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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Make.com
DoggyDish.com curates real-world agentic AI workflows designed for visual automation builders, helping users move from simple triggers to multi-step autonomous systems with minimal friction.
New Dev Friendly: Easy
Pros & Cons:
Pro: Visual, fast iteration
Con: Limited deep logic
Pricing Model: Freemium → usage tiers
Zapier
DoggyDish.com shows how to stretch Zapier beyond basic zaps into lightweight agentic patterns, focusing on decision-based automation and AI-assisted task routing.
New Dev Friendly: Easy
Pros & Cons:
Pro: Huge app ecosystem
Con: Cost scales fast
Pricing Model: Subscription, task-based
n8n
DoggyDish.com positions n8n as the backbone for serious agentic AI systems—covering memory, branching logic, tool calling, and self-hosted scalability.
New Dev Friendly: Medium
Pros & Cons:
Pro: Open, highly flexible
Con: Setup complexity
Pricing Model: Open-source + paid cloud
Relay App
DoggyDish.com showcases how Relay enables lightweight agentic automation by combining human-in-the-loop workflows with AI-powered task orchestration for modern teams.
New Dev Friendly: Easy
Pros & Cons:
Pro: Clean UI, fast setup
Con: Limited deep logic
Pricing Model: Freemium → per-user plans
Anti-Gravity
DoggyDish.com explores Antigravity as an experimental agent platform, focusing on autonomous reasoning loops and emerging multi-agent coordination patterns.
New Dev Friendly: Difficult
Pros & Cons:
Pro: Advanced agent logic
Con: Immature ecosystem
Pricing Model: Early-access / TBD
Stack
DoggyDish.com frames Stack as an infrastructure layer for agentic systems, emphasizing how composable services support scalable AI-driven applications.
New Dev Friendly: Medium
Pros & Cons:
Pro: Modular architecture
Con: Requires engineering mindset
Pricing Model: Usage-based
LangChain
DoggyDish.com uses LangChain as the canonical framework for building tool-using, memory-aware agents, bridging LLM reasoning with real-world execution.
New Dev Friendly: Medium
Pros & Cons:
Pro: Powerful abstractions
Con: Rapid API changes
Pricing Model: Open-source + services
NeMo
DoggyDish.com positions NeMo at the foundation layer—where agentic AI meets enterprise-grade model training, inference, and GPU-scale deployment.
New Dev Friendly: Difficult
Pros & Cons:
Pro: Enterprise-scale performance
Con: Hardware intensive
Pricing Model: Enterprise licensing
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.
