I will n8n openclaw jarvis ai hermes ai multi ai agent obsidian mcp ollama claude code


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À propos de ce service
Your AI agent shouldn't live on someone else's server & it shouldn't forget everything the moment you close the terminal.
I install, secure & connect OpenClaw, Hermes, Jarvis-style assistants, Claude Code & local LLMs so they actually run with memory, with integrations & without the config errors that leave most setups half-working.
SERVICES I OFFER
- OpenClaw & Hermes agent install, setup & security on your VPS or local machine
- Claude Code and Claude Cowork setup, config & error fixing
- Obsidian second brain: vault structure, persistent memory, capture workflow
- Custom MCP servers connecting your agent to files, databases or apps
- Multi agent systems where agents hand off tasks to each other
- Ollama & local LLMs private, self-hosted, nothing leaves your machine
- n8n & workflow automation across your tools
- Jarvis-style personal assistant builds for daily tasks
- VPS server setup & hardening for self-hosted AI
- CRM & integration connections (HubSpot, Zoho, GoHighLevel)
TECH STACK:
- OpenClaw
- Hermes
- Claude Code
- Claude Cowork
- Ollama
- MCP Servers
- Obsidian
- n8n
- Make.com
- Zapier
- CrewAI
- LangChain
- Vapi
- Retell
- Docker
- HubSpot
- Zoho
- GoHighLevel
- Python
- Bash
CONTACT ME TO GET STARTED.
Découvrez Ethan Innocent
AI Bot Builder and Automation Expert, n8n, CrewAI, OpenClaw, MakeCom, Zapier
- DeÉtats-Unis
- Membre depuismars 2026
- Temps de réponse moy.1 heure
- Dernière commande1 semaine
Langues
Français, Anglais, Espagnol, Allemand, Italien, Portugais, Norvégien
Mon portfolio
FAQ
Do you support Claude Code Agent Teams, or only subagents?
Both. Subagents for quick, focused tasks that report back; Agent Teams (currently experimental) when workers need to communicate directly, like a frontend/backend/test split.
How do you handle credentials and permissions inside Claude Cowork?
Cowork only reads/writes folders and connectors you've explicitly approved. I configure permission settings so it shows its plan and waits for approval before anything significant runs.
What's your approach to controlling token spend across agents?
I tier models by task, a stronger model for orchestration, lighter models for repetitive worker tasks, and set hard step/spend caps per agent so a stuck loop can't drain your budget.
How do you scope permissions so autonomous ai agents stay safe?
Every agent gets scoped tool access and spend/step limits up front. Anything with real-world consequences requires human approval before it executes, no agent acts outside its defined boundaries.
How does the Notion AI dashboard fit into the system you build?
It's your visibility layer, a Notion AI dashboard showing what each agent is doing, task status, and outputs, so you're not digging through logs to see what ran.

