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# MeshAI
LLM-powered assistant for Meshtastic mesh networks.
LLM-powered mesh intelligence assistant for Meshtastic networks. MeshAI connects to your mesh as a physical node, monitors network health in real-time, and answers questions about your infrastructure over LoRa.
## Features
## What It Does
- **LLM Chat**: Responds to @mentions and DMs with AI-generated responses
- **Multi-backend**: Supports OpenAI, Anthropic Claude, Google Gemini, and local LLMs via LiteLLM
- **Knowledge Base (RAG)**: Hybrid FTS5 + vector search over Meshtastic documentation
- **Message Chunking**: Sentence-aware splitting with continuation prompts for long responses
- **Bang Commands**: `!help`, `!ping`, `!reset`, `!status`, `!weather`
- **Conversation History**: Per-user context maintained in SQLite
- **Rate Limiting**: Configurable delays to avoid flooding the mesh
- **advBBS Compatible**: Runs alongside [advBBS](https://github.com/zvx-echo6/advbbs) on the same node — protocol sync messages and mail notifications are automatically filtered
- **Rich Configurator**: Interactive TUI for easy setup
- **MeshMonitor Integration**: Syncs with [MeshMonitor](https://github.com/Yeraze/meshmonitor) by Yeraze to avoid duplicate responses
MeshAI runs on your Meshtastic node and provides:
## Installation
```bash
# Clone the repository
git clone https://github.com/zvx-echo6/meshai.git
cd meshai
# Install with pip
pip install -e .
# Or install dependencies manually
pip install -r requirements.txt
```
- **Mesh Intelligence** — 5-pillar health scoring, per-region breakdowns, infrastructure monitoring, coverage gap analysis, and environmental sensing
- **Conversational Queries** — ask "how's the mesh?" or "tell me about MHR" and get data-driven answers over LoRa
- **Node Distance** — GPS-based distance calculations between any two nodes on the mesh
- **Multi-Source Awareness** — aggregates data from multiple Meshview instances and MeshMonitor with staggered polling
- **Feeder Gateway Tracking** — identifies which physical MQTT gateways hear each node and signal quality
- **Subscriptions** — scheduled daily/weekly health reports and instant alerts delivered via DM
- **LLM Chat** — general conversation, knowledge base lookups, and weather queries
- **Multi-Backend** — supports Google Gemini, OpenAI, Anthropic Claude, and local LLMs via LiteLLM
## Quick Start
```bash
# Run the configurator
# Clone
git clone https://github.com/zvx-echo6/meshai.git
cd meshai
# Install
pip install -e .
# Configure (interactive TUI)
meshai --config
# Or copy and edit the example config
cp config.example.yaml config.yaml
# Edit config.yaml with your settings
# Run the bot
# Run
meshai
```
## Configuration
Or with Docker:
Run `meshai --config` to launch the interactive configurator, or edit `config.yaml` directly.
### Key Settings
```yaml
bot:
name: "ai" # @mention trigger
respond_to_mentions: true
respond_to_dms: true
connection:
type: "serial" # serial or tcp
serial_port: "/dev/ttyUSB0"
llm:
backend: "openai" # openai, anthropic, google
api_key: "your-api-key"
model: "gpt-4o-mini"
```bash
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
docker compose up -d
```
### Using Local LLMs
MeshAI works with any OpenAI-compatible API, including:
- **LiteLLM**: `base_url: "http://localhost:4000/v1"`
- **Open WebUI**: `base_url: "http://localhost:3000/api"`
- **Ollama**: `base_url: "http://localhost:11434/v1"`
## Commands
| Command | Description |
|---------|-------------|
| `!health` | Mesh health overview with colored status dots |
| `!region` | List all regions with health status |
| `!region [name]` | Detailed region breakdown |
| `!neighbors [node]` | Top infrastructure neighbors with signal quality |
| `!sub daily 6pm` | Subscribe to daily health reports |
| `!sub weekly 8am sun` | Subscribe to weekly digest |
| `!sub alerts` | Subscribe to instant alerts on issues |
| `!unsub [type]` | Remove a subscription |
| `!mysubs` | List your active subscriptions |
| `!clear` | Clear conversation history |
| `!help` | Show available commands |
| `!ping` | Test connectivity |
| `!reset` | Clear your conversation history |
| `!status` | Show bot status and stats |
| `!weather [location]` | Get weather (uses GPS if no location given) |
| `!help [cmd]` | Detailed help for a command |
## Usage Examples
Commands can be disabled in config if another service (like MeshMonitor) handles them.
**Chat via @mention:**
```
@ai What's the weather like today?
> Seattle: 52F, Partly Cloudy, Wind 8mph
```
## Mesh Intelligence
**Direct message:**
```
DM: Tell me a short joke
> Why don't scientists trust atoms? They make up everything!
```
MeshAI continuously polls mesh data sources and computes a 5-pillar health score:
**Weather command:**
```
!weather Portland
> Portland: 48F, Rain, Wind 12mph
```
| Pillar | Weight | What It Measures |
|--------|--------|------------------|
| Infrastructure | 30% | Router uptime — how many infra nodes are online |
| Utilization | 25% | Channel busyness — RF congestion across the mesh |
| Coverage | 20% | Gateway reach — how many monitoring sources see each node |
| Behavior | 15% | Traffic patterns — detecting noisy or misconfigured nodes |
| Power | 10% | Battery health — infrastructure nodes only |
## Architecture
### Health Display
`!health` shows a compact overview with personality:
```
┌──────────────────────────────────────────────────────────────────┐
│ MeshAI │
├──────────────────────────────────────────────────────────────────┤
│ ┌─────────────┐ ┌─────────────┐ ┌──────────────────────┐ │
│ │ Meshtastic │ │ Message │ │ LLM Backend │ │
│ │ Connector │───▶│ Router │───▶│ (pluggable) │ │
│ │ Serial/TCP │ │ │ │ │ │
│ └─────────────┘ └──────┬──────┘ └──────────────────────┘ │
│ │ │ │ │
│ │ ┌──────▼──────┐ │ │
│ │ │ Conversation│ │ │
│ │ │ History │◀─────────────┘ │
│ │ │ (SQLite) │ │
│ │ └─────────────┘ │
│ │ │ │
│ │ ┌──────▼──────┐ ┌──────────────────────┐ │
│ │ │ Knowledge │───▶│ Hybrid FTS5+Vector │ │
│ │ │ Base │ │ (sqlite-vec + BGE) │ │
│ │ └─────────────┘ └──────────────────────┘ │
│ │ │
│ ▼ │
│ ┌─────────────┐ ┌─────────────┐ │
│ │ Responder │───▶│ Chunker │ Sentence-aware splitting │
│ │ │ │ │ + continuation prompts │
│ └─────────────┘ └─────────────┘ │
└──────────────────────────────────────────────────────────────────┘
📡 Mesh 🟢 healthy
🏗️ 15/16 routers up
❌ Down: TVM Tablerock Relay
📶 152 full coverage, 94 on thin ice
🔥 Hayden Peak Router at 21% util
🔋 All infra powered ✅
🌡️ 29-34°C across 2 sensors
Treasure Valley 🟢 | Magic Valley 🟢
```
Status dots: 🔵 perfect (100) · 🟢 healthy (75+) · 🟠 warning (50+) · 🔴 critical (<50)
### Monitoring Rules
Infrastructure nodes (routers, repeaters) are monitored individually with full detail — battery, offline alerts, coverage, neighbors, hardware. Client nodes dying is normal and not tracked. Channel utilization and environmental sensors are monitored for all nodes.
### Conversational Queries
Ask questions naturally over LoRa:
- "how's the mesh?" → health overview with top issues
- "tell me about MHR" → full node detail with neighbors, coverage, feeders
- "where do we need more coverage?" → named gaps with specific nodes
- "how far is MHR from AIDA?" → GPS distance calculation
- "which nodes only reach one gateway?" → named nodes with their gateway
- "which gateway has the best signal?" → feeder comparison
### Geographic Regions
Regions are configurable with local names, descriptions, aliases, and cities — all manageable through the TUI. No hardcoded geography in the code.
```yaml
mesh_intelligence:
regions:
- name: "South Central ID"
local_name: "Magic Valley"
description: "Twin Falls area"
aliases: ["southern Idaho", "magic valley"]
cities: ["Twin Falls", "Burley", "Jerome"]
lat: 42.5
lon: -114.5
radius_km: 80
```
## Data Sources
MeshAI aggregates from multiple sources using staggered tick-based polling (one API call per 30-second tick):
### Meshview
Unauthenticated REST API. Supports multiple instances.
| Endpoint | Interval | Data |
|----------|----------|------|
| `/api/packets` | 30s | Near real-time packet feed |
| `/api/nodes` | 2 min | Node list with metadata |
| `/api/stats` | 3 min | Traffic statistics |
| `/api/edges` | 3 min | Node-to-node connections |
| `/api/traceroutes` | 5 min | Route data |
| `/api/packets_seen` | 10 min | Per-gateway RSSI/SNR (sampled) |
### MeshMonitor
Authenticated (Bearer token). Single instance.
| Endpoint | Interval | Data |
|----------|----------|------|
| `/api/v1/packets` | 60s | Packet feed |
| `/api/v1/nodes` | 2 min | Nodes with battery, utilization, hardware |
| `/api/v1/telemetry` | 2 min | Environmental sensors, device metrics |
| `/api/v1/traceroutes` | 5 min | Route data |
| `/api/v1/channels` | 5 min | Channel configuration |
| `/api/v1/network` | 5 min | Network statistics |
| `/api/v1/solar` | 10 min | Solar estimates |
### Rate Limiting
Built-in protection for all sources: HTTP 429 backoff with Retry-After, exponential backoff on consecutive errors, slow response warnings, and optional polite mode for shared instances.
### Source Configuration
```yaml
mesh_sources:
- name: "local-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
```
## Knowledge Base (RAG)
MeshAI can answer questions using a local knowledge base built from Meshtastic documentation. The system uses hybrid search combining:
- **FTS5 keyword search** — fast exact term matching with domain-aware query construction
- **Vector embeddings** — semantic similarity using `bge-small-en-v1.5` (384 dimensions)
- **Reciprocal Rank Fusion** — merges results from both methods for best relevance
**Building the knowledge base:**
MeshAI answers questions using a local knowledge base built from Meshtastic documentation. The system uses hybrid search combining FTS5 keyword search, vector embeddings via `bge-small-en-v1.5`, and Reciprocal Rank Fusion for best relevance.
```bash
# Extract from Meshtastic ZIM file
# Build from Meshtastic ZIM file
python scripts/zim_to_knowledge.py meshtastic.zim --output knowledge.db
# Or from markdown files
python scripts/md_to_knowledge.py docs/ --output knowledge.db
```
**Configuration:**
```yaml
knowledge:
enabled: true
db_path: /data/meshai_knowledge.db
top_k: 5 # Number of chunks to retrieve
fts_weight: 0.5 # Weight for keyword matches (0-1)
vector_weight: 0.5 # Weight for semantic matches (0-1)
top_k: 5
fts_weight: 0.5
vector_weight: 0.5
```
The knowledge base requires `sqlite-vec` and `fastembed` (installed automatically with requirements.txt).
Requires `sqlite-vec` and `fastembed` (installed with requirements.txt).
## Architecture
```
┌──────────────────────────────────────────────────────────────────────┐
│ MeshAI │
├──────────────────────────────────────────────────────────────────────┤
│ │
│ DATA SOURCES INTELLIGENCE DELIVERY │
│ ┌─────────────┐ ┌──────────────┐ ┌────────────┐ │
│ │ Meshview ×N │─────┐ │ Health Engine │────────▶│ Reporter │ │
│ │ (staggered) │ │ │ 5-pillar │ │ Tier 1/2 │ │
│ └─────────────┘ ▼ │ scoring │ └─────┬──────┘ │
│ ┌─────────────┐ ┌──────┴──┐ │ │ │ │
│ │ MeshMonitor │─▶│ Data │─┘ │ ┌─────▼──────┐ │
│ │ (staggered) │ │ Store │ │ │ Router │ │
│ └─────────────┘ │ SQLite │ │ │ scope/dist │ │
│ └─────────┘ │ └─────┬──────┘ │
│ │ │ │ │
│ ┌────▼────┐ ┌─────▼──────┐ ┌────▼────┐ │
│ │ Feeder │ │ LLM │ │ Chunker │ │
│ │ Sampling│ │ Backend │ │LoRa-fit │ │
│ └─────────┘ └────────────┘ └────┬────┘ │
│ │ │
│ ┌─────────────┐ ┌─────────────┐ ┌─────────────┐ ┌────▼────┐ │
│ │ Knowledge │ │ Conversation│ │ Subscription │ │Responder│ │
│ │ Base (RAG) │ │ History │ │ Manager │ │ DM/CH │ │
│ └─────────────┘ └─────────────┘ └─────────────┘ └─────────┘ │
│ │
└──────────────────────────────────────────────────────────────────────┘
```
## Message Chunking
Long LLM responses are automatically split into mesh-friendly chunks:
- **Sentence-aware** — never splits a sentence across messages
- **Configurable limits** — max characters per message, max messages per response
- **Continuation prompts** — if content remains, asks "Want me to keep going?"
- **Natural follow-ups** — responds to "yes", "ok", "continue", "more", etc.
**Configuration:**
Long responses are split into mesh-friendly chunks with sentence-aware splitting, configurable limits, and continuation prompts. Command output (like `!health`) packs multiple lines into 2-3 messages using newlines within each message to minimize airtime usage.
```yaml
response:
@ -182,97 +237,64 @@ response:
max_messages: 3 # Messages before continuation prompt
```
## Docker
## LLM Configuration
### Quick Start with Docker
```bash
# Create working directory
mkdir -p meshai/data && cd meshai
# Download docker-compose file
curl -O https://raw.githubusercontent.com/zvx-echo6/meshai/main/docker-compose.yml
# Copy and edit config
curl -o data/config.yaml https://raw.githubusercontent.com/zvx-echo6/meshai/main/config.example.yaml
# Edit data/config.yaml with your settings
# Start
docker compose up -d
# View logs
docker compose logs -f
```yaml
llm:
backend: "google" # openai, anthropic, google
api_key: "your-api-key"
model: "gemini-2.0-flash"
```
### Docker Configuration
### Local LLMs
MeshAI works with any OpenAI-compatible API:
- **LiteLLM**: `base_url: "http://localhost:4000/v1"`
- **Open WebUI**: `base_url: "http://localhost:3000/api"`
- **Ollama**: `base_url: "http://localhost:11434/v1"`
## Docker
### TCP Connection (recommended)
**TCP Connection** (recommended for Docker):
```yaml
# data/config.yaml
connection:
type: "tcp"
tcp_host: "192.168.1.100" # Your Meshtastic node IP
tcp_host: "192.168.1.100"
tcp_port: 4403
```
**Serial Connection**:
### Serial Connection
```yaml
# data/config.yaml
connection:
type: "serial"
serial_port: "/dev/ttyUSB0"
```
Then edit `docker-compose.serial.yml` to match your device path.
Edit `docker-compose.serial.yml` to match your device path.
### Environment Variables
You can pass the API key via environment variable instead of config file:
```bash
LLM_API_KEY=your-key-here docker compose up -d
```
Or create a `.env` file:
```bash
LLM_API_KEY=your-key-here
```
## Running Alongside Other Services
### View Logs
### advBBS
```bash
docker compose logs -f meshai
```
## Running Alongside advBBS
MeshAI is designed to coexist with [advBBS](https://github.com/zvx-echo6/advbbs) on the same Meshtastic node. Both connect via TCP to meshtasticd and share the radio, but MeshAI automatically ignores advBBS traffic:
- **Sync protocol** — `MAILREQ|`, `MAILACK|`, `MAILDAT|`, `BOARDREQ|`, etc.
- **RAP protocol** — `advBBS|` pings, pongs, and route advertisements
- **Mail notifications** — `[MAIL]` new message alerts
- **Bang commands in DMs** — `!mail`, `!board`, etc. are left for advBBS to handle
No special configuration is needed. The filter is enabled by default and can be toggled in `config.yaml`:
MeshAI coexists with [advBBS](https://github.com/zvx-echo6/advbbs) on the same node. BBS protocol messages (sync, RAP, mail notifications) are automatically filtered. No configuration needed.
```yaml
bot:
filter_bbs_protocols: true # set false to disable
filter_bbs_protocols: true
```
Plain-text BBS responses (e.g. "Welcome back, matt!") are indistinguishable from normal user messages and will be processed normally — this is a known and accepted limitation.
### MeshMonitor
## MeshMonitor Integration
MeshAI integrates with [MeshMonitor](https://github.com/Yeraze/meshmonitor), a comprehensive Meshtastic monitoring platform by Yeraze. When enabled, MeshAI automatically fetches MeshMonitor's auto-responder trigger patterns and ignores messages that MeshMonitor handles, preventing duplicate responses on the mesh.
**Features:**
- Automatic trigger discovery via MeshMonitor's HTTP API
- Dynamic ignore list — no manual sync needed
- Trigger list injected into the LLM prompt so MeshAI can discuss MeshMonitor commands conversationally
- Configurable via TUI (option 9) or config.yaml
**Configuration:**
MeshAI integrates with [MeshMonitor](https://github.com/Yeraze/meshmonitor) at two levels: it fetches MeshMonitor's auto-responder patterns to avoid duplicate responses, and it uses MeshMonitor's API as a data source for mesh intelligence (battery, telemetry, traceroutes, solar).
```yaml
meshmonitor:
@ -282,15 +304,12 @@ meshmonitor:
refresh_interval: 300
```
MeshMonitor is a separate project — get it at https://github.com/Yeraze/meshmonitor
## Running as a Service
Create `/etc/systemd/system/meshai.service`:
```ini
# /etc/systemd/system/meshai.service
[Unit]
Description=MeshAI - Meshtastic LLM Assistant
Description=MeshAI - Meshtastic Mesh Intelligence
After=network.target
[Service]
@ -305,7 +324,6 @@ RestartSec=10
WantedBy=multi-user.target
```
Then:
```bash
sudo systemctl daemon-reload
sudo systemctl enable meshai
@ -315,7 +333,7 @@ sudo systemctl start meshai
## Acknowledgments
- [Meshtastic](https://meshtastic.org/) — the mesh networking platform
- [MeshMonitor](https://github.com/Yeraze/meshmonitor) by Yeraze — monitoring integration
- [MeshMonitor](https://github.com/Yeraze/meshmonitor) by Yeraze — monitoring integration and data source
- [advBBS](https://github.com/zvx-echo6/advbbs) — BBS coexistence design
- [sqlite-vec](https://github.com/asg017/sqlite-vec) by Alex Garcia — vector search in SQLite
- [fastembed](https://github.com/qdrant/fastembed) by Qdrant — fast local embeddings