI agents in perfect sync, on any model you already have It sizes up every task and picks the path: • Easy → answered instantly • Hard → a live to-do plan + agents fanned out in parallel, verified before it calls it done Caduceus has a UI right inside Hermes so you watch every agent build your project in real time Full demo 👇

Terp

17,006 просмотров • 5 месяцев назад

Hermes is an AI Harness from Nous Research that is truly yours: you can shape its persona, it remembers you across sessions, and it gets better the longer you use it. We ran it fully local on QVAC. The persona, the memory, the skills it learns, all of it stays on your machine.

Hermes is an AI Harness from Nous Research that is truly yours: you can shape its persona, it remembers you across sessions, and it gets better the longer you use it. We ran it fully local on QVAC. The persona, the memory, the skills it learns, all of it stays on your machine.

QVAC

65,873 просмотров • 3 месяцев назад

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

ANTHROPIC KILLED THE VECTOR DATABASE AND REPLACED IT WITH A FOLDER no embeddings. no black-box retrieval. no special memory tools. the agent just reads and writes a folder the insight: your agent already knows how to use a filesystem. so Anthropic made long-term memory a directory of small markdown files, mounted straight into the agent's sandbox at /mnt/memory the agent manages it with the tools it already has, bash, read, write, edit, grep, and Anthropic auto-injects a note into the system prompt so the agent always knows the memory is there and where to look the result: > every change is an immutable version: full audit trail, point-in-time recovery, redaction > read-only or read-write per store, so many agents can safely share one memory > workspace-scoped and persistent, memory that survives every session end most teams bolt on a vector DB and pray retrieval works. Anthropic went the opposite way: compiled knowledge as plain, human-readable files you can open, diff, and edit yourself and there's a second layer. Anthropic's "Dreaming" runs offline, reviews past sessions and the store, and rewrites it into a cleaner, higher-signal version. the agent literally consolidates memory in its sleep. bookmark this. the most production-grade agent memory right now is just a well-organized folder, and Anthropic proved it.

NO1ennn

13,359 просмотров • 3 месяцев назад

how to use Bot Screen in Hermes Agent: your bot gets its own desktop, and you step in when it hits a login. on the Linux machine that runs your Hermes gateway: 1. install the screen: `hermes computer-use screen install` 2. make the bot's browser show on it: `hermes config set browser.headed true` 3. in Hermes Desktop: Bots, right-click your bot, Open Screen, then Start 4. ask it to open a login page in its browser and stop at the sign-in 5. when it stops, click Take over and sign in 6. click Hand back and tell it to continue the bot keeps that login for later sessions, until the site expires it.

how to use Bot Screen in Hermes Agent: your bot gets its own desktop, and you step in when it hits a login. on the Linux machine that runs your Hermes gateway: 1. install the screen: `hermes computer-use screen install` 2. make the bot's browser show on it: `hermes config set browser.headed true` 3. in Hermes Desktop: Bots, right-click your bot, Open Screen, then Start 4. ask it to open a login page in its browser and stop at the sign-in 5. when it stops, click Take over and sign in 6. click Hand back and tell it to continue the bot keeps that login for later sessions, until the site expires it.

witcheer

56,107 просмотров • 1 месяц назад

Hermes Agent on Omarchy, in one minute. set Hermes as your default agent from the Omarchy menu, and every way you open an agent on the machine is the same Hermes: the terminal, the desktop app, the menu entry. ask it something about Omarchy itself and it knows where the files live, because Omarchy ships its own skill into Hermes. then switch your theme. the terminal, the app, everything follows.

Hermes Agent on Omarchy, in one minute. set Hermes as your default agent from the Omarchy menu, and every way you open an agent on the machine is the same Hermes: the terminal, the desktop app, the menu entry. ask it something about Omarchy itself and it knows where the files live, because Omarchy ships its own skill into Hermes. then switch your theme. the terminal, the app, everything follows.

witcheer

39,331 просмотров • 1 месяц назад

Hermes agent just left the terminal. 𝗛𝗲𝗿𝗺𝗲𝘀 𝗗𝗲𝘀𝗸𝘁𝗼𝗽 dropped yesterday. native app for macOS, Windows, and Linux. for months Hermes was the agent that learned your projects, wrote its own skills, and built a model of who you are. all of it buried in terminal logs. now it has a window. the important part is that it's not a wrapper. it runs the same agent core, the same sessions, memory, and skills as the CLI. you can start a task in the terminal and finish it in the app without anything resetting. the state is shared across every interface, not copied between them. what the GUI actually adds: → streaming chat that shows live tool calls and inline reasoning instead of a spinner → a preview rail that renders pages, code, and images right beside the conversation → an artifacts panel that collects every file the agent has ever produced → remote gateway mode, so you can point the app at a VPS and run the heavy work elsewhere → skills, cron, profiles, and gateways managed point-and-click instead of through YAML → voice mode, drag-drop files, and inline image generation remote gateway mode is the one worth slowing down on. the agent runs 24/7 on a $5 server while you control it from your laptop like a local app. other agent UIs are chatboxes with a logo. this one shows the autonomy instead of hiding it, so you watch the skills load, the tools fire, and the artifacts pile up as it works. it was teased in Jensen's GTC keynote. MIT licensed, local-first, no telemetry. if you already run Hermes, download it and everything is already there. your chats, memory, and skills carry straight over. i wrote a full masterclass on Hermes Agent that walks through the SOUL. md identity layer, the three-tier memory system, the self-evolving skills loop, and how to run three specialized agents 24/7. desktop is the interface that finally does all of it justice. the article is quoted below.

Akshay 🚀

51,540 просмотров • 5 месяцев назад