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r/aiagents
2025-06-28·4
15

Your ‘Team of AI Agents’ in n8n Is Actually a Single Dumb Pipeline

SAAS TOOLS

SaaSURLCategoryFeatures/Notes
n8nhttps://n8n.ioWorkflow AutomationNo-code workflow automation, API integrations, user triggers, GPT nodes, Tools nodes, If/Switch nodes, Memory node (with limitations), ReAct Agent node
AirtableNot specifiedDatabase/SpreadsheetExternal memory storage for agent context
Supabasehttps://supabase.ioDatabaseExternal memory storage for agent context
LangChainhttps://langchain.comAI FrameworkDedicated framework for multi-agent systems with shared state, coordination protocols
LangGraphNot specifiedAI FrameworkDedicated framework for multi-agent systems (implied from context)
FlowiseNot specifiedAI Workflow BuilderMentioned in comments for research workflow with reasoning agents

USER NEEDS

Pain Points:

  • n8n lacks built-in shared memory for AI agents, requiring manual setup
  • Fixed execution flow limits dynamic agent coordination
  • Stateless runs prevent learning from past executions
  • Parallel branches require manual coordination and merging
  • True multi-agent features (debate, task delegation, learning) are absent
  • High cost and manual setup for basic agent workflows
  • Overuse of multiple agents when a single well-configured agent suffices

Problems to Solve:

  • Achieving true multi-agent collaboration (shared memory, dynamic coordination)
  • Enabling agents to learn from past executions
  • Reducing manual setup for agent coordination
  • Preventing duplicated/competing outputs in parallel agents
  • Lowering costs by avoiding unnecessary multi-agent setups

Potential Solutions:

  • Build custom shared memory layer using external databases (Airtable, Supabase)
  • Use dedicated multi-agent frameworks (LangChain, LangGraph) via n8n webhooks
  • Adopt proven design patterns: chained pipelines, monolithic agents, gatekeeper+specialists
  • Invest in data preparation (scraping, OCR, chunking, embedding) before agent setup
  • Use single well-configured agents instead of unnecessary multi-agent setups
  • Implement coordinator agents with If/Switch nodes and shared memory

GROWTH FACTORS

Effective Strategies:

  • Focusing on core strengths (data orchestration, API integrations) rather than forcing unsupported features
  • Integrating with specialized frameworks for advanced capabilities
  • Providing clear design patterns for common use cases

Marketing & Acquisition:

  • Highlighting reliable use cases (data preparation pipelines, single-agent setups)
  • Addressing limitations transparently to build trust

Monetization & Product:

  • Potential need for native multi-agent features (shared memory, coordination protocols)
  • Improving cost-effectiveness by avoiding over-engineering
  • Ensuring product-market fit for workflow automation vs. advanced AI

User Engagement:

  • Community discussions on architectural best practices
  • Mentorship programs for knowledge sharing (as mentioned by author)
  • Encouraging user hacks and workarounds for limitations
14

Gemini CLI: Google Just Put the Terminal in Play

SAAS TOOLS

SaaSURLCategoryFeatures/Notes
Gemini CLIhttps://github.com/google-gemini/gemini-cliAI Development ToolFree multimodal interface, 1M-token context window, live Search grounding, Imagen, Veo, Workspace hooks, 60 RPM, 1,000 queries/day
ClaudeNot providedAI Coding AssistantSmooth coding UX, costs up to $200/month, smaller context window
CursorNot providedAI Code EditorImplied competitor to Gemini CLI

USER NEEDS

Pain Points:

  • Cost of AI tools (Claude costs up to $200/month)
  • Limited context window in existing tools
  • Need for terminal-based AI interaction
  • File access issues during AI-assisted coding

Problems to Solve:

  • Automating agent building and code generation
  • Converting projects between languages (e.g. Ruby to JavaScript)
  • Debugging and fixing code issues
  • Integrating AI into development workflow

Potential Solutions:

  • Free tier with generous usage limits (Gemini CLI's model)
  • Terminal integration for developers
  • Larger context windows for better code understanding
  • Improved file system access for AI tools

GROWTH FACTORS

Effective Strategies:

  • Offering free tiers to drive adoption
  • Creating ecosystem lock-in through deep integration
  • Focusing on interface innovation

Marketing & Acquisition:

  • Free pricing model as acquisition driver
  • Targeting developer workflows through terminal integration

Monetization & Product:

  • Using free products to build user base before monetization
  • Competing on context window size and features
  • Product positioning as ecosystem tether rather than standalone tool

User Engagement:

  • Community building through open-source GitHub repository
  • Leveraging terminal familiarity for lower adoption barrier
11

Why are websites still hard to make in 2024? So we fixed it

SAAS TOOLS

SaaSURLCategoryFeatures/Notes
mysite AIhttps://www.producthunt.com/products/mysite-aiWebsite BuilderAI-powered website creation via chat, no setup, custom layouts, lead capture, non-generic copy and visuals
WixNot specifiedWebsite BuilderDrag-and-drop website builder (mentioned as existing solution)

USER NEEDS

Pain Points:

  • Building websites is still complicated despite existing tools
  • Existing tools break easily (e.g., after 10 prompts)
  • Desire for fast website creation without complexity
  • Frustration with generic-looking templates and outputs

Problems to Solve:

  • Simplify website creation process
  • Eliminate technical setup requirements
  • Create professional-looking websites quickly
  • Generate unique content and visuals that avoid generic templates

Potential Solutions:

  • AI-powered chat interface for website generation (mysite AI)
  • All-in-one solution handling social media, ads, leads, and content
  • Zero-setup instant deployment

GROWTH FACTORS

Effective Strategies:

  • Solving a perceived pain point in website creation
  • Positioning as an 'AI employee' for small businesses

Marketing & Acquisition:

  • Product Hunt launch for visibility
  • Direct promotion on Reddit (despite negative reception)
  • Offering free trial with no login/credit card

Monetization & Product:

  • Targeting small businesses and side projects
  • Expanding beyond websites to full business automation (socials, ads, etc.)
  • Secured €2.1M funding for scaling

User Engagement:

  • Soliciting user feedback directly in post
  • Attempting to engage critics by inviting them to try the product
6

Is realistic to earn $50k on 20 days ?

SAAS TOOLS

SaaSURLCategoryFeatures/Notes
MakeNot specifiedAutomationUsed for running AI agents with Google Sheets integration

USER NEEDS

Pain Points:

  • Uncertainty about realistic income potential from AI agents
  • Difficulty in implementing and selling AI agent solutions
  • Skepticism from potential clients about AI agents
  • Lack of clarity on business models (renting vs. creating agents)

Problems to Solve:

  • How to monetize AI agent creation effectively
  • How to scale income to $1,000/day or $50,000/month
  • How to overcome implementation barriers

Potential Solutions:

  • Building unique solutions with competitive moats
  • Iterative testing of revenue targets (e.g., $50k in 20 days)
  • Client segmentation and tiered pricing strategies
  • Focusing on simple, high-demand automation (e.g., email workflows)

GROWTH FACTORS

Effective Strategies:

  • Running iterative revenue experiments to test viability
  • Client acquisition through tiered pricing models

Marketing & Acquisition:

  • Not explicitly mentioned, but implied need for sales skills

Monetization & Product:

  • Pricing models: Tiered client pricing ($2.5k-$50k per client)
  • Product-market fit: Simple automation (e.g., email workflows) meets 60% market demand

User Engagement:

  • Community discussion for validation and idea sharing