I will fix build jarvis ai assistant openclaw hermes agent obsidian mcp vps grokbot

I configure agents with defined roles, goals and backstories using CrewAI's sequential or hierarchical process. For most builds I default to hierarchical so a manager agent delegates tasks to specialized worker agents, giving you structured, auditable execution with minimal hallucination risk.

Lindy AI uses LLM reasoning to make contextual decisions mid-workflow, it doesn't just trigger actions, it understands intent. n8n handles deterministic logic and API routing. I combine both so Lindy handles the intelligence layer while n8n manages the execution and data pipeline layer underneath.

Yes. I expose CrewAI agents as API endpoints or webhooks that n8n and Make com can trigger natively. This means your existing workflow automation stack stays intact while CrewAI handles the agentic reasoning layer, no full rebuild required.

I configure different models per agent role using OpenAI, Anthropic Claude, Google Gemini or Mistral via OpenRouter. Research agents typically run on more capable models while execution agents run on faster cheaper models, balancing performance and token cost across the full agentic ai pipeline.

I build RAG pipelines using LangChain with vector databases like Pinecone or Chroma. The retrieval tool is assigned to the relevant CrewAI agent so it queries your knowledge base before responding. For Lindy AI I connect document stores via API integration so agents answer from your actual business

Yes. I build voice agents that can listen, understand speech, and reply in a natural voice, useful for support calls, bookings, or voice assistants on your website or app.