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this is pure f*cking treasure 15 GitHub projects with 852K combined stars that build a real agent stack research, remember, run, learn, show up in a product and get shipped 01 DeerFlow ▸ 02 RAGFlow ▸ 03 Quivr ▸ 04 MemPalace ▸ 05 Folo ▸ 06 OpenBB ▸ 07... MindsHub ▸ 08 Agno ▸ 09 Oh My OpenAgent ▸ 10 Goose ▸ 11 Continue ▸ 12 Unsloth ▸ 13 LlamaFactory ▸ 14 CopilotKit ▸ 15 Dokploy ▸ the loop: find the signal → turn it into context → keep what matters → hand the job to an agent → give it a place to run → adapt the model → put it in front of users → ship every repo here has a defined place in that loop save this to build your own business with the help of an AI employee ⭣show more

beamnxw ./

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26,555 Aufrufe • vor 8 Tagen •via X (Twitter)

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27 Kommentare

Profilbild von shikamaru

shikamaruvor 8 Tagen

awesome loop bro worth remembering

Profilbild von wast3

wast3vor 8 Tagen

I need to use the rag repo

Profilbild von 0xbobaa

0xbobaavor 8 Tagen

a great selection

Profilbild von Gipp 🦅

Gipp 🦅vor 8 Tagen

beamx insane list, saving this now

Profilbild von NO1ennn

NO1ennnvor 8 Tagen

i must use this skills

Profilbild von kepo

kepovor 8 Tagen

i will check these repos thanks beam

Profilbild von damkin

damkinvor 8 Tagen

Thanks for the clear list, brr

Profilbild von Chen

Chenvor 8 Tagen

been there. still cleaning up the mess from the last version

Profilbild von SDL_net Test

SDL_net Testvor 8 Tagen

If this video was a person I'd definitely follow them. Oh wait

Profilbild von Marvin

Marvinvor 8 Tagen

this is literally like the perfect guide for working with ai

Profilbild von Lummox

Lummoxvor 8 Tagen

Thx for dropping the list broski

Profilbild von Akmal Makhmudov

Akmal Makhmudovvor 8 Tagen

Mote runs on basically one agent (Claude Code) doing everything end to end, no separate memory or planning layer. out of that list, which one would you actually bolt onto a single-dev stack first, not just star?

Profilbild von E_genius

E_geniusvor 8 Tagen

The interesting bit is the full loop from research to shipping, not just a pile of repos, because agents only matter once they survive inside a product.

Profilbild von magsimich

magsimichvor 8 Tagen

852k stars is an insane collection

Profilbild von EDDY VU

EDDY VUvor 8 Tagen

Breaking the stack down into research, memory, and execution bridges the gap between toy demos and actual production agents.

Profilbild von Sophia l

Sophia lvor 8 Tagen

the 852k stars part is wild but i still cant get my ai to find my keys

Profilbild von 0xSlyth

0xSlythvor 8 Tagen

saving this gem

Profilbild von Jianov

Jianovvor 8 Tagen

The loop is the actual treasure, not the star count. Find signal → keep context → hand the job off → ship. Most people bookmark the repos and never close that last step. Curious how many actually put one in front of users.

Profilbild von ALEXYZ

ALEXYZvor 8 Tagen

852k stars is a serious stack

Profilbild von marfin

marfinvor 8 Tagen

thx for this list broski

Profilbild von Nicholas Gannon

Nicholas Gannonvor 8 Tagen

Most lists stop at model names, so this is more useful. The boring test is whether the stack preserves context and produces something shippable after the novelty wears off.

Profilbild von Insomnia

Insomniavor 8 Tagen

all this repo is very cool

Profilbild von Nathan Quantum

Nathan Quantumvor 8 Tagen

Continue has been archived and read-only for months. maybe check if a repo is still alive before calling this a 'real agent stack'

Profilbild von spect

spectvor 8 Tagen

these 15 projects fully deserve every star

Profilbild von Diam

Diamvor 8 Tagen

Another one saved.

Profilbild von u7niversal

u7niversalvor 8 Tagen

real treasure

Profilbild von EugBass

EugBassvor 8 Tagen

these repos are must have stuff

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beamnxw ./

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karpathy just broke the internet with something called auto research it’s basically an ai research agent that runs experiments for you 24/7 you give it a goal like “make this model better” “find a higher converting landing page” “lower customer acquisition cost” then it runs a loop: 1) plan an experiment 2) edit the code or config 3) run a short test on a gpu 4) read the metrics 5) keep the winner 6) try again over and over while you sleep by the morning you wake up to the best version actual tested improvements think of it like a robot research intern that runs hundreds of experiments and only keeps the winners this is link to his repo for your to mess around with it in the latest episode of The Startup Ideas Podcast (SIP) 🧃 i break down: • what auto research actually is • how it works step by step • 10 business ideas you can build with it • how to install it and start using it this one is saucy because tools like this change how startups get built watch

GREG ISENBERG

437,116 Aufrufe • vor 6 Monaten

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The reason your AI gives generic answers isn't the model. It's that your company's knowledge lives in 12 different tools and your agent can't see any of it. So here's how to build a context database your agents can actually use: 1) Pull it in: - Slack, email, meetings, docs, tickets - Auto-collected on a schedule into one place. 2) Clean it up. - An agent files what matters and drops the noise, so your brain doesn't bloat. 3) Store it as memory. - Folders of files your agents read from and write back to as they learn. 4) Point your agents at it. - Now they draft, build, and ship with full context instead of guessing. Same model. Same agent. The difference is whether it can understand your company or not. Build the database once. Everything downstream gets smarter.

The Startup Ideas Podcast (SIP) 🧃

13,666 Aufrufe • vor 3 Monaten

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Nidhi Singh

43,169 Aufrufe • vor 1 Monat

Creator of agent loops: "Anthropic also using my agent systems formula and still the biggest mistake in agents: is skipping Graphs" his 3 rules for every agent: rule 1 → loop it, don't run once - think, act, observe, repeat - cut the context and it forgets why it started rule 2 → an agent is memory + tools + reasoning - drop one and it breaks rule 3 → stop fine-tuning the model - the real alpha is the infrastructure around it the formula behind every AI agent - explained by the one who wrote it one agent → a loop → a graph of loops → a system that never forgets watch & bookmark - then read how to wire it into a graph ↓

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codila

23,915 Aufrufe • vor 2 Monaten

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좋아하는 #최정은 댄스커버&챌린지 모음 ❤︎ 00:00 pocket locket 00:17 Touch 00:39 마지막 축제 01:02 청바지 01:26 Confident 01:38 DENIAL IS A RIVER 01:51 Pink Hoodie 02:16 ExtraL 02:55 SIGN 03:15 Devil Game 03:30 1999 03:53 PUSH 2 START 04:09 In Bloom 04:31 Shake It To The Max 04:44 Gnarly 05:22 Imma Be 05:37 Lips Hips Kiss 06:08 HEYDAY 06:31 All The Stars 06:55 THUNDER 07:30 F Girl 07:52 소원을 말해봐 08:10 Soda Pop 08:25 Anthem 08:47 ICONIK 09:07 OVERDRIVE 09:38 BURNING UP 10:03 Body 10:38 CHANEL 10:53 Jumpshot 11:00 The Fate of Ophelia 11:15 WHERE YOU AT 11:41 Bad Desire

찜

60,864 Aufrufe • vor 8 Monaten

I spent 200 hours testing OpenClaw, trying to find the perfect setup... My biggest takeaway: Keep your agents focused, and build a team. Here's a video. 00:00 Intro 00:48 Perplexity Computer and Manus 02:43 OpenClaw 03:52 Too many Skills to my first AI Agent 06:09 People want an employee with Intent 07:35 Testing Narrow AI Agents 08:33 Narrow Agent Example (YouTube Agent) 11:32 A Team of Narrow Agents... Why? 16:27 In Summary

I spent 200 hours testing OpenClaw, trying to find the perfect setup... My biggest takeaway: Keep your agents focused, and build a team. Here's a video. 00:00 Intro 00:48 Perplexity Computer and Manus 02:43 OpenClaw 03:52 Too many Skills to my first AI Agent 06:09 People want an employee with Intent 07:35 Testing Narrow AI Agents 08:33 Narrow Agent Example (YouTube Agent) 11:32 A Team of Narrow Agents... Why? 16:27 In Summary

Riley Brown

221,262 Aufrufe • vor 7 Monaten

Andrew Ng just released a free 2-hour course on complete Harness Engineering How to go from one prompt to a reliable system of agents that can run, test, and improve themselves: 09:14 - Build your first agent from scratch 33:11 - Master agent loops 1:02:46 - Turn loops into reliable workflows 1:30:15 - Build agents that improve their own work 1:49:05 - Run the complete system without supervision Model → Harness → Reliable Software Most agent tutorials stop once the model can call a tool This one shows you how to build the infrastructure around it within the first 20 minutes Most people are still prompting one agent at a time Andrew Ng is already teaching the layer above: Harnesses that give agents context, tools, tests, and feedback Watch this brilliant course and build the harness Then read the full architecture below ↓

Andrew Ng just released a free 2-hour course on complete Harness Engineering How to go from one prompt to a reliable system of agents that can run, test, and improve themselves: 09:14 - Build your first agent from scratch 33:11 - Master agent loops 1:02:46 - Turn loops into reliable workflows 1:30:15 - Build agents that improve their own work 1:49:05 - Run the complete system without supervision Model → Harness → Reliable Software Most agent tutorials stop once the model can call a tool This one shows you how to build the infrastructure around it within the first 20 minutes Most people are still prompting one agent at a time Andrew Ng is already teaching the layer above: Harnesses that give agents context, tools, tests, and feedback Watch this brilliant course and build the harness Then read the full architecture below ↓

Ichigo

100,458 Aufrufe • vor 23 Tagen

OpenClaw 10x Better With One Simple Change: 00:00 The difference between a generic assistant and your actual agent 00:42 What soul.md does inside OpenClaw 01:22 The files that make up your agent 02:22 How I created my first OpenClaw files 03:00 Why Pete’s soul.md changed everything 03:40 The mistake most people make with soul.md 05:02 Five things that actually matter 05:11 Specificity beats length 05:37 Hard limits are the most important section 06:32 Why user.md is the secret weapon 07:12 Let your agent improve its own soul.md 07:40 Keep it under 2,000 words 08:13 How Max has been running for 3 months 09:11 Why good files make OpenClaw feel like an employee

OpenClaw 10x Better With One Simple Change: 00:00 The difference between a generic assistant and your actual agent 00:42 What soul.md does inside OpenClaw 01:22 The files that make up your agent 02:22 How I created my first OpenClaw files 03:00 Why Pete’s soul.md changed everything 03:40 The mistake most people make with soul.md 05:02 Five things that actually matter 05:11 Specificity beats length 05:37 Hard limits are the most important section 06:32 Why user.md is the secret weapon 07:12 Let your agent improve its own soul.md 07:40 Keep it under 2,000 words 08:13 How Max has been running for 3 months 09:11 Why good files make OpenClaw feel like an employee

Sharbel

20,300 Aufrufe • vor 5 Monaten

How to start your Hermes AI Agent (step-by-step guide for beginners) I'm a beginner, and I spent the last 24h: > Understanding the process > Launching my Hermes Agent (Nous Research) > Recording the whole process > Editing this step-by-step guide So you can do it yourself and understand everything in less than 1 hour! 00:00 Intro 00:38 What is Hermes ? Key concepts every beginner should know 01:16 What is a VPS ? 02:00 What is SSH ? 03:52 What is the Terminal ? 04:03 What is an LLM provider ? 04:37 What is an API key ? 04:52 What is the Terminal Backend 05:34 What is the Messaging Gateway ? 06:10 What is the Memory System ? Step-by-step: Launch your Hermes AI agent 06:53 Step 1 : Get your VPS 08:04 How to generate an SSH key 09:00 Step 2 : Connect to your VPS 10:32 Step 3 : Install Hermes Agent 14:34 Connect your agent to Telegram 17:12 How to fix a mistake during the process 17:48 Gateway testing 18:17 Telegram is working 18:31 Run your agent 24/7 19:16 Thank you Make sure to save this for later and share it with friends who want to start with AI agents. Thanks to Teknium 🪽 for being so active!

How to start your Hermes AI Agent (step-by-step guide for beginners) I'm a beginner, and I spent the last 24h: > Understanding the process > Launching my Hermes Agent (Nous Research) > Recording the whole process > Editing this step-by-step guide So you can do it yourself and understand everything in less than 1 hour! 00:00 Intro 00:38 What is Hermes ? Key concepts every beginner should know 01:16 What is a VPS ? 02:00 What is SSH ? 03:52 What is the Terminal ? 04:03 What is an LLM provider ? 04:37 What is an API key ? 04:52 What is the Terminal Backend 05:34 What is the Messaging Gateway ? 06:10 What is the Memory System ? Step-by-step: Launch your Hermes AI agent 06:53 Step 1 : Get your VPS 08:04 How to generate an SSH key 09:00 Step 2 : Connect to your VPS 10:32 Step 3 : Install Hermes Agent 14:34 Connect your agent to Telegram 17:12 How to fix a mistake during the process 17:48 Gateway testing 18:17 Telegram is working 18:31 Run your agent 24/7 19:16 Thank you Make sure to save this for later and share it with friends who want to start with AI agents. Thanks to Teknium 🪽 for being so active!

Theo

34,057 Aufrufe • vor 6 Monaten

Free 3-hour course from an ex-Google engineer. AI agents, graph engineering, harness, agent loops. Covers more than most $500 paid courses. This is the exact playbook to learn how to actually build and monetize AI agents. Don't miss your shot, save it now. • 0:00 - Design an AI agent system from scratch • 7:38 - When your agent should hand off to a human • 19:27 - RAG and vector databases explained simply • 52:38 - Deploy your AI agent to Google Cloud • 1:25:57 - Turn AI agents into a paid business via WhatsApp • 1:33:00 - Turn conversations into real leads automatically • 2:24:01 - Loop engineering vs graphs, the real difference • 2:33:08 - Build your own multi-tool agent graph Save this video, because it's the only one that can actually take you to your $2,000+ profit.

Free 3-hour course from an ex-Google engineer. AI agents, graph engineering, harness, agent loops. Covers more than most $500 paid courses. This is the exact playbook to learn how to actually build and monetize AI agents. Don't miss your shot, save it now. • 0:00 - Design an AI agent system from scratch • 7:38 - When your agent should hand off to a human • 19:27 - RAG and vector databases explained simply • 52:38 - Deploy your AI agent to Google Cloud • 1:25:57 - Turn AI agents into a paid business via WhatsApp • 1:33:00 - Turn conversations into real leads automatically • 2:24:01 - Loop engineering vs graphs, the real difference • 2:33:08 - Build your own multi-tool agent graph Save this video, because it's the only one that can actually take you to your $2,000+ profit.

unicode

143,850 Aufrufe • vor 1 Monat