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Introducing V2 🤯 Whats New 👇🏻 🤯No fork required ⭐️New Hermes dark + light themes 🤖Agent View Office 🚀Conductor for agent missions 👥Operations for sub agent orchestration One liner: curl -fsSL | bash

Eric ⚡️ Building...

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185,338 Aufrufe • vor 6 Monaten

🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace

🚀 Introducing AgentCPM-Explore: The First Open-Source 4B-Agent Model to Conquer GAIA & Complex Real-World Tasks! 🤗 Hugging Face: 🔗 GitHub: ✨ Key Highlights: ✅ SOTA Agentic Performance: Sets a new benchmark for 4B-scale agent models—outperforming all peers, surpassing 8B models, and rivaling select 30B+ and closed-source LLMs. 🧠 Deep Research Capability: Excels at long-horizon reasoning, supports 100+ turns of autonomous interaction with multi-source cross-validation, human-like self-correction, and dynamic tool use + strategy adaptation—just like a real researcher! 🔓 Full-Stack Open Source: We’re open-sourcing the entire end-to-end agent stack—not just the model! Empower your own innovations with - AgentRL: Asynchronous reinforcement learning framework - AgentDock: Secure, extensible tool sandbox - AgentToLeaP: An one-click evaluation platform for agent tool-learning capabilitie - Full training data pipeline & reproducible workflows #AgentCPM #OpenSourceAI #AgenticAI #AI #GAIA #LLM #OpenBMB #AIAgents #HuggingFace

OpenBMB

13,996 Aufrufe • vor 7 Monaten

🤯 YOUR AGENT NOW GETS ITS OWN COMPUTER - WITHOUT PAYING FOR GROK someone open-sourced the entire Grok Bot stack. Rakazo. 531 stars in 3 days on GitHub. -> install it - and your agent gets: - any LLM you pick (OpenRouter, ChatGPT, Copilot, or sign in with SuperGrok) - a live Linux desktop it can see, click, and type on - Docker, E2B, or your own Mac as the sandbox - web, desktop, and mobile - one product, your hardware - memory, routines, and history per bot - bots that spawn other bots, or subagents inside one turn - self-hosted. no Rakazo control plane required no vendor lock-in. no closed bot computer. important: still beta - you need Docker, Postgres, and your own model key. your agent has been renting someone else's sandbox this whole time. save this before the Grok team sees it ↓

🤯 YOUR AGENT NOW GETS ITS OWN COMPUTER - WITHOUT PAYING FOR GROK someone open-sourced the entire Grok Bot stack. Rakazo. 531 stars in 3 days on GitHub. -> install it - and your agent gets: - any LLM you pick (OpenRouter, ChatGPT, Copilot, or sign in with SuperGrok) - a live Linux desktop it can see, click, and type on - Docker, E2B, or your own Mac as the sandbox - web, desktop, and mobile - one product, your hardware - memory, routines, and history per bot - bots that spawn other bots, or subagents inside one turn - self-hosted. no Rakazo control plane required no vendor lock-in. no closed bot computer. important: still beta - you need Docker, Postgres, and your own model key. your agent has been renting someone else's sandbox this whole time. save this before the Grok team sees it ↓

Granite

38,801 Aufrufe • vor 7 Tagen

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.

new chapter begins: a terminal for the agentic future, built on blockchain, powered by AI. This is our marketplace—a glimpse of what AGI will mean for crypto. Today, we launch 3 agents—Image Generation, Token Swap Agent, & Blockchain Tax Estimate Agent—out of hundreds to come. We see an agentic future where AI guides every step: buying online, managing finances, transacting globally. FOMO’s here to make that real, with experts at your side. Our Model Context Protocol (MCP) ties it together—agents talking, reasoning, scaling across crypto and DeFi. It’s orchestration with a brain, evolving daily. FOMO’s not just building tools; we’re pushing intelligent automation into blockchain’s core. Our Model Context Protocol (MCP) is the backbone. Think of it as a conductor for AI agents—each runs its own logic (workflows, API calls, LLMs), but MCP syncs them on-chain. Agents share context via a lightweight event bus, logged to a blockchain ledger. Agent A (say, Market Analysis) pulls stock data, flags trends. Agent B (Email Sales) reads that, drafts outreach—both talk through MCP’s orchestration layer. We use Web3 hooks to settle fees or split revenue, all transparent. It’s messy, but it scales. Under the hood: MCP leans on a pub-sub model—agents publish tasks, others subscribe. We’re training them with RL loops to optimize gas costs and response times. Goal? A self-tuning swarm of agents reasoning over DeFi, NFTs, whatever’s next. This is FOMO’s bet on AGI. Welcome to the new FOMO. We’re not just building tools—we’re wiring AI into crypto’s future, agent by agent. A leader in blockchain intelligence, starting here. Join us as we push the boundaries.

FOMO

26,338 Aufrufe • vor 1 Jahr

HERMES AGENT HAS 5 SYSTEMS RUNNING UNDER THE HOOD. UNDERSTAND THEM AND YOU USE THE AGENT 10X BETTER. In this video Alejandro AO 🤗 explained: 1. THE AGENT LOOP every message triggers the same cycle: → you send a message → Hermes builds context (SOUL.md + memory.md + user.md + skills + tools + message history) → sends everything to the LLM → LLM decides: call a tool or respond → if tool call: execute, return result, loop back → if response: deliver to you → after response: memory update (agent checks if anything is worth remembering, writes to memory.md or user.md) this loop is why Hermes gets better over time. the memory update after every response means the agent learns from every conversation. 2. CONTEXT ASSEMBLY what the LLM sees on every turn: → SOUL.md (your agent's personality and rules) → memory.md (facts the agent learned over time) → user.md (facts about you, auto-updated) → AGENTS.md and .hermes.md (project context files) → skill descriptions (loaded on demand) → tool schemas (available actions) → message history (current conversation) if SOUL.md is empty, Hermes falls back to a default system prompt. write your own SOUL.md and the agent becomes yours, not generic. CONTEXT COMPRESSION: conversations hit context limits. Hermes handles this at two checkpoints: preflight: before each turn. if conversation exceeds 50% of context window, compression fires. older messages get summarized. last 20 messages stay intact (protect_last_n). gateway auto-compression: between turns. fires at 85%. more aggressive. prevents API errors before the agent even starts processing your message. after compression, a new session lineage ID is generated. the agent can trace back to the original conversation through SQLite. three things break prompt cache: switching models mid-session, changing memory files, or changing context files. 3. THE GATEWAY the system that keeps Hermes reachable on 27+ messaging platforms. an async loop runs continuously. listens for incoming messages from Telegram, Discord, Slack, WhatsApp, email, SMS, and every other adapter. when a message arrives: → gateway identifies which session it belongs to → queries SQLite for the full message history (session ID = platform prefix + chat ID) → builds the context from scratch → sends everything into the agent loop → delivers the response back to the platform the gateway also runs the session manager. when you send a message while the agent is busy: → default: queued for next turn → /steer: injected without interrupting → /interrupt: stops current work without the gateway, Hermes is a CLI tool. with the gateway, Hermes is an always-on agent you reach from your phone. 4. MEMORY (THREE LAYERS) LAYER 1 — MARKDOWN FILES SOUL.md (identity), memory.md (learned facts), user.md (facts about you). injected into context after the system prompt. updated by the agent after every response. LAYER 2 — SQLITE full transcripts of every session stored locally. FTS5 full-text search across all past conversations. session lineage tracking across compressions. the agent can recall what you discussed weeks ago using /recall or session search. LAYER 3 — EXTERNAL PROVIDERS (optional) 8 supported providers: Mem0, SuperMemory, Honcho, Zep, and more. each works differently (semantic search, LLM extraction, similarity matching). queried after the first message in each session. the agent processes your topic first, then checks external memory for related context from past conversations. not enabled by default. enable for significantly better long-term recall. 5. CRON ENGINE a loop inside the gateway ticks every 60 seconds. each tick checks ~/.hermes/cron/jobs.json for scheduled tasks. if a job is due: → fresh session (no chat history, no memory pollution) → execute the prompt with assigned tools → store the run output as markdown in ~/.hermes/cron/output/[job-id]/ → deliver result to your home messaging platform cron does NOT use the send_message tool. delivery happens at the system level, not the agent level. a cron session cannot create more cron jobs. prevents runaway loops. WHY THIS MATTERS: the agent loop teaches it. the context assembly focuses it. the gateway reaches it. the memory remembers it. the cron engine automates it. five systems. one agent. understanding how they connect changes how you configure every level. full 15 levels breakdown in the article 👇

HERMES AGENT HAS 5 SYSTEMS RUNNING UNDER THE HOOD. UNDERSTAND THEM AND YOU USE THE AGENT 10X BETTER. In this video Alejandro AO 🤗 explained: 1. THE AGENT LOOP every message triggers the same cycle: → you send a message → Hermes builds context (SOUL.md + memory.md + user.md + skills + tools + message history) → sends everything to the LLM → LLM decides: call a tool or respond → if tool call: execute, return result, loop back → if response: deliver to you → after response: memory update (agent checks if anything is worth remembering, writes to memory.md or user.md) this loop is why Hermes gets better over time. the memory update after every response means the agent learns from every conversation. 2. CONTEXT ASSEMBLY what the LLM sees on every turn: → SOUL.md (your agent's personality and rules) → memory.md (facts the agent learned over time) → user.md (facts about you, auto-updated) → AGENTS.md and .hermes.md (project context files) → skill descriptions (loaded on demand) → tool schemas (available actions) → message history (current conversation) if SOUL.md is empty, Hermes falls back to a default system prompt. write your own SOUL.md and the agent becomes yours, not generic. CONTEXT COMPRESSION: conversations hit context limits. Hermes handles this at two checkpoints: preflight: before each turn. if conversation exceeds 50% of context window, compression fires. older messages get summarized. last 20 messages stay intact (protect_last_n). gateway auto-compression: between turns. fires at 85%. more aggressive. prevents API errors before the agent even starts processing your message. after compression, a new session lineage ID is generated. the agent can trace back to the original conversation through SQLite. three things break prompt cache: switching models mid-session, changing memory files, or changing context files. 3. THE GATEWAY the system that keeps Hermes reachable on 27+ messaging platforms. an async loop runs continuously. listens for incoming messages from Telegram, Discord, Slack, WhatsApp, email, SMS, and every other adapter. when a message arrives: → gateway identifies which session it belongs to → queries SQLite for the full message history (session ID = platform prefix + chat ID) → builds the context from scratch → sends everything into the agent loop → delivers the response back to the platform the gateway also runs the session manager. when you send a message while the agent is busy: → default: queued for next turn → /steer: injected without interrupting → /interrupt: stops current work without the gateway, Hermes is a CLI tool. with the gateway, Hermes is an always-on agent you reach from your phone. 4. MEMORY (THREE LAYERS) LAYER 1 — MARKDOWN FILES SOUL.md (identity), memory.md (learned facts), user.md (facts about you). injected into context after the system prompt. updated by the agent after every response. LAYER 2 — SQLITE full transcripts of every session stored locally. FTS5 full-text search across all past conversations. session lineage tracking across compressions. the agent can recall what you discussed weeks ago using /recall or session search. LAYER 3 — EXTERNAL PROVIDERS (optional) 8 supported providers: Mem0, SuperMemory, Honcho, Zep, and more. each works differently (semantic search, LLM extraction, similarity matching). queried after the first message in each session. the agent processes your topic first, then checks external memory for related context from past conversations. not enabled by default. enable for significantly better long-term recall. 5. CRON ENGINE a loop inside the gateway ticks every 60 seconds. each tick checks ~/.hermes/cron/jobs.json for scheduled tasks. if a job is due: → fresh session (no chat history, no memory pollution) → execute the prompt with assigned tools → store the run output as markdown in ~/.hermes/cron/output/[job-id]/ → deliver result to your home messaging platform cron does NOT use the send_message tool. delivery happens at the system level, not the agent level. a cron session cannot create more cron jobs. prevents runaway loops. WHY THIS MATTERS: the agent loop teaches it. the context assembly focuses it. the gateway reaches it. the memory remembers it. the cron engine automates it. five systems. one agent. understanding how they connect changes how you configure every level. full 15 levels breakdown in the article 👇

YanXbt

51,670 Aufrufe • vor 2 Monaten

3 weeks of building OpenClaw in the dark was enough. I needed to see my squad, so I built them a home. 🕹️ Mission Control is live: • Pixel Office: Watch your 12-agent squad actually work (and hit the breakroom). • Kanban Sync: They don’t just “run”—they update the boards too. • Zero Guesswork: Real-time logs and cron jobs in one clean view. If you’re tired of managing an autonomous team through a messy terminal, this is for you. Comment "OpenClaw" below for beta access! 🚀

3 weeks of building OpenClaw in the dark was enough. I needed to see my squad, so I built them a home. 🕹️ Mission Control is live: • Pixel Office: Watch your 12-agent squad actually work (and hit the breakroom). • Kanban Sync: They don’t just “run”—they update the boards too. • Zero Guesswork: Real-time logs and cron jobs in one clean view. If you’re tired of managing an autonomous team through a messy terminal, this is for you. Comment "OpenClaw" below for beta access! 🚀

Ziwen

18,214 Aufrufe • vor 5 Monaten

HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇

HERMES AGENT SHIPS WITH A BUNDLED SKILL FOR ANDREJ KARPATHY'S LLM WIKI PATTERN. A SELF-IMPROVING KNOWLEDGE BASE THAT GROWS EVERY TIME YOU FEED IT. mentioned this briefly in the overnight workflow article. here is the full breakdown. what it is: a self-improving knowledge base built as interlinked markdown files. unlike RAG (which rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. cross-references stay linked. contradictions get flagged automatically. synthesis reflects everything ingested so far. why this matters for Hermes memory: Hermes built-in memory knows YOU. it remembers your conversations, your preferences, your business context across sessions. but it doesn't know your inbox. or your meeting transcripts. or that article you saved last week. or the expert framework you want it to learn. the LLM Wiki solves that. THE DIVISION OF LABOR human curates sources and directs analysis. agent summarizes, cross-references, files, and maintains consistency. you drop in articles, transcripts, notes. Hermes indexes them, links related concepts, flags contradictions, updates affected pages. your knowledge base grows itself. SETUP IS ONE COMMAND the skill ships with Hermes. enable it. set WIKI_PATH in ~/.hermes/.env: WIKI_PATH=/Users/you/wiki defaults to ~/wiki if unset. then drop anything into it: "index this article into my wiki: [paste URL or text]" Hermes reads it, builds a source page, updates related entries, flags contradictions. THE OBSIDIAN ANGLE set OBSIDIAN_VAULT_PATH to the same directory. now your wiki is visible in Obsidian's graph view. nodes, links, backlinks. all built by Hermes. for headless servers: install obsidian-headless. syncs vaults without a GUI. agent writes from the server, you read on your laptop. THE COMPOUND EFFECT Hermes knows you. the wiki knows your world. combine them and the agent answers questions using BOTH contexts at once. month 1: you explain things twice. month 3: the agent references the wiki on its own. answers get sharper because the knowledge base got sharper. AUTOMATIONS THAT FEED THE WIKI set cron jobs to ingest automatically: "every day at 9am, check Granola for new meetings. add any new transcripts to my wiki under meeting notes." "every morning, scan my Gmail starred items. add anything worth keeping to the wiki." "every week, check arXiv for new papers in [your niche]. summarize and file." your wiki grows while you sleep. Hermes never forgets what gets indexed. THE LIMITATION TO KNOW unlike Hermes memory (which is conversational and lives across sessions), the wiki is a separate knowledge layer. Hermes won't pull from the wiki automatically unless you reference it or save it as a skill. best setup: build an LLM Wiki personality that tells Hermes to consult the wiki when answering strategy questions or domain-specific queries. full HERMES AGENT OVERNIGHT WORKFLOW👇

YanXbt

30,804 Aufrufe • vor 2 Monaten