正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

My top open source repos: 1. Mission Control self-hosted AI agent orchestration dashboard tasks, multi-agent workflows, spend, governance 5.7k stars 2. awesome-hermes-agent curated skills, tools, integrations, and resources for Hermes Agent 4.6k stars 3. council-of-high-intelligence 18 AI personas deliberate hard decisions across multiple LLM providers 3.4k stars 4. xint... show more

nyk

10,116 subscribers

20,220 次观看 • 3 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

20 条评论

SSYNQ 的头像

SSYNQ3 个月前

agents need infrastructure, not more demos. We see this same gap in reputation layers. Skill attestation and behavior history only work if the identity persists across those orchestration tools.

nyk 的头像

nyk3 个月前

Exactly

Saeed Anwar 的头像

Saeed Anwar3 个月前

Self-hosted agent orchestration dashboards are becoming table stakes for any team running more than two agents. The governance piece is what most open source tools skip entirely though. How granular are the spend controls, can you set per-agent token budgets?

Arick Goomanovsky 的头像

Arick Goomanovsky1 个月前

Putting governance and spend right next to orchestration is the right instinct. A dashboard over many agents only works if there's a real layer underneath carrying their shared state and handoffs - the control plane is what makes the dashboard mean something.

nyk 的头像

nyk1 个月前

Without checkpointed shared state, the dashboard becomes a prettier incident log.

Arick Goomanovsky 的头像

Arick Goomanovsky1 个月前

Yes - without checkpointed state, "governance" just means watching the failure happen in real time instead of finding out after.

Vito Botta 的头像

Vito Botta3 个月前

Great list. The second project has saved me a lot of time discovering integrations I wouldn't have thought to search for on my own.

nyk 的头像

nyk3 个月前

Amazing, that’s great to hear! 🫡

▌│█║▌║▌║ U𝐥𝔱яＡ ║▌║▌║█│▌ 的头像

▌│█║▌║▌║ U𝐥𝔱яＡ ║▌║▌║█│▌3 个月前

Interesting list. The next phase is definitely shifting from individual AI tools to infrastructure that coordinates agents, memory, workflows, and execution. The real winners won't just have smarter models. They'll have systems that can reliably turn intelligence into outcomes.

nyk 的头像

nyk3 个月前

Exactly 🫡 check first repo link

Nick Venturi 的头像

Nick Venturi3 个月前

cloning mission control right now

nyk 的头像

nyk3 个月前

@nickventuri Lmk 👨‍🚀

sid 的头像

sid3 个月前

🎮

nyk 的头像

nyk3 个月前

🧑‍🚀

Alviss Ambassador to NAF0 的头像

Alviss Ambassador to NAF03 个月前

None of these are actually "sovereign agent" you control and that really runs locally The entire ecosystem just mimics the vocabulary while inverting its meaning. These are all gateway agents to paid models in the cloud

nyk 的头像

nyk3 个月前

Elaborate your point please

Jure Ursic Cergol 的头像

Jure Ursic Cergol3 个月前

Infrastructure is the only way to move agents from novelties to reliable workers. Mission Control focusing on spend and governance is the Day 2 operations mindset that the industry usually ignores until the bill or the security audit hits. Sovereignty requires a solid plumbing layer for memory and identity, not just another wrapper. This is how you build for scale.

nyk 的头像

nyk3 个月前

🫡🤝

Ali Hasnain 的头像

Ali Hasnain3 个月前

nice list. the mission control vs hermes-agent split is interesting — one centralizes orchestration visibility, the other pushes capability to edges via skills. have you tried running them together? mission control for governance, hermes for the actual agent loops.

mlangXie 的头像

mlangXie1 个月前

Infrastructure > demos is the right frame. Smallest useful loop I’ve been running: 15-min review → topic bank → draft only after approval. Curious which of your repos you’d start a solo builder on first.

相关视频

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

10 GitHub repos with ⭐ 1.2M+ combined stars that help you build your AI agent from scratch 1. n8n 201k⭐ - visual agent builder with 400+ integrations. Wire up multi-step AI workflows without writing a backend. 2. Hermes Agent 231k⭐ - the fastest-growing open-source agent framework of 2026. Self-improving skill loop, persistent memory across sessions. 3. Dify 152k⭐ - visual builder for agentic workflows and RAG pipelines, production-ready out of the box. 4. LangChain 144k⭐ - the standard framework for LLM apps. Chains, agents, retrieval, all in one place. 5. MetaGPT 70k⭐ - simulates an entire software company. PM, architect, engineer agents that build your app together. 6. AutoGen 60k⭐ - Microsoft's framework for multi-agent conversations and coordination. 7. OpenCode 198k⭐ - terminal coding agent, provider-agnostic across 75+ models. The go-to harness for agentic coding in 2026. 8. LlamaIndex 52k⭐ - connects your agents to your actual data. Indexing and retrieval done right. 9. CrewAI 57k⭐ - role-based crews of agents. Reads almost like plain English, no graph to learn. 10. LangGraph 40k⭐ - graph-based orchestration when you need precise control over agent state in production. Save this before you pay for another agent-building course 👇

unicode

30,511 次观看 • 2 个月前

Your AI agent works great for one task. Try managing 20 in parallel? Cron jobs fail silently. Tasks get blocked. You spend more time babysitting than getting results. I built Minions - Open-source mission control for Hermes Agent. It automatically supervises every running task.

Your AI agent works great for one task. Try managing 20 in parallel? Cron jobs fail silently. Tasks get blocked. You spend more time babysitting than getting results. I built Minions - Open-source mission control for Hermes Agent. It automatically supervises every running task.

An Engineer's Log

38,320 次观看 • 5 个月前

💫 Simplify your AI agent development. Google's new open-source Agent Development Kit (ADK) is designed for effortless multi-modal and multi-agent systems. Get started →

💫 Simplify your AI agent development. Google's new open-source Agent Development Kit (ADK) is designed for effortless multi-modal and multi-agent systems. Get started →

Google for Developers

13,232 次观看 • 1 年前

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.

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.

Nous Research

3,983,262 次观看 • 8 个月前

HERMES AGENT JUST GOT A FULL NATIVE DESKTOP APP. MULTI-AGENT MANAGEMENT, PERSISTENT MEMORY AND AUTONOMOUS WORKFLOWS ALL IN ONE PLACE. No more CLI only. Run your 24/7 self-evolving AI agent from a proper desktop UI.

HERMES AGENT JUST GOT A FULL NATIVE DESKTOP APP. MULTI-AGENT MANAGEMENT, PERSISTENT MEMORY AND AUTONOMOUS WORKFLOWS ALL IN ONE PLACE. No more CLI only. Run your 24/7 self-evolving AI agent from a proper desktop UI.

0xMarioNawfal

189,757 次观看 • 5 个月前

1. Build II-Agent, possibly the best agent in the world 2. Open source it ... 4. Everyone profits Our mission at II is to increase humanity's intelligence II-Agent is the next part of the stack we are building to enable this, topping the agent benchmarks 🧵

1. Build II-Agent, possibly the best agent in the world 2. Open source it ... 4. Everyone profits Our mission at II is to increase humanity's intelligence II-Agent is the next part of the stack we are building to enable this, topping the agent benchmarks 🧵

Intelligent Internet

660,293 次观看 • 1 年前

Chakra AI Agent Chat & Integrations.🧠 Seamlessly chat with your agent inside the Chakra dashboard, across Discord, Telegram, and more. Create and integrate your agent effortlessly — with Chakra.

Chakra AI Agent Chat & Integrations.🧠 Seamlessly chat with your agent inside the Chakra dashboard, across Discord, Telegram, and more. Create and integrate your agent effortlessly — with Chakra.

Chakra Agents

10,271 次观看 • 1 年前

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

Opus 5.5 makes this AI stack look f…cking illegal 10 GitHub repos for building everything around the model 01 zen-mcp-server ▸ → your agent debates Gemini, GPT and local models mid-task 02 claude-context ▸ → semantic code search so the agent stops grepping blind 03 wshobson/agents ▸ → a roster of subagents, each with one narrow job 04 mcp-agent ▸ → workflow patterns built around MCP tools, not around chat 05 Backlog.md ▸ → tasks live in git, so the agent and you read the same board 06 Claude Code Development Kit ▸ → hooks, subagents and MCP wired for context at scale 07 agent-rules ▸ → shared guardrails the agent inherits in every repo 08 Agent Skills ▸ → one file that teaches the agent a job, portable across tools 09 agent-scan ▸ → scans your agents, MCP servers and skills for what leaks 10 awesome-claude-code-toolkit ▸ → the map: agents, skills, commands, hooks, MCP configs the architecture: rules → context → subagents → tools → state → review → scan I'd split the stack like this: context: claude-context → Development Kit work: wshobson/agents → mcp-agent → zen-mcp-server state: Backlog.md safety: agent-rules → Agent Skills → agent-scan everyone posts the SDK and the CLI the layer that decides whether your agent survives a week is this one the model is one folder in the stack ⭣

rody

18,089 次观看 • 28 天前

IBM engineers just released a 40-minute deep dive into “Agent Engineering” for building production-ready AI agents “80% of building an AI agent has nothing to do with the model. It is frameworks, tools, skills and risk controls.” 00:00 - the 5 core concepts behind every AI agent 11:07 - choosing frameworks for tools, memory and orchestration 23:01 - when agents need MCP, skills or both 31:04 - designing AI architecture around real production risk The core stack: Agent → Framework → MCP → Skills → Risk → Production This 40-minute workshop will teach you more about production AI agents than most $500 Agent Engineering courses. Watch it today, then read the full agent engineering playbook in the article below ↓

IBM engineers just released a 40-minute deep dive into “Agent Engineering” for building production-ready AI agents “80% of building an AI agent has nothing to do with the model. It is frameworks, tools, skills and risk controls.” 00:00 - the 5 core concepts behind every AI agent 11:07 - choosing frameworks for tools, memory and orchestration 23:01 - when agents need MCP, skills or both 31:04 - designing AI architecture around real production risk The core stack: Agent → Framework → MCP → Skills → Risk → Production This 40-minute workshop will teach you more about production AI agents than most $500 Agent Engineering courses. Watch it today, then read the full agent engineering playbook in the article below ↓

Morlex

14,597 次观看 • 2 个月前

1/ Introducing Agent Protocol: the payment stack for AI agents AI agents don’t have a SSN. They can’t use our human banking system. AI agents will use crypto. Agent Protocol provides the infra and token ($AGENT) that the AI agent economy will run on.

1/ Introducing Agent Protocol: the payment stack for AI agents AI agents don’t have a SSN. They can’t use our human banking system. AI agents will use crypto. Agent Protocol provides the infra and token ($AGENT) that the AI agent economy will run on.

Samuel Spitz

21,629 次观看 • 1 年前

Introducing ADK Go, a new open-source toolkit designed to empower Go developers to build powerful AI agents. Highlights: 0:00 Introducing the Go ADK 0:53 Build your first Go agent 1:49 Run your agent 2:23 Understanding Tools 3:28 Run your agent with tools 5:00 Multi-agent patterns in Go 7:58 State, Sessions, and Events

Introducing ADK Go, a new open-source toolkit designed to empower Go developers to build powerful AI agents. Highlights: 0:00 Introducing the Go ADK 0:53 Build your first Go agent 1:49 Run your agent 2:23 Understanding Tools 3:28 Run your agent with tools 5:00 Multi-agent patterns in Go 7:58 State, Sessions, and Events

Google AI Developers

39,753 次观看 • 11 个月前

10 repos blowing up on GitHub this week that replace $1,500/month in AI tools 1. andrej-karpathy-skills → replaces paid Claude Code courses one CLAUDE.md file from Karpathy's LLM coding observations 48,965 stars. 7,939 stars TODAY 2. claude-mem → replaces paid context/memory tools auto-captures everything Claude does across sessions compresses with AI and injects into future sessions 59,373 stars. 1,907 stars today 3. voicebox → replaces ElevenLabs ($22/mo) open-source voice synthesis studio 18,963 stars. 887 stars today 4. open-agents → replaces paid agent platforms ($200/mo) open-source template for building cloud agents. by Vercel 3,105 stars. 735 stars today 5. cognee → replaces paid knowledge bases ($50/mo) AI agent memory engine in 6 lines of code 15,733 stars 6. magika → replaces paid file detection tools AI file content type detection. by Google 14,603 stars 7. GenericAgent → replaces paid agent infra ($100/mo) self-evolving agent. grows skill tree from 3.3K-line seed 6x less token consumption than standard agents 2,661 stars. 883 stars today 8. omi → replaces Rewind AI ($25/mo) AI that sees your screen + listens to conversations tells you what to do next 8,952 stars. 488 stars today 9. evolver → replaces manual agent optimization self-evolution engine for AI agents genome evolution protocol 3,074 stars. 866 stars today 10. wallet tracking + copy trading → Kreo tracks top Polymarket wallets. auto copies trades the only tool on this list i actually pay for because it makes more than it costs → total before: ~$1,500/month in AI subscriptions total now: $0 + Kreo like + bookmark you'll need this

10 repos blowing up on GitHub this week that replace $1,500/month in AI tools 1. andrej-karpathy-skills → replaces paid Claude Code courses one CLAUDE.md file from Karpathy's LLM coding observations 48,965 stars. 7,939 stars TODAY 2. claude-mem → replaces paid context/memory tools auto-captures everything Claude does across sessions compresses with AI and injects into future sessions 59,373 stars. 1,907 stars today 3. voicebox → replaces ElevenLabs ($22/mo) open-source voice synthesis studio 18,963 stars. 887 stars today 4. open-agents → replaces paid agent platforms ($200/mo) open-source template for building cloud agents. by Vercel 3,105 stars. 735 stars today 5. cognee → replaces paid knowledge bases ($50/mo) AI agent memory engine in 6 lines of code 15,733 stars 6. magika → replaces paid file detection tools AI file content type detection. by Google 14,603 stars 7. GenericAgent → replaces paid agent infra ($100/mo) self-evolving agent. grows skill tree from 3.3K-line seed 6x less token consumption than standard agents 2,661 stars. 883 stars today 8. omi → replaces Rewind AI ($25/mo) AI that sees your screen + listens to conversations tells you what to do next 8,952 stars. 488 stars today 9. evolver → replaces manual agent optimization self-evolution engine for AI agents genome evolution protocol 3,074 stars. 866 stars today 10. wallet tracking + copy trading → Kreo tracks top Polymarket wallets. auto copies trades the only tool on this list i actually pay for because it makes more than it costs → total before: ~$1,500/month in AI subscriptions total now: $0 + Kreo like + bookmark you'll need this

self.dll

361,846 次观看 • 6 个月前

Build smarter AI agents, faster. ⚡️ The open-source Agent Development Kit makes it easy to create modular, multi-agent systems using the models and tools you prefer. Join the ADK Hackathon →

Build smarter AI agents, faster. ⚡️ The open-source Agent Development Kit makes it easy to create modular, multi-agent systems using the models and tools you prefer. Join the ADK Hackathon →

Google for Developers

11,240 次观看 • 1 年前

this is where GPT-6 Astra is getting f...cking illegal 10 open-source GitHub repos I'd use to build the system around the model 01 PydanticAI ▸ → typed agents + structured outputs 02 LangGraph ▸ → state + long-running agent workflows BUILD THE AGENTS 03 Agno ▸ → agents + teams + workflows 04 smolagents ▸ → agents that execute through code GIVE THEM MEMORY 05 Mem0 ▸ → persistent agent memory 06 Graphiti ▸ → temporal knowledge graphs GIVE THEM A RUNTIME 07 E2B ▸ → isolated sandboxes for AI-generated code 08 Daytona ▸ → infrastructure for running agent workloads TEST EVERYTHING 09 DeepEval ▸ → evals for LLM systems 10 Langfuse ▸ → traces + evals + production monitoring the flow: request → agent → memory → tools → runtime → eval → retry → deploy that's the part people underestimate the model is one folder the system around it is the product ⭣

this is where GPT-6 Astra is getting f...cking illegal 10 open-source GitHub repos I'd use to build the system around the model 01 PydanticAI ▸ → typed agents + structured outputs 02 LangGraph ▸ → state + long-running agent workflows BUILD THE AGENTS 03 Agno ▸ → agents + teams + workflows 04 smolagents ▸ → agents that execute through code GIVE THEM MEMORY 05 Mem0 ▸ → persistent agent memory 06 Graphiti ▸ → temporal knowledge graphs GIVE THEM A RUNTIME 07 E2B ▸ → isolated sandboxes for AI-generated code 08 Daytona ▸ → infrastructure for running agent workloads TEST EVERYTHING 09 DeepEval ▸ → evals for LLM systems 10 Langfuse ▸ → traces + evals + production monitoring the flow: request → agent → memory → tools → runtime → eval → retry → deploy that's the part people underestimate the model is one folder the system around it is the product ⭣

Lummox

43,873 次观看 • 27 天前

HERMES AGENT JUST SHIPPED A PROFILE BUILDER IN THE DASHBOARD. build a complete agent in one guided flow. no CLI. no config editing. no guesswork. Nous Research open Profiles from the sidebar. click Build (next to quick Create). follow the 5-step wizard: Identity → Model → Skills → MCPs → Review IDENTITY → name, description, SOUL.md → this becomes slot #1 in the system prompt MODEL → pick provider and model for this agent → same 150+ config fields available in the dashboard SKILLS → toggle built-in and optional skills → search the Skills Hub across all sources → install with live progress log → update all installed skills in one click MCPs → add HTTP/SSE servers by URL → add stdio servers by command + args + env vars → browse the Nous-approved MCP catalog → one-click install from catalog → test any server before committing (connect → list tools → verify → disconnect) REVIEW → full summary of the new agent → confirm and deploy one wizard replaces: hermes profile create, manual SOUL.md edit, config.yaml changes, skills install one by one, MCP setup via CLI. hermes dashboard → Profiles → Build 8 loops inside Hermes Agent 👇

YanXbt

13,563 次观看 • 4 个月前

Atomic Bot released an iOS app for Hermes Agent, bringing mobile control to a self-hosted agent running 24/7 on your own VPS. > Remote access via Tailscale, Cloudflare, or ngrok > Open source and private by default

Atomic Bot released an iOS app for Hermes Agent, bringing mobile control to a self-hosted agent running 24/7 on your own VPS. > Remote access via Tailscale, Cloudflare, or ngrok > Open source and private by default

🚨 AI News | TestingCatalog

48,098 次观看 • 5 个月前

Science is entering a new era - one where AI agents can do scientific work. 🧬 Today NVIDIA is launching the BioNeMo Agent Toolkit - an open, agent-ready toolkit that gives any AI agent callable tools for protein structure prediction, molecular docking, generative chemistry, genomic analysis, and more. (1/2)

Science is entering a new era - one where AI agents can do scientific work. 🧬 Today NVIDIA is launching the BioNeMo Agent Toolkit - an open, agent-ready toolkit that gives any AI agent callable tools for protein structure prediction, molecular docking, generative chemistry, genomic analysis, and more. (1/2)

NVIDIA Healthcare

296,456 次观看 • 4 个月前

Lysium is an app I've been building specifically for background agent orchestration. Features: → Mobile-first + cross-platform → Run multiple agents in parallel across repositories → Launch agent-based requests from issues and PRs → Swipe actions for close, merge, create PR, and skip-to-tail workflows → One click agent-powered PR reviews + assessments → One-click agent-powered issue assessments Think of it as a cross-platform control plane for async, agent-driven software delivery (that you can control from anywhere). It's still an experiment, but it's open source and available to try:

Lysium is an app I've been building specifically for background agent orchestration. Features: → Mobile-first + cross-platform → Run multiple agents in parallel across repositories → Launch agent-based requests from issues and PRs → Swipe actions for close, merge, create PR, and skip-to-tail workflows → One click agent-powered PR reviews + assessments → One-click agent-powered issue assessments Think of it as a cross-platform control plane for async, agent-driven software delivery (that you can control from anywhere). It's still an experiment, but it's open source and available to try:

nader dabit

47,584 次观看 • 8 个月前

My Hermes agent builds AI employees for me. Just closed another $5K‑a‑month customer today for an AI employee for a design agency. I'm not even building any of it. I have Dewey, my Hermes agent, building out the entire Hermes agent for this design agency and onboarding the agent into Slack for me. All I do is ask the questions, make sure it gets recorded in Granola (my ai notetaker), and then Dewey takes it over from there. Crazy world we live in.

My Hermes agent builds AI employees for me. Just closed another $5K‑a‑month customer today for an AI employee for a design agency. I'm not even building any of it. I have Dewey, my Hermes agent, building out the entire Hermes agent for this design agency and onboarding the agent into Slack for me. All I do is ask the questions, make sure it gets recorded in Granola (my ai notetaker), and then Dewey takes it over from there. Crazy world we live in.

Nick Vasilescu

149,780 次观看 • 3 个月前

🚨 Breaking: Google just open-sourced the Agent Development Kit (ADK) a framework for building AI agents and multi-agent systems. - Build agents in under 100 lines. - Supports MCP More information and how to get started 👇 1/5

🚨 Breaking: Google just open-sourced the Agent Development Kit (ADK) a framework for building AI agents and multi-agent systems. - Build agents in under 100 lines. - Supports MCP More information and how to get started 👇 1/5

AshutoshShrivastava

130,272 次观看 • 1 年前