Building AI-native workflows with confidence.
For Exploring
Discover pre-built, customizable agent templates curated by our global AI community. From task bots to Web3 protocol wrappers, start fast and stay focused.
No-Code Fast Deployment
Create visually design & deploy agent workflows using modular tools. Drag, chain, and simulate complex task hierarchies with logic, context, & memory baked in.
New to Agentic AI? Start here.
Like your README file of DoggyDish. Quickly get started building intelligent agents that can think, act, and adapt autonomously. Agentic AI learning starts here.
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Scale Your App
Empowering developers and innovators to design, deploy, and scale agent-based applications that think, act, and adapt on their own. Whether you’re building a single-agent assistant or orchestrating a multi-agent system for enterprise AI, our tools and insights are built to move with you—not against you. That’s what being unstuckable means: no rigid playbooks, no gatekeeping—just smart guidance and scalable foundations built to grow with your ambition.
Build agents with memory, tools, and context.
LangChain gives you powerful modules to build reasoning-capable agents that chain together prompts, APIs, and retrieval systems. LangChain is ideal for developers who:
Want granular control over each step Need to connect to databases, APIs, tools, and memory Plan to scale from simple to complex agentic logic
CrewAI makes it easy to define multiple agents with roles, goals, and tools—working in sync toward a common task. Think of it as teamwork for LLMs.
CrewAI is perfect for devs who:
Want to delegate tasks across specialized agents Prefer a declarative approach to agent behavior Value fast prototypes and real-time coordination
NVIDIA NeMo gives you a full-stack framework for building domain-specific LLMs and multi-agent systems with deep control over model training, inference, and memory optimization.
Need to custom-train or fine-tune foundation models (LLMs, ASR, TTS, etc.) Optimize agent inference across GPUs or multi-node environments Multimodal agents that understand and generate across text, voice, & vision
Adaptive Learning
Agentic AI can maintain memory and adapt over time, allowing it to improve its behavior based on past experiences—like learning from successes and failures.
Absolutely. We feature content from developers, researchers, and AI enthusiasts. If you're building something innovative in the agentic space—or have insights into scaling infrastructure—we’d love to hear from you.
Absolutely. We feature content from developers, researchers, and AI enthusiasts. If you're building something innovative in the agentic space—or have insights into scaling infrastructure—we’d love to hear from you.
Yes. Every application breakdown includes suggested infrastructure—ranging from single-node prototyping to rack-scale deployments. We often highlight configurations using NVIDIA GB200, Supermicro systems, and liquid cooling.
A typical LLM app (like a chatbot) responds to inputs but lacks memory or autonomous decision-making. An agentic system uses LLMs alongside reasoning loops, tool access, and long-term memory to complete multi-step tasks over time.
Yes, with attribution. Our mission is to spread agentic AI knowledge, so feel free to quote us with proper credit and link back to the original source.
We highlight technologies we believe in—especially those that enable scalable, efficient agentic AI. While we may collaborate with partners, and innovators all recommendations are grounded in performance, scalability, and relevance to agentic workflows.
Not at all. While we have technical deep-dives, we also have a visual, no-code Agent Builder for fast agentic app deployment. Good for quick deployments for all skill levels.
DoggyDish is where agentic AI meets real-world hardware.
Founders & product leaders – turning AI ideas into deployable products
Infra & data-center folks – figuring out how to host agentic workloads at scale
Developers / tinkerers – wanting practical how-to guides, not vague hype
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