aeroreyna
90 lines
3.9 KiB
Markdown
90 lines
3.9 KiB
Markdown
---
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Date: 2026-07-28
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Author: Adolfo Reyna + Hermes
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Tags: [paseo, centralized-agents, orchestration, home-infra, project]
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---
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# Paseo — Centralized Agents Across Home Fleet
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## Goal
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Centralized control plane for AI agents across all home computers, where Hermes (this agent) can orchestrate tasks to the best machine.
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## Fleet (from 2026-07-13 inventory)
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- **FamReynaServer .110** - 7.6Gi, Docker 17 containers, Caddy v2.10.2, main host, candidate for paseo-server
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- **iMac .124** - 15Gi healthy, Node 22, Chrome reyna-bot :9222, BareBrowse/Default for heavy/browser tasks
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- **Mac-mini-M4 .102** - macOS, MCP server 7331, Deco API, image gen, high traffic
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- **aeropi5 Pi .126** - 3.9Gi 3.3Gi swap thrashing, Hermes dashboard :9119, cron orchestrator, should stay light
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- **emiserver .119** - EMI API, SSH locked, ports 3000/3001/9000/2283 open
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## Core Insight from User
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> "paseo is the solution for centralized agents across my computers at home. Not only that, but also provides a way for you to help me orchestrate"
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So paseo must:
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1. Be the fabric where agents register from each machine
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2. Let Hermes (me) dispatch/orchestrate work via tools
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## Architecture
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### Components
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```
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[paseo-server .110:7001] <- Caddy paseo.reynafamily.com
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FastAPI + SQLite + Bearer auth
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Tables: agents, tasks, heartbeats
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Endpoints: /agents/register, /agents/heartbeat, /agents/list, /tasks/create, /tasks/claim, /tasks/update, /tasks/list
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[paseo-agent daemon] on each host (.110, .124, .102, .126)
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Python, lightweight (~20MB RAM)
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On start: detect capabilities -> register
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Loop: heartbeat 15s + poll for tasks
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Capabilities: { ram_gb, cpu, chrome_debug_port, mcp_tools[], gpu, python, node, docker, voicebox }
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[Hermes skill: paseo]
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CLI: `paseo agents`, `paseo tasks`, `paseo dispatch --type chrome --payload ...`
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MCP-ish: list agents, create task assigned to best-fit, watch logs
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Used by me to route work: e.g., BareBrowse -> iMac .124, image gen -> Mac mini .102, light cron -> Pi .126
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```
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### Capability-based routing (MVP)
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- `browser/chrome` -> .124 (15Gi, :9222)
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- `image/codex/gemini` -> .102 (Mac mini M4)
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- `heavy-llm/ollama` -> .110 (Ollama host) or .124
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- `light/cron` -> .126 (Pi)
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- `docker/build` -> .110
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- `emi-api` -> .119
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### Data Model (SQLite MVP)
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- agents: id TEXT PK, hostname TEXT, ip TEXT, capabilities JSON, status TEXT, last_heartbeat ISO, created_at
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- tasks: id TEXT PK, type TEXT, payload JSON, assigned_agent TEXT FK, status TEXT (pending/claimed/running/done/failed), created_by TEXT, result JSON, created_at, updated_at
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### Security
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- Bearer token from env PASEO_TOKEN (stored in ~/.config/paseo-token 0600)
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- Caddy forward_auth via Authentik optional, but start with LAN-only + bearer
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- No public write without token
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### Hermes Orchestration Flow
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1. User asks Hermes to do coding task requiring Chrome
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2. Hermes calls `paseo dispatch`: creates task type=browser payload
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3. paseo-server matches capability -> assigns to iMac .124 agent
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4. iMac agent claims, runs BareBrowse flow, updates result
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5. Hermes polls task, returns result to user
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### Phases
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- Phase 0: Repo scaffold + FastAPI server + agent + Hermes skill (this week)
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- Phase 1: Deploy server on .110 via docker-compose or systemd, Caddy route paseo.reynafamily.com :7001
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- Phase 2: Install agents on .124, .102, .126 via systemd user services
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- Phase 3: Hermes skill integration: `delegate_task` wrapper that uses paseo to route
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- Phase 4: Dashboard UI + logs streaming + kill/restart
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- Phase 5: Extend to ESP32 fleet as executable endpoints (display, etc)
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## Decisions Made
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- Stack: Python FastAPI + SQLite (fastest to build, familiar, low RAM, same as Hermes scripts)
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- Server host: .110 FamReynaServer (central, Caddy already, Docker)
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- Agent: single python file daemon, no deps beyond requests + psutil
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- Token: shared via ~/.config/paseo-token
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## Next
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- Scaffold ~/Projects/paseo
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- Implement server MVP and agent MVP
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- Create ~/.hermes/skills/paseo skill for Hermes use
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- Test locally on Pi .126 first, then deploy to .110
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