I've been building automation workflows for years. When AI agents became the hot new thing, I did what everyone else did: I opened n8n, dragged some nodes around, and tried to build a 'smart' workflow. Then I hit the wall.
n8n is fantastic for IFTTT-style automations, but it's too rigid for autonomous agents. You can't have an agent decide its next step based on context that wasn't pre-defined in a JSON schema. It's like trying to teach improvisation using a coloring book.
So I tried LangChain. It's powerful, yes, but it's also heavy. On my Raspberry Pi 4, importing LangChain took 8 seconds. Running a simple agent consumed 2GB of RAM. I wanted something that could run on a $35 computer, not a cloud instance costing $200/month.
I needed a third way. Something between 'rigid workflow' and 'heavy framework.'
The Problem: We Need Personal Agents That Respect Privacy
Cloud-based agents are convenient until you realize you're sending your calendar, emails, and personal notes to someone else's API. Even with 'privacy policies,' you're still creating a single point of failure for your digital life.
I wanted agents that could:
Run entirely on local hardware (Raspberry Pi 4 or better)
Orchestrate multiple specialized agents without choking the CPU
Execute real tools (Telegram, file system, APIs) without cloud dependencies
Maintain shared memory across sessions without a SaaS subscription
Most importantly, I wanted them to feel like collaborators, not scripts. If I tell my agent 'remind me about the meeting tomorrow,' I don't want to pre-define what 'remind' means. The agent should figure out the best channel (Telegram? Email? Calendar block?) based on context.
The Solution: Introducing 9Lives
9Lives is a lightweight runtime for personal AI agents. It's not a framework you import; it's a runtime that orchestrates. Think of it as systemd for AI agents—small, fast, and designed for local-first operation.
But wait.. take a look with your Eye https://github.com/flaz78/9Lives
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