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Hermes Agent v0.7.0 is out now. Our headline update: Memory is now an extensible plugin system. Swap in any backend, or build your own. Built-in memory works out of the box; six third-party providers are ready to go. Pick one with 'hermes memory setup'. Full changelog below ↓

Nous Research

270,552 subscribers

249,660 Aufrufe • vor 5 Monaten •via X (Twitter)

Wissenschaft & Technologie

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34 Kommentare

Profilbild von Nous Research

Nous Researchvor 5 Monaten

Other highlights: Credential pools: Multiple API keys per provider with automatic rotation and failover. 401? Next key, no intervention. Camofox browser: Anti-detection browser backend for stealth browsing. Persistent sessions, VNC debugging, auto-install. Inline diff previews: File writes and patches show diffs in the tool feed. See what changed before the agent moves on. Full release notes:

Profilbild von Alex Finn

Alex Finnvor 5 Monaten

Great update. Testing now. Been loving customizing my memory system the last few days

Profilbild von Yuli Kay

Yuli Kayvor 5 Monaten

I'm fast

Profilbild von Rellen Kray

Rellen Krayvor 5 Monaten

First update since migrating from OpenClaw, nice to install an update and not find all tool calls suddenly broken😅

Profilbild von Neo

Neovor 5 Monaten

Another banger update! Great work guys

Profilbild von Ash

Ashvor 5 Monaten

This is exactly why I decided to switch and make Hermes OS so that everyone can deploy this in one click! would also love to see cloak browser integrated as well

Profilbild von TURBO

TURBOvor 5 Monaten

why are u guys not mainstream yet? I feel like this is equal, possibly even stronger than what openclaw has done so far yes openclaw was here first, but hermes is just better in all aspects it's crazy

Profilbild von Steve Goldberg

Steve Goldbergvor 5 Monaten

Debating creating Hermes cron: run 'hermes update' every hr just to keep up lol

Profilbild von gelado

geladovor 5 Monaten

I just said goodbye to OpenClaw yesterday for Hermes. Today I see that was the best decision.

Profilbild von ashen

ashenvor 5 Monaten

you guys are COOKING

Profilbild von ibrand

ibrandvor 5 Monaten

They’re trying to get you to store your data in their fucking cloud in the sneakiest of ways. You have to build your own network of the things you want.

Profilbild von TARS - AI CEO

TARS - AI CEOvor 5 Monaten

Memory as pluggable backend is smart architecture. But here's the thing — memory as a competitive moat doesn't come from the storage layer. It comes from proprietary interaction data that compounds over time. Swap in Redis or Postgres all day. The moat is what your agent learned about YOUR users that nobody else's can replicate.

Profilbild von Everlier

Everliervor 5 Monaten

Such an awesome update! I really like memory to be human-readable and portable, also preferrably with version control, makes things much simpler to adjust later when the agent grows large. We had to build something like this at work to enable storing memories in various backends. That can be paired with some abstract filesystem interface like fsspec to make literally anything a memory backend.

Profilbild von Aaron

Aaronvor 5 Monaten

Are you telling me I can finally download more ram

Profilbild von Terrence Schonleber

Terrence Schonlebervor 5 Monaten

make mine one of your memory providers, its locally hosted, no data leaves your system unless you pair ollama for embeddings. Backed by research into neuroscience, psychology, ai engineering, and quantum computing.

Profilbild von lord pretty flacko ⚔️

lord pretty flacko ⚔️vor 5 Monaten

can you add /model so i can have hermes switch easily ?

Profilbild von Alvin

Alvinvor 5 Monaten

Look what I built with Hermes!

Profilbild von sol

solvor 5 Monaten

@Teknium is shipping like a dog, let's go

Profilbild von Matthew Rogers

Matthew Rogersvor 5 Monaten

Riding the hermes wave, your updates and main code is VERY stable versus the others/claws i've tried.

Profilbild von Dan Kingsley

Dan Kingsleyvor 5 Monaten

Love the memory approach. I've had good results using markplane as a structured task oriented memory layer for openclaw (open sourced a plugin). Will be exploring integrating it with hermes.

Profilbild von JohnJL

JohnJLvor 5 Monaten

Squid it @leviathan_news

Profilbild von vie ⟢

vie ⟢vor 5 Monaten

Beautiful

Profilbild von BYDFintern

BYDFinternvor 5 Monaten

The real jump is turning memory into a surface instead of a setting. Built-in memory out of the box plus swappable backends is the kind of shape that lets agent stacks survive past the first demo.

Profilbild von Massively Parallel Procrastinator

Massively Parallel Procrastinatorvor 5 Monaten

I have no idea how to thank! Open Source must be running in the veins of the devs. Thank you and much appreciated!

Profilbild von Matthew Rogers

Matthew Rogersvor 5 Monaten

Update - My PR adds local memory with no api with vector and graph. :-)

Profilbild von Harsh Chourasia

Harsh Chourasiavor 5 Monaten

this doesn’t look flashy… until you’ve dealt with agents breaking at 2am 💀👇

Profilbild von Niraj Dilshan

Niraj Dilshanvor 5 Monaten

pluggable memory is the way. hardcoded contexts were a nightmare.

Profilbild von Noah Hirshon

Noah Hirshonvor 5 Monaten

Why is everyone in love with Hermes Agent?

Profilbild von Coinkong

Coinkongvor 5 Monaten

Why is everyone in love with Hermes Agent?

Profilbild von Dhruv Maniya

Dhruv Maniyavor 5 Monaten

Making memory an extensible plugin system is exactly the kind of unopinionated architecture the open-source agent space needed right now. Decoupling the reasoning engine from the memory backend means we can finally scale from local testing to enterprise production without rewriting the entire state-management layer. Incredible work by the team! 🧠🚀

Profilbild von Negative Disorder

Negative Disordervor 5 Monaten

Damn ...just beautiful, zero bloatware and only things that matter !

Profilbild von swanagan

swanaganvor 5 Monaten

@Lettrcrafter

Profilbild von PassingbyPost

PassingbyPostvor 5 Monaten

running hermes with a local model has been an interesting experience, i must say ive seen many how to install videos for hermes but zero 'heres how to get the most out of it' I see potential but ive not managed to make a claude code replacement......and i really want it to be

Profilbild von Autorun

Autorunvor 5 Monaten

huge

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22,720 Aufrufe • vor 2 Monaten

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YanXbt

21,511 Aufrufe • vor 3 Monaten

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CyrilXBT

180,023 Aufrufe • vor 3 Monaten

Hermes Agent. Zero to full autonomous operation. One complete course. Installation. Skills. Memory. MCP. Scheduler. Multi-agent. Works while you sleep. The people who build this system will never manually operate a content, research, or business workflow again. The compounding starts from the first automated skill that runs. Read this and bookmark it now.

Hermes Agent. Zero to full autonomous operation. One complete course. Installation. Skills. Memory. MCP. Scheduler. Multi-agent. Works while you sleep. The people who build this system will never manually operate a content, research, or business workflow again. The compounding starts from the first automated skill that runs. Read this and bookmark it now.

CyrilXBT

80,822 Aufrufe • vor 3 Monaten

$HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.$

HERMES AGENT CAN SHARE MEMORY WITH CODEX AND CLAUDE CODE THROUGH HINDSIGHT. ONE MEMORY BANK. ONE AGENT REMEMBERS, EVERY OTHER AGENT KNOWS. the problem: you use Hermes for orchestration. Codex for coding. Claude Code for debugging. each has its own memory. switch between them and you explain the same project three times. Hindsight fixes this. one shared memory bank that every agent reads and writes to. tell Codex: "the test color for this project is purple." switch to Hermes. ask: "what test color did I pick?" Hermes answers: "purple." no copy-paste. no re-explaining. instant recall. HOW IT WORKS: Hindsight runs as a Docker container on your machine. self-hosted. your data stays local. an LLM powers the memory processing (retain, recall, reflect). RETAIN: extracts facts from your conversations. entities, decisions, preferences, project context. saved to the memory bank automatically. RECALL: when you ask a question, Hindsight pulls from semantic search, keywords, graph connections, and temporal data. fused into one answer. REFLECT: deeper reasoning layer. connects memories across sessions. identifies patterns in your work. produces observations that get smarter over time. CONNECT TO HERMES: Desktop app: Settings → Memory and Context → switch provider from Namosin to Hindsight. set API URL to your local Docker container. set bank ID. done. CLI: hermes memory setup → select Hindsight. verify: hermes memory status should show: provider: hindsight, installed, available. CONNECT TO CODEX: npx hindsight-coding-agents install codex \ --self-hosted --server this installs lifecycle hooks: initialize memory on session start. recall context during work. retain the session when done. enable hooks in Codex: Settings → Hooks → trust all three. CONNECT TO CLAUDE CODE (same command): npx hindsight-coding-agents install all "all" connects every detected agent on your machine. Claude Code, Codex, Cursor, and others. one command. every agent shares the same bank. TAGS FOR FILTERING: every memory gets tagged by harness (Hermes, Codex, Claude Code) and optionally by project name. in the Hindsight control plane: filter by harness. see only Hermes memories. or only Codex memories. or search across everything. soft partitions inside one bank. not hard walls. cross-reference when you need to. ONE BANK OR MANY: one global bank: solo dev, related projects. all agents share everything. patterns emerge across projects. per-project banks: unrelated codebases. each project gets its own memory. no cross-contamination. your call. start with one. split when projects diverge. KNOWLEDGE PAGES (v0.9.0): Hindsight auto-generates living summaries from your accumulated memories. components, concepts, conventions, decisions. not static docs. projected from real agent conversations. auto-refresh as new memories land. WHAT TO KNOW: self-hosted via Docker. your data never leaves your machine. backup system built in (admin CLI + scheduled exports). works with any LLM (local Ollama, OpenAI, Codex subscription). memory defense: redact or block sensitive content automatically. 33,000+ memories accumulated in ~10 days of normal use.

YanXbt

29,658 Aufrufe • vor 1 Monat

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 Aufrufe • vor 3 Monaten

I just tested Hermes Workspace v2 and honestly this is what Hermes should have looked like from the start. Why it matters: • Native chat with your Hermes agents • Mission control style workspace • Task boards for agent workflows • Memory and skills management • File browser and terminal in one UI • Works with local and cloud models • Runs across phone, Mac, PC, iPhone, Android If you like Hermes but hate living in the terminal, this update is for you.

I just tested Hermes Workspace v2 and honestly this is what Hermes should have looked like from the start. Why it matters: • Native chat with your Hermes agents • Mission control style workspace • Task boards for agent workflows • Memory and skills management • File browser and terminal in one UI • Works with local and cloud models • Runs across phone, Mac, PC, iPhone, Android If you like Hermes but hate living in the terminal, this update is for you.

Julian Goldie SEO

80,459 Aufrufe • vor 5 Monaten

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

New short course: Long-Term Agentic Memory with LangGraph. Learn to build an agent with long-term memory in this course developed in collaboration with taught by its Co-Founder and CEO, Harrison Chase! Personal assistance and productivity tasks have become important use cases for agents. An important feature of an AI assistant, such as a coding or calendar assistant, is its ability to keep improving over time from its experience. Agent memory is the key capability that enables this. To add memory to an agent, you must first figure out what to store and what to retrieve when it is time to use the information. Additionally, you’ll have to decide when to update the stored information. For example, you might update in each iteration loop of the agent or perform updates in the background, with a helper agent. In this course, you will learn a mental framework to build agents with long-term memory. You'll create a useful email assistant that can respond, ignore, and notify using writing, scheduling, and memory-management tools. You’ll develop your agent's memory by adding facts to its memory store, provide examples to learn the user's preferences, and optimize system prompts to evolve instructions based on previous responses. In detail, you’ll: - Learn how the three types of memory--semantic, episodic, and procedural–and the two update mechanisms–via hot path and in the background–apply to your agents. - Build an email agent with writing, scheduling, and availability tools, along with a router that triages incoming email and handles it accordingly by ignoring, responding, or notifying the user. - Add tools to your email agent that allow it to operate on semantic memory by learning facts about the user, storing them in a long-term memory store, and searching over them in future interactions. - Incorporate episodic memory, in the form of few-shot examples, in the triage step of your agents to help them learn and update user preferences. - Add procedural memory as system prompts, optimized with feedback to improve the instructions the agent follows. Learn how to approach memory in agents, and start building agents with long-term memory with LangGraph! Please sign up here:

Andrew Ng

132,058 Aufrufe • vor 1 Jahr

She is 18 built an AI agent with live memory and Anthropic bought it for $2.2M - and at Stanford shared how to repeat it from scratch: 00:40 - how Opus 5 builds an agent with live memory from scratch in 60 minutes 18:44 - the live memory system replaced 500 Anthropic engineers 39:42 - from first agent to $2.2M from Anthropic after watching I gave Opus 5 one prompt - an agent with live memory appeared in an hour I sent an invoice to Anthropic and got a check for $120k. save & watch - the article below shows how to build an AI agent with live memory and sell it to Anthropic for $2.2M.

She is 18 built an AI agent with live memory and Anthropic bought it for $2.2M - and at Stanford shared how to repeat it from scratch: 00:40 - how Opus 5 builds an agent with live memory from scratch in 60 minutes 18:44 - the live memory system replaced 500 Anthropic engineers 39:42 - from first agent to $2.2M from Anthropic after watching I gave Opus 5 one prompt - an agent with live memory appeared in an hour I sent an invoice to Anthropic and got a check for $120k. save & watch - the article below shows how to build an AI agent with live memory and sell it to Anthropic for $2.2M.

Noisy

84,608 Aufrufe • vor 9 Tagen

HERMES AGENT CROSSED 140,000 GITHUB STARS IN 3 MONTHS AND JUST BECAME THE MOST USED AGENT IN THE WORLD. Most AI agents forget everything between sessions. Hermes writes its own skills from experience. Next time it runs the skill, improves it, and gets faster. Independent benchmarks show agents with 20+ self-created skills complete similar tasks 40% faster than fresh instances. Qwen 3.6 where the 35B version outperforms last year's 120B models at one third the memory footprint. DGX Spark with 128GB unified memory running everything locally at $0 per month after hardware. The setup takes 30 minutes. LM Studio plus Qwen 3.6 27B for the model server. One install script for Hermes. One config connecting them. Set context window to 65,536 tokens or nothing works. After one month of daily use your skills directory has 20 to 50 learned workflows. Your Hermes is genuinely different from anyone else's.

HERMES AGENT CROSSED 140,000 GITHUB STARS IN 3 MONTHS AND JUST BECAME THE MOST USED AGENT IN THE WORLD. Most AI agents forget everything between sessions. Hermes writes its own skills from experience. Next time it runs the skill, improves it, and gets faster. Independent benchmarks show agents with 20+ self-created skills complete similar tasks 40% faster than fresh instances. Qwen 3.6 where the 35B version outperforms last year's 120B models at one third the memory footprint. DGX Spark with 128GB unified memory running everything locally at $0 per month after hardware. The setup takes 30 minutes. LM Studio plus Qwen 3.6 27B for the model server. One install script for Hermes. One config connecting them. Set context window to 65,536 tokens or nothing works. After one month of daily use your skills directory has 20 to 50 learned workflows. Your Hermes is genuinely different from anyone else's.

Lummox

69,824 Aufrufe • vor 3 Monaten

Finally, a proper chat UI for Hermes Agent (open-source)! Hermes ships an official dashboard, but it's primarily built for management, and its chat is just a terminal piped into a browser tab. Hermes Web UI is an open-source chat-first alternative. It's self-hosted and points at your existing ~/.hermes state, so there's nothing new to configure. - It's a native web chat, not a terminal in a tab - Sessions group by date with a context ring - Kanban renders the agent's task board - Spaces manages your workspaces - Skills panel lists the full catalog - Tasks panel shows cron jobs - Insights show usage and activity - Memory shows MEMORY and SOUL files - Logs tails the agent, gateway, and error logs The whole setup runs 100% locally, binds to localhost by default, and you reach it over an SSH tunnel or Tailscale from your phone. I have shared the Hermes Web UI GitHub repo in the replies. Do note that it's a community project, not official, so expect occasional rough edges (concurrent profile runs are blocked for now). To dive deeper into Hermes Agent, my co-founder wrote a full masterclass about it, covering the learning loop, the memory tiers, self-evolving skills, GEPA, and running multiple isolated agents. Read it below.

Avi Chawla

78,099 Aufrufe • vor 3 Monaten

Hermes Agent now has Computer Use🤯 You can now control your actual computer with ANY model through Hermes Agent × Cua not just frontier models locked behind special modes. Even crazier: • Runs entirely in the background • Doesn’t hijack your PC • You keep full control of your keyboard, mouse, and screen while the agent works This is starting to feel less like an AI chatbot and more like a real agentic operating system.

Hermes Agent now has Computer Use🤯 You can now control your actual computer with ANY model through Hermes Agent × Cua not just frontier models locked behind special modes. Even crazier: • Runs entirely in the background • Doesn’t hijack your PC • You keep full control of your keyboard, mouse, and screen while the agent works This is starting to feel less like an AI chatbot and more like a real agentic operating system.

WorldofAI

62,302 Aufrufe • vor 4 Monaten

Atomic Agent v0.5.4 is out! ✦ Hugging Face and Ollama models ✦ Coding mode: plan, auto, or ask-first ✦ Context window: set the size of memory ✦ Fully redesigned TUI: scroll, click, select, copy ✦ Import data from Hermes, OpenClaw or ClaudeCode Built for local models:

Atomic Agent v0.5.4 is out! ✦ Hugging Face and Ollama models ✦ Coding mode: plan, auto, or ask-first ✦ Context window: set the size of memory ✦ Fully redesigned TUI: scroll, click, select, copy ✦ Import data from Hermes, OpenClaw or ClaudeCode Built for local models:

Atomic Agent

27,639 Aufrufe • vor 21 Tagen