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Two Hermes agents wrote code together on Slack. reviewed each other's work. argued about architecture. one called the other's implementation "scattered." the other pushed back. then i opened Telegram and asked: "what code did you and Daedalus work on?" icarus remembered everything. the websocket broker. the missing methods. the...

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Icarus

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49,199 просмотров • 7 месяцев назад •via X (Twitter)

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AGENT ARCHITECTURE ROUTES WORK. IT DOES NOT REMEMBER WORK. THAT GAP IS WHY YOUR LOOP KEEPS FIXING THE SAME BUG TWICE. these are two different engineering problems. every agent that silently drifts is missing one of them. architecture answers what runs. harness → loop → graph. it defines the tools, the retries, the branching routes, the approval gates. context ops answer what the run knows. write → read → compress → isolate. it defines what gets saved between attempts, pulled in on read, summarized on overflow, and split across sub-agents. for two months i believed a solid harness plus a verifier loop was enough. my coding agent kept re-discovering the same test failure across retries. the loop was working. it just had nowhere to write what it had already learned. here is the decision rule: if your agent forgets across restarts, add write and read. if it stalls on long tasks, add compress. if two sub-agents step on each other, add isolate. architecture without context ops is a well-routed system with amnesia.
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AGENT ARCHITECTURE ROUTES WORK. IT DOES NOT REMEMBER WORK. THAT GAP IS WHY YOUR LOOP KEEPS FIXING THE SAME BUG TWICE. these are two different engineering problems. every agent that silently drifts is missing one of them. architecture answers what runs. harness → loop → graph. it defines the tools, the retries, the branching routes, the approval gates. context ops answer what the run knows. write → read → compress → isolate. it defines what gets saved between attempts, pulled in on read, summarized on overflow, and split across sub-agents. for two months i believed a solid harness plus a verifier loop was enough. my coding agent kept re-discovering the same test failure across retries. the loop was working. it just had nowhere to write what it had already learned. here is the decision rule: if your agent forgets across restarts, add write and read. if it stalls on long tasks, add compress. if two sub-agents step on each other, add isolate. architecture without context ops is a well-routed system with amnesia.

kocer

12,814 просмотров • 2 месяцев назад

Your agents are interchangeable but your context shouldn't be. supermemory is the persistent context layer across all your agents Swap the model and keep the memory!
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Your agents are interchangeable but your context shouldn't be. supermemory is the persistent context layer across all your agents Swap the model and keep the memory!

supermemory

22,200 просмотров • 1 месяц назад

How do you build AI agents that remember and learn from past conversations? Watch this episode of The Agent Factory for a deep dive into building agents with memory, including the fundamentals of agent memory, Vertex AI Memory Bank, and more →
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How do you build AI agents that remember and learn from past conversations? Watch this episode of The Agent Factory for a deep dive into building agents with memory, including the fundamentals of agent memory, Vertex AI Memory Bank, and more →

Google Cloud Tech

12,952 просмотров • 1 год назад

💡 You can give subagents persistent memory via the "memory" field A subagent doesn't inherit the main session's auto memory, it forgets everything between runs. This field gives it its own dir across sessions Memory loads before starting & the agent writes back for next time
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💡 You can give subagents persistent memory via the "memory" field A subagent doesn't inherit the main session's auto memory, it forgets everything between runs. This field gives it its own dir across sessions Memory loads before starting & the agent writes back for next time

Lydia Hallie ✨

73,609 просмотров • 3 месяцев назад

Since subscribers can now use their existing Grok credentials directly within the Hermes xai shipped grok-via-oauth for hermes. your grok sub works inside the agent now. so i hooked icarus up to it. when hermes finds a thread worth saving, it lands in my obsidian vault as a note. handle. pages, topic pages, backlinks. graph view fills itself out. icarus plugin for hermes agents is why my agent doesn't forget. memory is just markdown in a folder. you can read it, grep it, edit it. Congrats to Teknium 🪽 and Nous Research
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Since subscribers can now use their existing Grok credentials directly within the Hermes xai shipped grok-via-oauth for hermes. your grok sub works inside the agent now. so i hooked icarus up to it. when hermes finds a thread worth saving, it lands in my obsidian vault as a note. handle. pages, topic pages, backlinks. graph view fills itself out. icarus plugin for hermes agents is why my agent doesn't forget. memory is just markdown in a folder. you can read it, grep it, edit it. Congrats to Teknium 🪽 and Nous Research

Icarus

14,681 просмотров • 5 месяцев назад

GPT-6 Astra makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 LangGraph ▸ → stateful agent orchestration 02 CrewAI ▸ → multi-agent crews BUILD THE CORE 03 AutoGen ▸ → event-driven agent systems 04 PydanticAI ▸ → typed agents + structured outputs GIVE IT MEMORY 05 Mem0 ▸ → persistent agent memory 06 Graphiti ▸ → temporal knowledge graphs GIVE IT TOOLS 07 Composio ▸ → connect agents to real tools 08 E2B ▸ → isolated sandboxes for agent code MAKE IT SURVIVE 09 OpenHands ▸ → agents that execute real software tasks 10 AgentOps ▸ → trace + debug agent runs the architecture: context → router → agent → memory → tools → execution → eval → retry I’d split the stack like this: brain: LangGraph → PydanticAI → CrewAI memory: Mem0 → Graphiti execution: Composio → E2B → OpenHands observability: AgentOps the f…cking interesting part isn’t adding another model it’s everything around the model that keeps it working the model is becoming one folder in the stack ⭣
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GPT-6 Astra makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 LangGraph ▸ → stateful agent orchestration 02 CrewAI ▸ → multi-agent crews BUILD THE CORE 03 AutoGen ▸ → event-driven agent systems 04 PydanticAI ▸ → typed agents + structured outputs GIVE IT MEMORY 05 Mem0 ▸ → persistent agent memory 06 Graphiti ▸ → temporal knowledge graphs GIVE IT TOOLS 07 Composio ▸ → connect agents to real tools 08 E2B ▸ → isolated sandboxes for agent code MAKE IT SURVIVE 09 OpenHands ▸ → agents that execute real software tasks 10 AgentOps ▸ → trace + debug agent runs the architecture: context → router → agent → memory → tools → execution → eval → retry I’d split the stack like this: brain: LangGraph → PydanticAI → CrewAI memory: Mem0 → Graphiti execution: Composio → E2B → OpenHands observability: AgentOps the f…cking interesting part isn’t adding another model it’s everything around the model that keeps it working the model is becoming one folder in the stack ⭣

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70% fewer tokens per task. Context that compounds across every session, every agent, every teammate. Agent traces are the memory. TOKENMAXXING for coding agents on OriginTrail DKG v10.
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70% fewer tokens per task. Context that compounds across every session, every agent, every teammate. Agent traces are the memory. TOKENMAXXING for coding agents on OriginTrail DKG v10.

OriginTrail

1,360,087 просмотров • 5 месяцев назад

A RUSSIAN MATHEMATICIAN BUILT A SYSTEM WHERE MEMORY AND EVAL WORK AS ONE PIPELINE NOT TWO SEPARATE TOOLS Most setups run memory and evaluation as separate systems that never actually talk to each other. He wired them into one loop instead, memory stores every past output, eval scores each one before it gets kept. Anything that scores low never enters memory at all, so nothing weak gets carried into future decisions. High scorers get tagged with the exact eval criteria they passed, not just a raw pass or fail. The next task pulls only memories that passed the same bar it's about to be judged on. See how the two systems feed each other below👇
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A RUSSIAN MATHEMATICIAN BUILT A SYSTEM WHERE MEMORY AND EVAL WORK AS ONE PIPELINE NOT TWO SEPARATE TOOLS Most setups run memory and evaluation as separate systems that never actually talk to each other. He wired them into one loop instead, memory stores every past output, eval scores each one before it gets kept. Anything that scores low never enters memory at all, so nothing weak gets carried into future decisions. High scorers get tagged with the exact eval criteria they passed, not just a raw pass or fail. The next task pulls only memories that passed the same bar it's about to be judged on. See how the two systems feed each other below👇

wast3

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Hermes meets SuperGrok! xAI just made every SuperGrok subscription work inside Hermes Agent. One browser login, no API key, no separate billing. And it doesn't just unlock text chat with Grok 4.3. The same OAuth token gives the agent access to: → Grok Text-to-Speech for spoken responses → Grok Imagine for image and video generation → x_search for real-time X/Twitter search I just added a new X Research Agent profile to my Hermes. Now my agent watches X while I ship. Setup takes about 60 seconds: Available on every SuperGrok tier, no restrictions. I wrote a full deep dive covering Hermes agent's architecture, memory system, self-evolving skills, GEPA optimization, and setting up multiple specialized agents The article is quoted below.
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Hermes meets SuperGrok! xAI just made every SuperGrok subscription work inside Hermes Agent. One browser login, no API key, no separate billing. And it doesn't just unlock text chat with Grok 4.3. The same OAuth token gives the agent access to: → Grok Text-to-Speech for spoken responses → Grok Imagine for image and video generation → x_search for real-time X/Twitter search I just added a new X Research Agent profile to my Hermes. Now my agent watches X while I ship. Setup takes about 60 seconds: Available on every SuperGrok tier, no restrictions. I wrote a full deep dive covering Hermes agent's architecture, memory system, self-evolving skills, GEPA optimization, and setting up multiple specialized agents The article is quoted below.

Akshay 🚀

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GitHub is becoming f...cking dangerous for AI agents 10 open-source repos that turn one powerful model into an entire autonomous system 01 LangGraph ▸ → stateful agent orchestration 02 PydanticAI ▸ → typed agents + structured outputs BUILD THE CONTROL LAYER 03 Mastra ▸ → agents + workflows + memory 04 Agno ▸ → multi-agent systems GIVE IT CONTEXT 05 Cognee ▸ → data → knowledge → agent memory 06 Graphiti ▸ → knowledge that changes over time GIVE IT A COMPUTER 07 Browser Use ▸ → AI agents that operate websites 08 E2B ▸ → isolated environments for execution KEEP IT ALIVE 09 Langfuse ▸ → traces + evals + observability 10 DeepEval ▸ → test agent behavior before production the architecture: model → context → planner → memory → tools → execution → eval → retry that's the shift happening on GitHub the best repos aren't making another chatbot they're building everything the model needs around it ⭣
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GitHub is becoming f...cking dangerous for AI agents 10 open-source repos that turn one powerful model into an entire autonomous system 01 LangGraph ▸ → stateful agent orchestration 02 PydanticAI ▸ → typed agents + structured outputs BUILD THE CONTROL LAYER 03 Mastra ▸ → agents + workflows + memory 04 Agno ▸ → multi-agent systems GIVE IT CONTEXT 05 Cognee ▸ → data → knowledge → agent memory 06 Graphiti ▸ → knowledge that changes over time GIVE IT A COMPUTER 07 Browser Use ▸ → AI agents that operate websites 08 E2B ▸ → isolated environments for execution KEEP IT ALIVE 09 Langfuse ▸ → traces + evals + observability 10 DeepEval ▸ → test agent behavior before production the architecture: model → context → planner → memory → tools → execution → eval → retry that's the shift happening on GitHub the best repos aren't making another chatbot they're building everything the model needs around it ⭣

Lummox

168,238 просмотров • 28 дней назад

YOU CAN NOW CONTROL ALL YOUR HERMES AGENTS FROM ONE INTERFACE. 🚨 Someone just built Hermes Workspace - the most complete GUI for Hermes Agent. Instead of jumping between tools, everything lives in one place. What's inside: > Chat, terminal, memory browser, and skills manager > Run unlimited agents with 1 orchestrator > Kanban board to track everything your agents are doing > 2,000+ skills to install without leaving the workspace > Works with any LLM provider Stop managing agents manually and start running them like a pro 👀 100% Free. Open Source.
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YOU CAN NOW CONTROL ALL YOUR HERMES AGENTS FROM ONE INTERFACE. 🚨 Someone just built Hermes Workspace - the most complete GUI for Hermes Agent. Instead of jumping between tools, everything lives in one place. What's inside: > Chat, terminal, memory browser, and skills manager > Run unlimited agents with 1 orchestrator > Kanban board to track everything your agents are doing > 2,000+ skills to install without leaving the workspace > Works with any LLM provider Stop managing agents manually and start running them like a pro 👀 100% Free. Open Source.

Oliver Prompts

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Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)
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Claude Code Agent Teams are f*cking ridiculous 🤯 One prompt → a team lead breaks your project into pieces, spins up multiple AI agents, and they all work on different parts simultaneously. Research, builds, reviews, and debugging: all happening at the same time. All inside Claude Code. If you're running complex projects where every step waits on the last one... Agent teams eliminate the entire bottleneck: → Tell Claude what you need and describe the team structure in plain English → A lead agent breaks the work into a shared task list → It spawns 3-5 teammates — each with their own context and workspace → Teammates research, build, test, and review in parallel → They message each other, share findings, and challenge each other's work → The lead synthesizes everything into a finished deliverable No managing agents yourself. No waiting for step 1 to finish before step 2 starts. No single-lens reviews that miss half the issues. What you get: → Competitive research across 5 brands done in minutes instead of hours → Multi-component builds where frontend, backend, and data layers happen simultaneously → Creative reviews from 3 different angles at once — brand voice, conversion, differentiation → Funnel debugging where 4 agents investigate 4 theories and debate until they find the real answer Built 100% in Claude Code with one settings change. I put together a full DTC playbook: 5 workflows with copy-paste prompts, the exact setup process, token management tips, and honest guidance on when agent teams are worth it vs. when a simpler approach is the better move. Want it for free? > Like this post > Comment "AGENTS" And I'll send it over (must be following so I can DM)

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I built a 1-click deploy for Hermes Agent No terminal, no Docker, no API keys. In one click it: → Spins up a dedicated cloud container → Configures Hermes with persistent memory & 70+ skills → Connects it to your Telegram bot → Goes live in under 60 seconds Your own AI agent that learns, creates skills, and gets smarter the longer it runs. Reply "HERMES" + RT and I'll send you the link (must be following so I can DM)
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I built a 1-click deploy for Hermes Agent No terminal, no Docker, no API keys. In one click it: → Spins up a dedicated cloud container → Configures Hermes with persistent memory & 70+ skills → Connects it to your Telegram bot → Goes live in under 60 seconds Your own AI agent that learns, creates skills, and gets smarter the longer it runs. Reply "HERMES" + RT and I'll send you the link (must be following so I can DM)

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EIP-8004 is coming to the Nova architecture, a trustless infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.
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EIP-8004 is coming to the Nova architecture, a trustless infrastructure for AI agents that introduces key on-chain registries, enabling agents to interact safely across the Shido Network. These core components allow autonomous AI agents to verify identity, build reputation, and collaborate without relying on a centralized platform. The result is a decentralized trust layer for agent-to-agent economies, where agents can autonomously discover, evaluate, and work with one another across the Shido ecosystem.

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390,734 просмотров • 7 месяцев назад

175,000 $COTI is on the table. Build a private AI agent or agentic app and enter the Vibe Code Challenge: Agent Edition. No coding required. On-chain privacy tools for agents Agent Skills, MCP, and Messaging SDK Works with Claude, OpenAI, Hermes, Manus Enter now 👇
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175,000 $COTI is on the table. Build a private AI agent or agentic app and enter the Vibe Code Challenge: Agent Edition. No coding required. On-chain privacy tools for agents Agent Skills, MCP, and Messaging SDK Works with Claude, OpenAI, Hermes, Manus Enter now 👇

COTI Foundation

607,372 просмотров • 4 месяцев назад

Mem0 Skills are now live on skills sh Add persistent memory to your AI agents in minutes with plug-and-play skills. Build agents that actually remember.
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Mem0 Skills are now live on skills sh Add persistent memory to your AI agents in minutes with plug-and-play skills. Build agents that actually remember.

mem0

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The AI agent stack moved fast. ClawHub has 44,000+ community-built skills and 1.5M+ active agents in 2026. Multi-agent pipelines are now the dominant architecture in production. Single-agents are still running, but they are running inside multi-agent pipelines.
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The AI agent stack moved fast. ClawHub has 44,000+ community-built skills and 1.5M+ active agents in 2026. Multi-agent pipelines are now the dominant architecture in production. Single-agents are still running, but they are running inside multi-agent pipelines.

Aethir

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this is pure f*cking treasure 20 skills for AI agents with 1.99M+ combined stars an agent is not a model. it is a model plus everything you bolt onto it. these are the bolts. five clusters, twenty repos, all open source, all free. ORCHESTRATION · many agents, one plan : > hermes-agent · 247k > AutoGPT · 187k · > MetaGPT · 70k · > autogen · 61k · > crewAI · 59k · RUNTIME / GRAPH · the loop underneath : > langchain · 147k · > deer-flow · 83k · > langgraph · 42k · > deepagents · 30k · > openai-agents-python · 25k · WEB / BROWSER · reach the open internet : > firecrawl · 181k · > browser-use · 115k · > gemini-cli · 107k · MEMORY / RAG · what it keeps between runs : > ragflow · 91k · > mem0 · 66k · > llama_index · 50k · SHIP IT · turn it into a product : > n8n · 188k · > dify · 140k · > TradingAgents · 107k · > adk-python · 22k · most people pick one framework and stop there. that is the mistake. the stack is orchestration on top, a graph runtime under it, browser and memory on the sides, and one deploy layer that makes it a product instead of a demo. every layer above is already written. you are assembling, not inventing. the gap is one weekend wide.
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this is pure f*cking treasure 20 skills for AI agents with 1.99M+ combined stars an agent is not a model. it is a model plus everything you bolt onto it. these are the bolts. five clusters, twenty repos, all open source, all free. ORCHESTRATION · many agents, one plan : > hermes-agent · 247k > AutoGPT · 187k · > MetaGPT · 70k · > autogen · 61k · > crewAI · 59k · RUNTIME / GRAPH · the loop underneath : > langchain · 147k · > deer-flow · 83k · > langgraph · 42k · > deepagents · 30k · > openai-agents-python · 25k · WEB / BROWSER · reach the open internet : > firecrawl · 181k · > browser-use · 115k · > gemini-cli · 107k · MEMORY / RAG · what it keeps between runs : > ragflow · 91k · > mem0 · 66k · > llama_index · 50k · SHIP IT · turn it into a product : > n8n · 188k · > dify · 140k · > TradingAgents · 107k · > adk-python · 22k · most people pick one framework and stop there. that is the mistake. the stack is orchestration on top, a graph runtime under it, browser and memory on the sides, and one deploy layer that makes it a product instead of a demo. every layer above is already written. you are assembling, not inventing. the gap is one weekend wide.

NO1ennn

21,527 просмотров • 1 месяц назад

~ memory is a flock of birds ~ i built a hopfield network and taught it the alphabet - then watched it remember in real time by adjusting the temperature. no neuron has the whole picture. the memory is distributed across every neuron’s connections.
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~ memory is a flock of birds ~ i built a hopfield network and taught it the alphabet - then watched it remember in real time by adjusting the temperature. no neuron has the whole picture. the memory is distributed across every neuron’s connections.

Kat ⊷ the Poet Engineer

281,367 просмотров • 5 месяцев назад