AgentSwarms

Theory: "Multi-agent systems will revolutionize enterprise workflows." Practice: "I just spent 4 hours fighting LangGraph circular dependency errors." 🫠 We need to stop teaching AI through messy setup code and start teaching it through system architecture. AgentSwarms is a visual sandbox where you learn to build AI swarms by actually building them. No Docker. Just logic. agentswarms.fyi

@_vmlops Good course but we need fast hands on as well! Check agentswarms.fyi

HUGGING FACE DROPPED A FREE CONTEXT ENGINEERING COURSE and the curriculum is stacked: ▫️ unit 1: agent skills + SKILL.md format ▫️ unit 2: MCP (model context protocol) ▫️ unit 3: plugins for tool distribution ▫️ unit 4: subagents + multi-agent workflows ▫️ unit 5: hooks to guard the agent lifecycle ▫️ bonus: build your own agent from scratch huggingface.co/learn/context-…

@e_opore Agentswarms.fyi is also free 🙂

Node.js is free Express.js is free NestJS is free FastAPI is free Django is free Flask is free Spring Boot is free Laravel is free Python is free JavaScript is free TypeScript is free Go is free Java is free PHP is free PostgreSQL is free MySQL is free MongoDB is free Redis is free Git is free GitHub is free GitHub Actions is free GitLab is free Docker is free Kubernetes is free NGINX is free Apache is free Postman is free Insomnia is free Swagger is free RabbitMQ is free Apache Kafka is free Terraform is free Ansible is free Jenkins is free Prometheus is free Grafana is free AWS is free tier GCP is free tier Azure is free tier VS Code is free ChatGPT is free Claude is free Gemini is free Perplexity is free We are in the AI era. What’s stopping you from becoming a backend engineer?

@suraj_sharma14 You are spot on and thats why we built the only free browser based agentic ai learning playground here: agentswarms.fyi

If I had to bet on one skillset for the next decade, it's this: > AI agents > MCP ecosystems > RAG pipelines > memory architectures > eval frameworks > multimodal applications > voice AI systems > coding agents > agent orchestration > AI infrastructure > synthetic data pipelines > model routing layers > browser automation > tool-calling systems > AI observability > human-in-the-loop workflows > inference optimization > vector databases > agent security layers > production AI products Coding is becoming cheaper. Knowing how to combine models, tools, data, memory & workflows into useful products is becoming more valuable.

@Tanaypawar27 Learning is incomplete without fast lab executions. We bridge the gap for free agentswarms.fyi

Stop wasting hours trying to learn AI. I have already done it for you. With one list. Zero confusion. And no fluff. 📹 Videos: 1. LLM Introduction: t.co/kyDon6qLrb 2. LLMs from Scratch: t.co/2hyMhuKoiI 3. Agentic AI Overview (Stanford): t.co/FXu6cAqITC 4. Building and Evaluating Agents: t.co/ZigR1tdOFL 5. Building Effective Agents: t.co/uYwfwO55mO 6. Building Agents with MCP: t.co/4arFTW1b3i 7. Building an Agent from Scratch: t.co/eOmveyM9Hz 8. Philo Agents: t.co/zLu7x1tx9m 🗂️ Repos 1. GenAI Agents: t.co/eXCl2YaRPv 2. Microsoft's AI Agents for Beginners: t.co/3CSW4zPAwf 3. Prompt Engineering Guide: t.co/GVzvxPYDVO 4. Hands-On Large Language Models: t.co/0rgDvhx3pI 5. AI Agents for Beginners: t.co/3CSW4zPAwf 6. GenAI Agents: lnkd.in/dEt72MEy 7. Made with ML: t.co/9z5KHF9DMe 8. Hands-On AI Engineering: t.co/dldAj5Xkr6 9. Awesome Generative AI Guide: t.co/U2WZhT4ERV 10. Designing Machine Learning Systems: t.co/sYAZX34YdQ 11. Machine Learning for Beginners from Microsoft: t.co/NjFxHbC9jZ 12. LLM Course: t.co/N34YTPu1OK 🗺️ Guides 1. Google's Agent Whitepaper: t.co/bW3Ov3vMW0 2. Google's Agent Companion: t.co/wredwWAbBA 3. Building Effective Agents by Anthropic: t.co/fxtE4alVrJ 4. Claude Code Best Agentic Coding practices: t.co/lLSwJ9pG7C 5. OpenAI's Practical Guide to Building Agents: t.co/xgkEIogGfh 📚 Books: 1. Understanding Deep Learning: t.co/CjcKpTemmV 2. Building an LLM from Scratch: t.co/DaWBxOx8o3 3. The LLM Engineering Handbook: t.co/ZA1n0N41Mf 4. AI Agents: The Definitive Guide - Nicole Koenigstein: t.co/boLkl1VlKb 5. Building Applications with AI Agents - Michael Albada: t.co/H1Xf5EkJLL 6. AI Agents with MCP - Kyle Stratis: t.co/JI3ELQZE6a 7. AI Engineering: t.co/Xk0JzMIf7o 📜 Papers 1. ReAct: t.co/QNqE4UU55w 2. Generative Agents: t.co/CwEpoJgY1U 3. Toolformer: t.co/5m9xZd5teZ 4. Chain-of-Thought Prompting: t.co/KjVlgdWi77 🧑🏫 Courses: 1. HuggingFace's Agent Course: t.co/7FSUYKxIdG 2. MCP with Anthropic: t.co/IkZGiWm2yS 3. Building Vector Databases with Pinecone: t.co/2YRoMfLdXd 4. Vector Databases from Embeddings to Apps: t.co/23A50ixbHJ 5. Agent Memory: t.co/uc3L9BrNF7 👇 Comment “A” for more resources Repost for your network ♻️ Bookmark for future.

@DivyanshT91162 And the best Agentic AI learning playground is .. us! agentswarms.fyi

THE BEST FREE AI ENGINEERING COURSE ON THE INTERNET MIGHT BE THIS GITHUB REPO. Not a random tutorial collection. A complete open-source roadmap that takes you from beginner to AI engineer: ✓ 435 lessons ✓ 320+ hours of content ✓ Python, TypeScript, Rust & more ✓ Prompt engineering, AI skills, agents & MCP servers ✓ Hands-on exercises for every lesson No paywalls. No certifications to upsell. Just a massive, practical curriculum built for people who want to actually build with AI. Bookmark this one. You'll be coming back to it for months. REPO👇

@AiCamila_ Want to learn how to build such agentic systems? Check us out! agentswarms.fyi

Most developers think Claude Code is an AI coding assistant. They’re wrong. Claude Code is secretly a 5-layer operating system for AI agents. And 90% of people never go beyond Layer 1. Here’s the architecture 👇 ━━━━━━━━━━━━━━━ 🧠 Layer 1 — Memory (CLAUDE.md) Your project’s brain. Stores: → coding standards → architecture decisions → workflows → repo conventions → team rules Persistent across sessions. This is what transforms Claude from “generic AI” into: “your engineering team’s AI.” ━━━━━━━━━━━━━━━ 📚 Layer 2 — Skills Reusable expertise modules. Need: → a React expert? → a security auditor? → a database architect? Claude dynamically loads the correct knowledge only when needed. Benefits: → cleaner context → lower token usage → specialized execution → fewer hallucinations This is where AI starts feeling agentic instead of conversational. ━━━━━━━━━━━━━━━ 🔒 Layer 3 — Hooks The layer most teams completely ignore. Hooks are programmable infrastructure triggers. Examples: → auto-run tests → block risky commands → enforce code quality → inject runtime context → send Slack alerts → auto-format outputs This is NOT AI reasoning. It’s deterministic reliability. Production-grade AI systems are built here. ━━━━━━━━━━━━━━━ 🤖 Layer 4 — Subagents This is where Claude Code becomes a true multi-agent system. Delegate tasks like a real engineering org: → one agent writes code → one reviews PRs → one writes tests → one investigates bugs Parallel execution. Isolated context. Separate tools. No context pollution. No recursive chaos. You stop thinking: “One assistant” And start thinking: “Distributed cognitive workers.” ━━━━━━━━━━━━━━━ 📦 Layer 5 — Plugins The distribution layer. Package: → skills → hooks → commands → agents → workflows …into one reusable install. Install once. Share across teams. Reuse everywhere. This is how organizations operationalize AI engineering at scale. ━━━━━━━━━━━━━━━ The biggest misconception in AI right now: People think the magic is prompting. It’s not. The real leverage comes from: → architecture → orchestration → memory systems → deterministic workflows → agent coordination Most people are chatting with Claude. A few are building autonomous software teams inside it. That’s the real shift happening right now. Bookmark this if you’re serious about AI engineering. 🔖 #ClaudeCode #AIAgents #AgenticAI #AIEngineering #SoftwareDevelopment #LLM

@mdancho84 You get everything consolidated, interactively explained along with a fully featured browser based lab here : agentswarms.fyi

OpenAI, Google, and Anthropic just published guides on: • Prompt engineering • Building agents • AI in business • 601 AI use cases 9 of the best guides you can't miss:

@shushant_l For complete hands on learning of Agentic AI, check agentswarms.fyi - we are the only free tool offering fully featured sandbox to try out all the concepts

Most people are building AI agents completely wrong. The architecture is the real secret nobody talks about. Here's the AI agent architecture used to build agents that actually work. 📂 AI Agents Architecture ┃ ┣ 📂 What Is An AI Agent? ┃ ┣ 📂 Goal Driven System ┃ ┣ 📂 Reasoning Engine ┃ ┣ 📂 Task Planning ┃ ┣ 📂 Tool Usage ┃ ┣ 📂 Memory Access ┃ ┣ 📂 Action Execution ┃ ┗ 📂 Feedback Learning ┃ ┣ 📂 Input Layer ┃ ┣ 📂 Text Inputs ┃ ┣ 📂 Voice Inputs ┃ ┣ 📂 Image Inputs ┃ ┣ 📂 File Inputs ┃ ┣ 📂 API Data ┃ ┗ 📂 User Requests ┃ ┣ 📂 Reasoning Layer ┃ ┣ 📂 Understanding Goals ┃ ┣ 📂 Decision Making ┃ ┣ 📂 Problem Solving ┃ ┣ 📂 Analysis ┃ ┣ 📂 Planning Logic ┃ ┗ 📂 Next Step Selection ┃ ┣ 📂 Planning Layer ┃ ┣ 📂 Goal Breakdown ┃ ┣ 📂 Task Creation ┃ ┣ 📂 Workflow Design ┃ ┣ 📂 Prioritization ┃ ┣ 📂 Dependency Mapping ┃ ┗ 📂 Execution Strategy ┃ ┣ 📂 Memory Layer ┃ ┣ 📂 Working Memory ┃ ┣ 📂 Episodic Memory ┃ ┣ 📂 Semantic Memory ┃ ┣ 📂 Long Term Memory ┃ ┣ 📂 User Preferences ┃ ┗ 📂 RAG Knowledge Retrieval ┃ ┣ 📂 Tool Layer ┃ ┣ 📂 Search Engines ┃ ┣ 📂 Knowledge Bases ┃ ┣ 📂 CRM Systems ┃ ┣ 📂 Calendars ┃ ┣ 📂 Email Platforms ┃ ┣ 📂 APIs ┃ ┗ 📂 Databases ┃ ┣ 📂 Action Layer ┃ ┣ 📂 Execute Tasks ┃ ┣ 📂 Send Emails ┃ ┣ 📂 Update Records ┃ ┣ 📂 Generate Content ┃ ┣ 📂 Run Workflows ┃ ┗ 📂 Trigger Automations ┃ ┣ 📂 Observation Layer ┃ ┣ 📂 Monitor Results ┃ ┣ 📂 Capture Feedback ┃ ┣ 📂 Detect Errors ┃ ┣ 📂 Evaluate Outcomes ┃ ┣ 📂 Measure Success ┃ ┗ 📂 Collect Signals ┃ ┣ 📂 Reflection Layer ┃ ┣ 📂 Analyze Performance ┃ ┣ 📂 Learn From Failures ┃ ┣ 📂 Improve Strategy ┃ ┣ 📂 Refine Plans ┃ ┣ 📂 Optimize Decisions ┃ ┗ 📂 Retry Actions ┃ ┣ 📂 Agent Control Loop ┃ ┣ 📂 Observe ┃ ┣ 📂 Think ┃ ┣ 📂 Plan ┃ ┣ 📂 Act ┃ ┣ 📂 Check Result ┃ ┣ 📂 Reflect ┃ ┗ 📂 Repeat ┃ ┣ 📂 Single Agent Architecture ┃ ┣ 📂 Research Agent ┃ ┣ 📂 Writing Agent ┃ ┣ 📂 Coding Agent ┃ ┣ 📂 Assistant Agent ┃ ┗ 📂 Productivity Agent ┃ ┣ 📂 Multi Agent Architecture ┃ ┣ 📂 Master Agent ┃ ┣ 📂 Research Agent ┃ ┣ 📂 Analysis Agent ┃ ┣ 📂 Writer Agent ┃ ┣ 📂 Reviewer Agent ┃ ┗ 📂 Security Agent ┃ ┣ 📂 Enterprise Agent Stack ┃ ┣ 📂 Applications ┃ ┣ 📂 Orchestration Layer ┃ ┣ 📂 Intelligence Layer ┃ ┣ 📂 Memory Layer ┃ ┣ 📂 Tools Layer ┃ ┗ 📂 Infrastructure Layer ┃ ┣ 📂 Popular Agent Patterns ┃ ┣ 📂 ReAct Pattern ┃ ┣ 📂 RAG Agent ┃ ┣ 📂 Reflection Agent ┃ ┣ 📂 Planner Agent ┃ ┣ 📂 Hierarchical Agent ┃ ┗ 📂 Manager Worker System ┃ ┣ 📂 Safety Layer ┃ ┣ 📂 Input Validation ┃ ┣ 📂 Output Filtering ┃ ┣ 📂 Permission Controls ┃ ┣ 📂 Human Approval ┃ ┣ 📂 Security Checks ┃ ┗ 📂 Validation Loops ┃ ┗ 📂 Final Agent Formula ┣ 📂 Input ┣ 📂 Reason ┣ 📂 Plan ┣ 📂 Memory ┣ 📂 Tools ┣ 📂 Action ┣ 📂 Observe ┣ 📂 Reflect ┗ 📂 Output

@LearnWithBrij For hands on agentic AI learning, we have agentswarms.fyi - completely free tool with 0 setup required to run multi agent systems and visually see the flows!

🤖 Most people learn prompts. Very few learn AI Engineering. Here's the complete AI Engineering Master Tree 🌲 📂 AI Engineering ┃ ┣ 📂 Foundations ┃ ┣ 📂 Python ┃ ┣ 📂 APIs ┃ ┣ 📂 JSON ┃ ┣ 📂 Git ┃ ┣ 📂 Linux ┃ ┗ 📂 Cloud Basics ┃ ┣ 📂 LLM Fundamentals ┃ ┣ 📂 Tokens ┃ ┣ 📂 Context Windows ┃ ┣ 📂 Embeddings ┃ ┣ 📂 Attention ┃ ┣ 📂 Fine-Tuning ┃ ┗ 📂 Inference ┃ ┣ 📂 Prompt Engineering ┃ ┣ 📂 Zero-Shot ┃ ┣ 📂 Few-Shot ┃ ┣ 📂 Chain of Thought ┃ ┣ 📂 Structured Outputs ┃ ┣ 📂 System Prompts ┃ ┗ 📂 Prompt Chaining ┃ ┣ 📂 RAG ┃ ┣ 📂 Chunking ┃ ┣ 📂 Embeddings ┃ ┣ 📂 Vector Databases ┃ ┣ 📂 Retrieval ┃ ┣ 📂 Re-ranking ┃ ┗ 📂 Citations ┃ ┣ 📂 AI Agents ┃ ┣ 📂 Tool Calling ┃ ┣ 📂 Memory ┃ ┣ 📂 Planning ┃ ┣ 📂 Multi-Agent Systems ┃ ┣ 📂 Reflection ┃ ┗ 📂 Agent Workflows ┃ ┣ 📂 Model Providers ┃ ┣ 📂 OpenAI ┃ ┣ 📂 Anthropic ┃ ┣ 📂 Google ┃ ┣ 📂 Meta ┃ ┣ 📂 Mistral ┃ ┗ 📂 DeepSeek ┃ ┣ 📂 Frameworks ┃ ┣ 📂 LangChain ┃ ┣ 📂 LlamaIndex ┃ ┣ 📂 CrewAI ┃ ┣ 📂 AutoGen ┃ ┣ 📂 Haystack ┃ ┗ 📂 PydanticAI ┃ ┣ 📂 Databases ┃ ┣ 📂 PostgreSQL ┃ ┣ 📂 Redis ┃ ┣ 📂 Pinecone ┃ ┣ 📂 Weaviate ┃ ┣ 📂 Qdrant ┃ ┗ 📂 Chroma ┃ ┣ 📂 Deployment ┃ ┣ 📂 Docker ┃ ┣ 📂 Kubernetes ┃ ┣ 📂 Serverless ┃ ┣ 📂 CI/CD ┃ ┣ 📂 Monitoring ┃ ┗ 📂 Scaling ┃ ┣ 📂 Evaluation ┃ ┣ 📂 Hallucinations ┃ ┣ 📂 Benchmarks ┃ ┣ 📂 Tracing ┃ ┣ 📂 Human Feedback ┃ ┣ 📂 Observability ┃ ┗ 📂 Guardrails ┃ ┣ 📂 Production AI ┃ ┣ 📂 Security ┃ ┣ 📂 Cost Optimization ┃ ┣ 📂 Caching ┃ ┣ 📂 Rate Limits ┃ ┣ 📂 Governance ┃ ┗ 📂 Reliability ┃ ┗ 📂 Mastery ┣ 📂 AI Chatbots ┣ 📂 AI Search Engines ┣ 📂 AI Copilots ┣ 📂 Voice Agents ┣ 📂 Autonomous Agents ┗ 📂 AI Products Learn these layers and you'll be ahead of 90% of AI developers. Save this roadmap for later 🌲

@ezra_muinde_ we've been saying this - that's why we built AgentSwarms.fyi as a visual sandbox where you can actually see the system dynamics play out in real time

Another most important trend about Agentic AI is Multi-agent systems, they mirror how high-performing human teams work: specialists collaborating under coordination. The agent is not the unit of analysis. The system is.

@Kcodess congrats! curious what they asked you in the interview? We have a full set of interview guidelines for Agentic AI available for free at AgentSwarms!

GOT HIRED AS AN AGENTIC AI ENGINEER INTERN Zero prep, Last-minute call, 4 people waiting on the other side ( i was unaware of this fact ) Here's the full story 🧵

@Raullen we've been testing it with building multi-agent workflows and that 4x drop in passing flawed code is huge when you're running parallel tasks

For Claude Opus 4.8, overall community sentiment is solidly positive: it's a modest but tangible improvement over 4.7. Not a revolutionary leap, but a meaningful step forward in reliability and real-world agentic performance. Developers especially appreciate the more "engineer-like" behavior. Praise: - SWE-Bench Pro: 69.2% (up from 64.3%, +4.9pts) • Agentic Terminal Coding: 74.6% (up from 66.1%, +8.5pts) — biggest jump - Honesty & reliability. The rate of passing flawed code without flagging issues dropped ~4x. Opus 4.8 is significantly better at admitting uncertainty, reducing hallucinations, and avoiding overconfident mistakes. Feels like a much more trustworthy collaborator on long-running tasks. - Adaptive Thinking + Effort Control (Max/High/Medium/Low) — dynamically scales reasoning depth Dynamic Workflows: supports hundreds of parallel sub-agents for large-scale code migration/refactoring Criticism exists too: some feel the jump isn't big enough compared to expectations, and a few users note it became slightly more cautious/sycophantic in creative or simple tasks. Bottom line: If you use Claude heavily for coding, agentic workflows, or long-horizon projects, 4.8 is worth switching to — the reliability gains are noticeable in daily use. For casual or creative work, the difference is more subtle.

@KingWilliamDefi we're seeing this play out in real time with AgentSwarms people build entire workflows solo that used to need 3-4 devs

Y Combinator just taught a Startup School lecture on building AI-native companies. Diana Hu, YC Partner, said the quiet part out loud: AI isn't making teams more productive. it's replacing the need for teams entirely. 229,000 people watched this. most of them are still hiring. > the old model: hire 3-5 people. pay $8,000-15,000/month in overhead. manage them. hope the output is consistent. > the new model: one person. one AI. one system. same output. 95% margins. > she's not talking about cutting corners. she's talking about a structural change in how companies get built. the article below is the proof. someone built a $5,000/month content agency with one client, no team, and 6 hours of work per week. their only tools: Claude, a scheduling app, and a Google Doc. that's not a freelancer. that's a company with $60,000/year revenue and zero employees. the lecture is 10 minutes. the article is the playbook. full breakdown below

@HighyieldHarry we see this playing out in real time the infra/tooling layer is where it gets messy - tons of companies building similar wrappers with unclear moats

"The AI bubble" isn't about whether OpenAI or Anthropic is a bubble. It's about whether a bunch of Series A-B companies are bubbles.

@DeRonin_ damn that's wild, congrats what kind of agentic workflows are people paying for?

i made $57,273 in may 2026… ⚒️ distribution to ai products — $40k ✍️ running agentic workflows — $7k 💵 X monetization — $3.4k (net) 🪪 paid campaigns — $6.8k It’s been my record for the last time, idk how it happened, since my main products are not even released, but it is

We used to nod along when people said "just spin up a local environment" was no big deal. We were wrong. They were wrong. Setup friction kills more learning than bad content ever does.

we've been using agentic coding daily for months now and honestly? we're watching our own skills atrophy in real-time. the productivity gains are undeniable but there's this growing tension: the agents handle more, we code less, and the muscle memory just... fades. we're actively forcing ourselves to stay sharp but it's getting harder to justify when the agent does it faster

we're seeing AI outpace human coders and mathematicians in real-time. soon it'll exceed us at practically every measurable task. the question isn't if this happens-it's whether we're building the right infrastructure and mindsets now. we need systems that let people work *with* these capabilities, not get displaced by them. that's why we're focused on making agentic AI accessible. the transition will be smoother if more people understand how these systems actually work.

we've been watching this play out in real-time with agentic systems - the models that win aren't the ones we hardcode with domain knowledge-they're the ones we give tools to and let them figure it out search, learning, iteration. that's what scales. human priors don't.

@hwchase17 We have included similar ready to run examples in AgentSwarms.fyi/templates to quickly see what happens at each step visually! The templstes can be exported and run in AWS Bedrock Agentcore!

🧑‍⚖️Evaluating Deep Agents with LangSmith on AWS Great deep dive blog with our friends at AWS on evaluating DeepAgents with LangSmith Covers datapoint and evaluator design for longer horizon agents aws.amazon.com/blogs/machine-…