meshai/work

MeshAI

An LLM-powered assistant for LoRa mesh networks — on Meshtastic and MeshCore, at the same time.

MeshAI connects to your mesh, watches network health and the world around it in real time, answers questions over the air, and broadcasts the alerts that matter — weather, wildfire, road, seismic, RF, and mesh-health — with a full web dashboard to drive it all.

🤖 Built with AI ("vibecoded")

In the interest of transparency: MeshAI was vibecoded — designed, built, debugged, and documented in close collaboration with LLM coding assistants. The architecture, most of the implementation, and this README were produced that way. It's a real, running project on a live mesh, but expect the pragmatic style, opinionated shortcuts, and occasional rough edges that come with the territory. Issues and PRs are welcome.

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Highlights

• Dual-transport — runs on Meshtastic and MeshCore simultaneously. Each mesh is first-class: independent connection, routing, and behavior, one shared brain.
• Conversational bot — DM it "how's the mesh?" or ask about weather, fires, roads, or a specific node, and get a data-driven answer over LoRa. The reply goes back on whichever mesh you asked from.
• Per-mesh awareness — it watches chat on each mesh separately (rolling short-term memory), so "what's happening on the mesh?" answers about your mesh. Private DMs stay private; curated knowledge stays separate.
• Broadcast intelligence — weather alerts, wildfire updates, road/traffic, seismic, RF/band conditions, and mesh-health notifications, formatted to fit LoRa and routed per-mesh, per-family.
• Mesh health — a 5-pillar health score with per-region breakdowns, infrastructure monitoring, coverage-gap analysis, and battery/solar tracking (Meshtastic).
• Web dashboard — a clean React UI to configure every transport, route every message type, watch a live activity feed, browse contacts, and tune the bot — no config-file spelunking required.
• Knowledge base (RAG) — optional hybrid retrieval over a large curated vector store for survival/comms/technical Q&A.
• Multi-backend LLM — Google Gemini, OpenAI, Anthropic, or any OpenAI-compatible local model (Ollama, LiteLLM, etc.).

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The dashboard

Everything is driven from the web UI, organized into General, Meshtastic, and MeshCore sections — each mesh mirrors the other so there's nothing to relearn when you add the second transport.

Live activity log — every broadcast, on both meshes, with per-mesh badges and Sent/Skip status:

Per-family routing — decide exactly where each message type goes: broadcast vs. DM, which channel, which recipients — independently for each mesh:

MeshCore contacts & companion — the live roster from your MeshCore companion node, with names, types, last-heard, position, and optional telemetry polling:

Data feeds — turn environmental sources on/off and tune thresholds in one place:

Nodes & health — per-node infrastructure detail: battery, utilization, coverage, neighbors, hardware:

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Quick start

git clone https://github.com/zvx-echo6/meshai.git
cd meshai
pip install -e .
meshai --config     # interactive setup TUI
meshai

Or with Docker:

mkdir -p meshai/data && cd meshai
curl -O https://raw.githubusercontent.com/zvx-echo6/meshai/main/docker-compose.yml
curl -o data/config.yaml https://raw.githubusercontent.com/zvx-echo6/meshai/main/config.example.yaml
# edit data/config.yaml, then:
docker compose up -d

The dashboard comes up on http://localhost:8080.

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Transports

MeshAI speaks two mesh protocols. Meshtastic is always the base transport. MeshCore turns on automatically the moment you set a MeshCore host — there's no separate on/off toggle to forget.

Meshtastic

Connect over TCP (recommended) or serial:

connection:
  type: "tcp"           # or "serial"
  tcp_host: "192.168.1.100"
  tcp_port: 4403
  # serial_port: "/dev/ttyUSB0"

MeshCore

MeshAI attaches to a MeshCore companion (the pyMC / MeshCore companion frame server) over TCP and acts as a node on the MeshCore mesh:

connection:
  meshcore_host: "192.168.1.253"   # blank = MeshCore off
  meshcore_port: 5050

Once connected, MeshCore gets its own Connection, Routing, Scheduled Broadcasts, Contacts & Companion, and Danger Zones pages in the dashboard — the same capabilities as Meshtastic, using MeshCore's own idioms (channels by name, contacts by pubkey). Messages are sized to fit whichever mesh they go out on.

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The conversational bot

DM MeshAI on either mesh and it answers with the LLM, using live mesh data, environmental feeds, and (optionally) a knowledge base. A few things it's careful about:

• Answers on the mesh you asked from. A MeshCore DM gets a MeshCore reply; a Meshtastic DM gets a Meshtastic reply. Each mesh's "answer DMs" switch is independent.
• Per-mesh chat memory. It keeps a short rolling window of recent channel chatter per mesh (configurable retention, default 14 days) so "what's happening on the mesh?" reflects the mesh you're on. Ask about the other mesh by name to cross over.
• Three separate lanes. Shared channel context, your private DM history, and the curated knowledge base never bleed into each other.
• LoRa-fit replies. Responses are chunked to a per-mesh character budget with sentence-aware splitting and continuation prompts.

Commands

Alongside natural-language questions, a set of ! commands are available (all toggleable, so they can defer to another service like MeshMonitor):

Category	Commands
Mesh	!health · !mesh · !status · !region [name] · !neighbors [node]
Weather / RF	!wx-alerts · !solar · !hf · !satpass
Fire	!fire · !hotspots · !ignitions
Hazards	!avalanche · !roads / !traffic · !rivers / !gauges
Utility	!help · !clear

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Mesh intelligence (Meshtastic)

MeshAI continuously aggregates mesh data and computes a 5-pillar health score:

Pillar	Weight	Measures
Infrastructure	30%	Router/repeater uptime
Utilization	25%	Channel busyness / RF congestion
Coverage	20%	How many monitoring sources see each node
Behavior	15%	Traffic patterns (noisy/misconfigured nodes)
Power	10%	Battery health of infrastructure nodes

Infrastructure nodes are tracked individually (battery, offline alerts, coverage, neighbors, hardware); client nodes coming and going is normal and ignored. Regions are fully configurable — local names, aliases, cities, and radius — with no hardcoded geography.

Data comes from one or more Meshview instances and a MeshMonitor instance, polled on a staggered schedule with built-in rate-limiting:

mesh_sources:
  - name: "meshview"
    type: meshview
    url: "http://192.168.1.100:8080"
    enabled: true
  - name: "meshmonitor"
    type: meshmonitor
    url: "http://192.168.1.100:3333"
    api_token: "your-bearer-token"
    enabled: true

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Environmental & hazard feeds

MeshAI pulls real-time situational data and turns it into LoRa broadcasts and query answers. Sources include NWS weather alerts, NIFC wildfire perimeters, NASA FIRMS satellite fire detections, USGS earthquakes, USGS stream gauges, road/traffic (511 / TomTom), NOAA space weather, and avalanche/RF-propagation feeds.

Everything is switched on/off and tuned from the dashboard's Data Feeds page — enable a source, set thresholds and geography, and route its output per-mesh on the Routing page. Broadcast wording is tightened to fit a single LoRa packet without dropping the important details (e.g. affected towns on a weather alert).

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Knowledge base (RAG)

Optional hybrid retrieval for survival, comms, medical, and technical Q&A.

• Primary — queries a Qdrant hybrid store (dense bge-m3 + sparse, Reciprocal Rank Fusion) over a large curated vector set, via a networked TEI embedding service. Nothing is copied locally.
• Fallback — a local SQLite knowledge base (FTS5 keyword + bge-small-en-v1.5 vectors) if the vector service is unreachable.

knowledge:
  enabled: true
  backend: auto          # qdrant | sqlite | auto
  qdrant_host: "192.168.1.150"
  qdrant_port: 6333
  qdrant_collection: "recon_knowledge_hybrid"
  tei_host: "192.168.1.150"
  tei_port: 8090
  top_k: 5

The curated channel chatter your bot observes is used only as short-term context — it is never written into the knowledge base.

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LLM configuration

llm:
  backend: "google"          # google | openai | anthropic
  api_key: "your-api-key"
  model: "gemini-2.5-flash"

Any OpenAI-compatible endpoint works for local models — point base_url at Ollama (http://localhost:11434/v1), LiteLLM (http://localhost:4000/v1), or Open WebUI.

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Architecture

                       ┌─────────────────────────────┐
   Meshtastic ────────▶│                             │◀──────── MeshCore
   (TCP / serial)      │      CompositeTransport      │     (companion / pyMC TCP)
                       │   per-mesh routing + sizing  │
                       └──────────────┬──────────────┘
                                      │
        ┌─────────────────────────────┼─────────────────────────────┐
        ▼                             ▼                             ▼
  ┌───────────┐              ┌─────────────────┐            ┌──────────────┐
  │  Router   │              │  Notification    │            │  Mesh Data   │
  │ LLM / cmd │              │  Pipeline        │            │  Store +     │
  │ DM gating │              │  weather · fire  │            │  Health      │
  │ per-mesh  │              │  road · seismic  │            │  Engine      │
  │ context   │              │  RF · mesh-health│            │  5-pillar    │
  └─────┬─────┘              └────────┬─────────┘            └──────┬───────┘
        │                            │                              │
   ┌────▼─────┐   ┌──────────────┐   │        ┌──────────────┐      │
   │   LLM    │   │  Knowledge   │   │        │ Env / Central │◀─────┘
   │ backend  │   │  Qdrant/FTS5 │   │        │ feed adapters │
   └──────────┘   └──────────────┘   ▼        └──────────────┘
                                ┌──────────┐
                                │ Responder│  ACK-paced, LoRa-fit,
                                │ + Chunker│  routed per mesh
                                └──────────┘
                                      │
                              Web Dashboard (React) ── configure everything

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Running as a service

# /etc/systemd/system/meshai.service
[Unit]
Description=MeshAI
After=network.target

[Service]
Type=simple
User=your-user
WorkingDirectory=/path/to/meshai
ExecStart=/usr/bin/python3 -m meshai
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

sudo systemctl daemon-reload
sudo systemctl enable --now meshai

Every deployment is designed to survive a reboot; the dashboard's connection settings drive both transports.

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Playing nice with other services

• advBBS — MeshAI coexists on the same Meshtastic node; BBS protocol traffic (sync, RAP, mail) is auto-filtered (bot.filter_bbs_protocols: true).
• MeshMonitor — MeshAI reads MeshMonitor's auto-responder patterns to avoid duplicate replies, and uses its API as a mesh-intelligence data source.
• MeshCore companion — MeshAI attaches as its own companion identity so it can share the radio without evicting other companion clients.

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Acknowledgments

• Meshtastic — the mesh platform it started on
• MeshCore & pyMC — the second transport
• MeshMonitor by Yeraze — monitoring integration & data source
• advBBS — coexistence design
• Qdrant · sqlite-vec · fastembed — retrieval stack
• The LLM coding assistants that vibecoded most of this

License

MIT

Author

K7ZVX — matt@echo6.co