15
Your ‘Team of AI Agents’ in n8n Is Actually a Single Dumb Pipeline
r/aiagents
6/28/2025
SAAS TOOLS
| SaaS | URL | Category | Features/Notes |
|---|---|---|---|
| n8n | https://n8n.io | Workflow Automation | No-code workflow automation, API integrations, user triggers, GPT nodes, Tools nodes, If/Switch nodes, Memory node (with limitations), ReAct Agent node |
| Airtable | Not specified | Database/Spreadsheet | External memory storage for agent context |
| Supabase | https://supabase.io | Database | External memory storage for agent context |
| LangChain | https://langchain.com | AI Framework | Dedicated framework for multi-agent systems with shared state, coordination protocols |
| LangGraph | Not specified | AI Framework | Dedicated framework for multi-agent systems (implied from context) |
| Flowise | Not specified | AI Workflow Builder | Mentioned 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