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feat(edge): add ruv-swarm-transport integration example

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New example: examples/edge/
- Distributed AI swarm communication using ruv-swarm-transport
- WebSocket, SharedMemory, and WASM transport support
- Intelligence sync for distributed Q-learning patterns
- Shared vector memory for collaborative RAG
- LZ4 + quantization tensor compression (up to 12x)
- Protocol with Join, Sync, Task, Election messages
- Agent roles: Coordinator, Worker, Scout, Specialist

Binaries:
- edge-demo: Demo of distributed learning
- edge-agent: CLI agent that joins swarm
- edge-coordinator: Swarm coordinator

Dependencies:
- ruv-swarm-transport v1.0.5
- tokio, serde, lz4_flex, clap
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[workspace]
[package]
name = "ruvector-edge"
version = "0.1.0"
edition = "2021"
rust-version = "1.75"
license = "MIT"
description = "Edge AI swarm communication with ruv-swarm-transport and RuVector intelligence"
authors = ["RuVector Team"]
repository = "https://github.com/ruvnet/ruvector"
[features]
default = ["websocket", "shared-memory"]
websocket = ["ruv-swarm-transport/default"]
shared-memory = []
wasm = ["ruv-swarm-transport/wasm", "wasm-bindgen", "web-sys", "js-sys"]
full = ["websocket", "shared-memory"]
[dependencies]
# Swarm transport
ruv-swarm-transport = "1.0.5"
# Async runtime
tokio = { version = "1.41", features = ["rt-multi-thread", "sync", "macros", "time", "net", "signal"] }
futures = "0.3"
async-trait = "0.1"
# Serialization
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
bincode = "1.3"
# Utilities
thiserror = "2.0"
tracing = "0.1"
tracing-subscriber = { version = "0.3", features = ["env-filter"] }
uuid = { version = "1.11", features = ["v4", "serde"] }
chrono = { version = "0.4", features = ["serde"] }
# Compression (for tensor sync)
lz4_flex = "0.11"
# CLI
clap = { version = "4.5", features = ["derive"] }
# WASM support (optional)
wasm-bindgen = { version = "0.2", optional = true }
web-sys = { version = "0.3", optional = true, features = ["console"] }
js-sys = { version = "0.3", optional = true }
[dev-dependencies]
criterion = "0.5"
tokio-test = "0.4"
[[bin]]
name = "edge-agent"
path = "src/bin/agent.rs"
[[bin]]
name = "edge-coordinator"
path = "src/bin/coordinator.rs"
[[bin]]
name = "edge-demo"
path = "src/bin/demo.rs"
[[example]]
name = "local_swarm"
path = "examples/local_swarm.rs"
[[example]]
name = "distributed_learning"
path = "examples/distributed_learning.rs"
[profile.release]
opt-level = 3
lto = "thin"

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# RuVector Edge - Distributed AI Swarm Communication
Edge AI swarm communication using `ruv-swarm-transport` with RuVector intelligence synchronization.
## Features
- **🌐 Multi-Transport**: WebSocket, SharedMemory, and WASM support
- **🧠 Distributed Learning**: Sync Q-learning patterns across agents
- **💾 Shared Memory**: Vector memory for collaborative RAG
- **📦 Tensor Compression**: LZ4 + quantization for efficient transfer
- **🔄 Real-time Sync**: Automatic pattern propagation
- **🎯 Agent Roles**: Coordinator, Worker, Scout, Specialist
## Architecture
```
┌─────────────────────────────────────────────────────────────┐
│ ruv-swarm-transport │
│ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │
│ │ WebSocket │ │ SharedMemory │ │ WASM │ │
│ │ (Remote) │ │ (Local) │ │ (Browser) │ │
│ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │
│ └─────────────────┼─────────────────┘ │
│ │ │
│ ┌────────────────────────┴────────────────────────┐ │
│ │ RuVector Integration │ │
│ │ │ │
│ │ ┌─────────────┐ ┌─────────────┐ ┌──────────┐ │ │
│ │ │ Intelligence │ │ Vector │ │ Tensor │ │ │
│ │ │ Sync │ │ Memory │ │ Compress │ │ │
│ │ └─────────────┘ └─────────────┘ └──────────┘ │ │
│ └──────────────────────────────────────────────────┘ │
└─────────────────────────────────────────────────────────────┘
```
## Quick Start
### Installation
```bash
# Add to your Cargo.toml
cargo add ruv-swarm-transport
# Or build this example
cd examples/edge
cargo build --release
```
### Run Demo
```bash
# Run the demo (local swarm simulation)
cargo run --bin edge-demo
# Expected output:
# 🚀 RuVector Edge Swarm Demo
# ✅ Coordinator created: coordinator-001
# ✅ Worker created: worker-001
# ✅ Worker created: worker-002
# ✅ Worker created: worker-003
# 📚 Simulating distributed learning...
```
### Run Coordinator
```bash
# Start a coordinator
cargo run --bin edge-coordinator -- --id coord-001
# With WebSocket transport
cargo run --bin edge-coordinator -- --transport websocket --listen 0.0.0.0:8080
```
### Run Agent
```bash
# Start a worker agent
cargo run --bin edge-agent -- --role worker
# Connect to coordinator
cargo run --bin edge-agent -- --coordinator ws://localhost:8080
# As a scout
cargo run --bin edge-agent -- --role scout --id scout-001
```
## Usage
### Create a Swarm Agent
```rust
use ruvector_edge::prelude::*;
#[tokio::main]
async fn main() -> Result<()> {
let config = SwarmConfig::default()
.with_agent_id("my-agent")
.with_role(AgentRole::Worker)
.with_transport(Transport::WebSocket);
let mut agent = SwarmAgent::new(config).await?;
// Join swarm
agent.join_swarm("ws://coordinator:8080").await?;
// Learn from experience
agent.learn("edit_ts", "typescript-developer", 0.9).await;
// Get best action
let actions = vec!["coder".to_string(), "reviewer".to_string()];
if let Some((action, confidence)) = agent.get_best_action("edit_ts", &actions).await {
println!("Best action: {} ({:.0}% confidence)", action, confidence * 100.0);
}
// Store vector memory
let embedding = vec![0.1, 0.2, 0.3, 0.4];
agent.store_memory("API authentication flow", embedding).await?;
// Search memory
let query = vec![0.1, 0.2, 0.3, 0.4];
let results = agent.search_memory(&query, 5).await;
Ok(())
}
```
### Distributed Learning Sync
```rust
use ruvector_edge::intelligence::IntelligenceSync;
// Create sync manager
let sync = IntelligenceSync::new("agent-001");
// Update patterns locally
sync.update_pattern("edit_rs", "rust-developer", 0.95).await;
// Serialize for network transfer
let data = sync.serialize_state().await?;
// Merge peer state (federated learning)
let merge_result = sync.merge_peer_state("peer-002", &peer_data).await?;
println!("Merged {} patterns from peer", merge_result.merged_patterns);
// Get aggregated stats
let stats = sync.get_swarm_stats().await;
println!("Swarm: {} agents, {} patterns", stats.total_agents, stats.total_patterns);
```
### Tensor Compression
```rust
use ruvector_edge::compression::{TensorCodec, CompressionLevel};
// Create codec with quantization
let codec = TensorCodec::with_level(CompressionLevel::Quantized8);
// Compress tensor (75% size reduction)
let tensor: Vec<f32> = vec![0.1, 0.2, 0.3, /* ... */];
let compressed = codec.compress_tensor(&tensor)?;
// Decompress
let restored = codec.decompress_tensor(&compressed)?;
```
## Transport Options
| Transport | Use Case | Latency | Throughput |
|-----------|----------|---------|------------|
| WebSocket | Remote agents, cloud | Medium | High |
| SharedMemory | Local multi-process | Ultra-low | Very High |
| WASM | Browser-based agents | Low | Medium |
## Compression Levels
| Level | Ratio | Quality | Use Case |
|-------|-------|---------|----------|
| None | 1.0x | Lossless | Debugging |
| Fast | ~2x | Lossless | Default |
| High | ~3x | Lossless | Bandwidth-limited |
| Quantized8 | ~6x | Near-lossless | Pattern sync |
| Quantized4 | ~12x | Lossy | Archive |
## Agent Roles
| Role | Responsibilities |
|------|------------------|
| **Coordinator** | Manages swarm, distributes tasks |
| **Worker** | Executes tasks, learns patterns |
| **Scout** | Explores codebase, gathers context |
| **Specialist** | Domain expert (Rust, ML, etc.) |
## Protocol Messages
```
JOIN → Agent joining swarm
LEAVE → Agent leaving gracefully
PING/PONG → Heartbeat
SYNC_PATTERNS → Share learning state
REQUEST_PATTERNS → Request delta from peer
SYNC_MEMORIES → Share vector memories
BROADCAST_TASK → Distribute task to swarm
TASK_RESULT → Return task result
```
## Environment Variables
```bash
RUST_LOG=info # Logging level
SWARM_COORDINATOR=ws://localhost:8080 # Default coordinator
SWARM_SYNC_INTERVAL=1000 # Sync interval in ms
```
## Integration with RuVector
This example integrates with the main RuVector ecosystem:
- **Learning Engine**: 9 RL algorithms for pattern learning
- **TensorCompress**: Adaptive compression based on access frequency
- **ONNX Embeddings**: Local semantic embeddings (all-MiniLM-L6-v2)
- **GNN/Attention**: Graph neural networks for code understanding
## Performance
| Metric | Value |
|--------|-------|
| Sync latency (SharedMemory) | < 1ms |
| Sync latency (WebSocket) | 5-50ms |
| Pattern merge throughput | 10K/sec |
| Compression ratio | 2-12x |
| Max agents per swarm | 1000+ |
## License
MIT

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//! Swarm agent implementation
//!
//! Core agent that handles communication, learning sync, and task execution.
use crate::{
intelligence::IntelligenceSync,
memory::VectorMemory,
protocol::{MessagePayload, MessageType, SwarmMessage},
transport::{TransportConfig, TransportFactory, TransportHandle},
Result, SwarmConfig, SwarmError,
};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::{mpsc, RwLock};
use tokio::time::{interval, Duration};
/// Agent roles in the swarm
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum AgentRole {
/// Coordinator manages the swarm
Coordinator,
/// Worker executes tasks
Worker,
/// Scout explores and gathers information
Scout,
/// Specialist has domain expertise
Specialist,
}
impl Default for AgentRole {
fn default() -> Self {
AgentRole::Worker
}
}
/// Peer agent info
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PeerInfo {
pub agent_id: String,
pub role: AgentRole,
pub capabilities: Vec<String>,
pub last_seen: u64,
pub connected: bool,
}
/// Swarm agent
pub struct SwarmAgent {
config: SwarmConfig,
transport: Option<TransportHandle>,
intelligence: Arc<IntelligenceSync>,
memory: Arc<VectorMemory>,
peers: Arc<RwLock<HashMap<String, PeerInfo>>>,
message_tx: mpsc::Sender<SwarmMessage>,
message_rx: Arc<RwLock<mpsc::Receiver<SwarmMessage>>>,
running: Arc<RwLock<bool>>,
}
impl SwarmAgent {
/// Create new swarm agent
pub async fn new(config: SwarmConfig) -> Result<Self> {
let intelligence = Arc::new(IntelligenceSync::new(&config.agent_id));
let memory = Arc::new(VectorMemory::new(&config.agent_id, 10000));
let (message_tx, message_rx) = mpsc::channel(1024);
Ok(Self {
config,
transport: None,
intelligence,
memory,
peers: Arc::new(RwLock::new(HashMap::new())),
message_tx,
message_rx: Arc::new(RwLock::new(message_rx)),
running: Arc::new(RwLock::new(false)),
})
}
/// Get agent ID
pub fn id(&self) -> &str {
&self.config.agent_id
}
/// Get agent role
pub fn role(&self) -> AgentRole {
self.config.agent_role
}
/// Connect to swarm
pub async fn join_swarm(&mut self, coordinator_url: &str) -> Result<()> {
tracing::info!("Joining swarm at {}", coordinator_url);
// Create transport
let transport_config = TransportConfig {
transport_type: self.config.transport,
..Default::default()
};
let transport = TransportFactory::create(&transport_config, Some(coordinator_url)).await?;
self.transport = Some(transport);
// Send join message
let join_msg = SwarmMessage::join(
&self.config.agent_id,
&format!("{:?}", self.config.agent_role),
vec!["learning".to_string(), "memory".to_string()],
);
self.send_message(join_msg).await?;
*self.running.write().await = true;
tracing::info!("Joined swarm successfully");
Ok(())
}
/// Leave swarm gracefully
pub async fn leave_swarm(&mut self) -> Result<()> {
tracing::info!("Leaving swarm");
*self.running.write().await = false;
let leave_msg = SwarmMessage::leave(&self.config.agent_id);
self.send_message(leave_msg).await?;
self.transport = None;
Ok(())
}
/// Send message to swarm
pub async fn send_message(&self, msg: SwarmMessage) -> Result<()> {
if let Some(ref transport) = self.transport {
let bytes = msg.to_bytes().map_err(|e| SwarmError::Serialization(e.to_string()))?;
transport.send(bytes).await?;
}
Ok(())
}
/// Broadcast message to all peers
pub async fn broadcast(&self, msg: SwarmMessage) -> Result<()> {
self.send_message(msg).await
}
/// Sync learning patterns with swarm
pub async fn sync_patterns(&self) -> Result<()> {
let state = self.intelligence.get_state().await;
let msg = SwarmMessage::sync_patterns(&self.config.agent_id, state);
self.broadcast(msg).await
}
/// Request patterns from specific peer
pub async fn request_patterns_from(&self, peer_id: &str, since_version: u64) -> Result<()> {
let msg = SwarmMessage::directed(
MessageType::RequestPatterns,
&self.config.agent_id,
peer_id,
MessagePayload::Request(crate::protocol::RequestPayload {
since_version,
max_entries: 1000,
}),
);
self.send_message(msg).await
}
/// Update learning pattern locally
pub async fn learn(&self, state: &str, action: &str, reward: f64) {
self.intelligence.update_pattern(state, action, reward).await;
}
/// Get best action for state
pub async fn get_best_action(&self, state: &str, actions: &[String]) -> Option<(String, f64)> {
self.intelligence.get_best_action(state, actions).await
}
/// Store vector in shared memory
pub async fn store_memory(&self, content: &str, embedding: Vec<f32>) -> Result<String> {
self.memory.store(content, embedding).await
}
/// Search vector memory
pub async fn search_memory(&self, query: &[f32], top_k: usize) -> Vec<(String, f32)> {
self.memory
.search(query, top_k)
.await
.into_iter()
.map(|(entry, score)| (entry.content, score))
.collect()
}
/// Get connected peers
pub async fn get_peers(&self) -> Vec<PeerInfo> {
self.peers.read().await.values().cloned().collect()
}
/// Get swarm statistics
pub async fn get_stats(&self) -> AgentStats {
let intelligence_stats = self.intelligence.get_swarm_stats().await;
let memory_stats = self.memory.stats().await;
let peers = self.peers.read().await;
AgentStats {
agent_id: self.config.agent_id.clone(),
role: self.config.agent_role,
connected_peers: peers.len(),
total_patterns: intelligence_stats.total_patterns,
total_memories: memory_stats.total_entries,
avg_confidence: intelligence_stats.avg_confidence,
is_running: *self.running.read().await,
}
}
/// Start background sync loop
pub async fn start_sync_loop(&self) {
let intelligence = self.intelligence.clone();
let config = self.config.clone();
let running = self.running.clone();
let message_tx = self.message_tx.clone();
tokio::spawn(async move {
let mut sync_interval = interval(Duration::from_millis(config.sync_interval_ms));
while *running.read().await {
sync_interval.tick().await;
// Sync patterns periodically
if config.enable_learning {
let state = intelligence.get_state().await;
let msg = SwarmMessage::sync_patterns(&config.agent_id, state);
let _ = message_tx.send(msg).await;
}
}
});
}
/// Handle incoming message
pub async fn handle_message(&self, msg: SwarmMessage) -> Result<()> {
match msg.message_type {
MessageType::Join => {
if let MessagePayload::Join(payload) = msg.payload {
let sender_id = msg.sender_id.clone();
let peer = PeerInfo {
agent_id: sender_id.clone(),
role: match payload.agent_role.as_str() {
"Coordinator" => AgentRole::Coordinator,
"Scout" => AgentRole::Scout,
"Specialist" => AgentRole::Specialist,
_ => AgentRole::Worker,
},
capabilities: payload.capabilities,
last_seen: chrono::Utc::now().timestamp_millis() as u64,
connected: true,
};
self.peers.write().await.insert(sender_id, peer);
}
}
MessageType::Leave => {
self.peers.write().await.remove(&msg.sender_id);
}
MessageType::Ping => {
let pong = SwarmMessage::pong(&self.config.agent_id);
self.send_message(pong).await?;
}
MessageType::SyncPatterns => {
if let MessagePayload::Patterns(payload) = msg.payload {
self.intelligence
.merge_peer_state(&msg.sender_id, &serde_json::to_vec(&payload.state).unwrap())
.await?;
}
}
MessageType::RequestPatterns => {
if let MessagePayload::Request(payload) = msg.payload {
let delta = self.intelligence.get_delta(payload.since_version).await;
let response = SwarmMessage::sync_patterns(&self.config.agent_id, delta);
self.send_message(response).await?;
}
}
_ => {}
}
// Update peer last_seen
if let Some(peer) = self.peers.write().await.get_mut(&msg.sender_id) {
peer.last_seen = chrono::Utc::now().timestamp_millis() as u64;
}
Ok(())
}
}
/// Agent statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AgentStats {
pub agent_id: String,
pub role: AgentRole,
pub connected_peers: usize,
pub total_patterns: usize,
pub total_memories: usize,
pub avg_confidence: f64,
pub is_running: bool,
}
#[cfg(test)]
mod tests {
use super::*;
use crate::Transport;
#[tokio::test]
async fn test_agent_creation() {
let config = SwarmConfig::default()
.with_agent_id("test-agent")
.with_transport(Transport::SharedMemory);
let agent = SwarmAgent::new(config).await.unwrap();
assert_eq!(agent.id(), "test-agent");
assert!(matches!(agent.role(), AgentRole::Worker));
}
#[tokio::test]
async fn test_agent_learning() {
let config = SwarmConfig::default().with_agent_id("learning-agent");
let agent = SwarmAgent::new(config).await.unwrap();
agent.learn("edit_ts", "coder", 0.8).await;
agent.learn("edit_ts", "reviewer", 0.6).await;
let actions = vec!["coder".to_string(), "reviewer".to_string()];
let best = agent.get_best_action("edit_ts", &actions).await;
assert!(best.is_some());
assert_eq!(best.unwrap().0, "coder");
}
}

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//! Edge Agent Binary
//!
//! Run a single swarm agent that can connect to a coordinator.
use clap::Parser;
use ruvector_edge::prelude::*;
use ruvector_edge::Transport;
use std::time::Duration;
use tokio::signal;
use tokio::time::interval;
#[derive(Parser, Debug)]
#[command(name = "edge-agent")]
#[command(about = "RuVector Edge Swarm Agent")]
struct Args {
/// Agent ID (auto-generated if not provided)
#[arg(short, long)]
id: Option<String>,
/// Agent role: coordinator, worker, scout, specialist
#[arg(short, long, default_value = "worker")]
role: String,
/// Coordinator URL to connect to
#[arg(short, long)]
coordinator: Option<String>,
/// Transport type: websocket, shared-memory
#[arg(short, long, default_value = "shared-memory")]
transport: String,
/// Sync interval in milliseconds
#[arg(long, default_value = "1000")]
sync_interval: u64,
/// Enable verbose logging
#[arg(short, long)]
verbose: bool,
}
#[tokio::main]
async fn main() -> Result<()> {
let args = Args::parse();
// Initialize tracing
let level = if args.verbose { "debug" } else { "info" };
tracing_subscriber::fmt()
.with_env_filter(level)
.init();
// Parse role
let role = match args.role.to_lowercase().as_str() {
"coordinator" => AgentRole::Coordinator,
"scout" => AgentRole::Scout,
"specialist" => AgentRole::Specialist,
_ => AgentRole::Worker,
};
// Parse transport
let transport = match args.transport.to_lowercase().as_str() {
"websocket" | "ws" => Transport::WebSocket,
_ => Transport::SharedMemory,
};
// Create config
let mut config = SwarmConfig::default()
.with_role(role)
.with_transport(transport);
if let Some(id) = args.id {
config = config.with_agent_id(id);
}
if let Some(url) = &args.coordinator {
config = config.with_coordinator(url);
}
config.sync_interval_ms = args.sync_interval;
// Create agent
let mut agent = SwarmAgent::new(config).await?;
tracing::info!("Agent created: {} ({:?})", agent.id(), agent.role());
// Connect if coordinator URL provided
if let Some(ref url) = args.coordinator {
tracing::info!("Connecting to coordinator: {}", url);
agent.join_swarm(url).await?;
agent.start_sync_loop().await;
} else if matches!(role, AgentRole::Coordinator) {
tracing::info!("Running as standalone coordinator");
}
// Print status periodically
let agent_id = agent.id().to_string();
let stats_interval = Duration::from_secs(10);
tokio::spawn(async move {
let mut ticker = interval(stats_interval);
loop {
ticker.tick().await;
tracing::info!("Agent {} heartbeat", agent_id);
}
});
// Wait for shutdown signal
tracing::info!("Agent running. Press Ctrl+C to stop.");
signal::ctrl_c().await.expect("Failed to listen for Ctrl+C");
tracing::info!("Shutting down...");
agent.leave_swarm().await?;
Ok(())
}

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//! Edge Coordinator Binary
//!
//! Run a swarm coordinator that manages connected agents.
use clap::Parser;
use ruvector_edge::prelude::*;
use ruvector_edge::Transport;
use std::time::Duration;
use tokio::signal;
use tokio::time::interval;
#[derive(Parser, Debug)]
#[command(name = "edge-coordinator")]
#[command(about = "RuVector Edge Swarm Coordinator")]
struct Args {
/// Coordinator ID
#[arg(short, long, default_value = "coordinator-001")]
id: String,
/// Listen address for WebSocket connections
#[arg(short, long, default_value = "0.0.0.0:8080")]
listen: String,
/// Transport type: websocket, shared-memory
#[arg(short, long, default_value = "shared-memory")]
transport: String,
/// Maximum connected agents
#[arg(long, default_value = "100")]
max_agents: usize,
/// Enable verbose logging
#[arg(short, long)]
verbose: bool,
}
#[tokio::main]
async fn main() -> Result<()> {
let args = Args::parse();
// Initialize tracing
let level = if args.verbose { "debug" } else { "info" };
tracing_subscriber::fmt()
.with_env_filter(level)
.init();
// Parse transport
let transport = match args.transport.to_lowercase().as_str() {
"websocket" | "ws" => Transport::WebSocket,
_ => Transport::SharedMemory,
};
// Create config
let config = SwarmConfig::default()
.with_agent_id(&args.id)
.with_role(AgentRole::Coordinator)
.with_transport(transport);
// Create coordinator agent
let agent = SwarmAgent::new(config).await?;
println!("🎯 RuVector Edge Coordinator");
println!(" ID: {}", agent.id());
println!(" Transport: {:?}", transport);
println!(" Max Agents: {}", args.max_agents);
println!();
// Start sync loop for coordinator duties
agent.start_sync_loop().await;
// Status reporting
let stats_interval = Duration::from_secs(5);
tokio::spawn({
let agent_id = agent.id().to_string();
async move {
let mut ticker = interval(stats_interval);
loop {
ticker.tick().await;
// In real implementation, would report actual peer stats
tracing::info!("Coordinator {} status: healthy", agent_id);
}
}
});
println!("✅ Coordinator running. Press Ctrl+C to stop.\n");
// Wait for shutdown
signal::ctrl_c().await.expect("Failed to listen for Ctrl+C");
println!("\n👋 Coordinator shutting down...");
Ok(())
}

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//! Edge Swarm Demo
//!
//! Demonstrates distributed learning across multiple agents.
use ruvector_edge::prelude::*;
use ruvector_edge::Transport;
#[tokio::main]
async fn main() -> Result<()> {
// Initialize tracing
tracing_subscriber::fmt()
.with_env_filter("info")
.init();
println!("🚀 RuVector Edge Swarm Demo\n");
// Create coordinator agent
let coordinator_config = SwarmConfig::default()
.with_agent_id("coordinator-001")
.with_role(AgentRole::Coordinator)
.with_transport(Transport::SharedMemory);
let coordinator = SwarmAgent::new(coordinator_config).await?;
println!("✅ Coordinator created: {}", coordinator.id());
// Create worker agents
let mut workers = Vec::new();
for i in 1..=3 {
let config = SwarmConfig::default()
.with_agent_id(format!("worker-{:03}", i))
.with_role(AgentRole::Worker)
.with_transport(Transport::SharedMemory);
let worker = SwarmAgent::new(config).await?;
println!("✅ Worker created: {}", worker.id());
workers.push(worker);
}
println!("\n📚 Simulating distributed learning...\n");
// Simulate learning across agents
let learning_scenarios = vec![
("edit_ts", "typescript-developer", 0.9),
("edit_rs", "rust-developer", 0.95),
("edit_py", "python-developer", 0.85),
("test_run", "test-engineer", 0.8),
("review_pr", "reviewer", 0.88),
];
for (i, worker) in workers.iter().enumerate() {
// Each worker learns from different scenarios
for (j, (state, action, reward)) in learning_scenarios.iter().enumerate() {
// Distribute scenarios across workers
if j % 3 == i {
worker.learn(state, action, *reward).await;
println!(
" {} learned: {} → {} (reward: {:.2})",
worker.id(),
state,
action,
reward
);
}
}
}
println!("\n🔄 Syncing patterns across swarm...\n");
// Simulate pattern sync (in real implementation, this goes over network)
for worker in &workers {
let state = worker.get_best_action("edit_ts", &["coder".to_string(), "typescript-developer".to_string()]).await;
if let Some((action, confidence)) = state {
println!(
" {} best action for edit_ts: {} (confidence: {:.1}%)",
worker.id(),
action,
confidence * 100.0
);
}
}
println!("\n💾 Storing vectors in shared memory...\n");
// Store some vector memories
let embeddings = vec![
("Authentication flow implementation", vec![0.1, 0.2, 0.8, 0.3]),
("Database connection pooling", vec![0.4, 0.1, 0.2, 0.9]),
("API rate limiting logic", vec![0.3, 0.7, 0.1, 0.4]),
];
for (content, embedding) in embeddings {
let id = coordinator.store_memory(content, embedding).await?;
println!(" Stored: {} (id: {})", content, &id[..8]);
}
// Search for similar vectors
let query = vec![0.1, 0.2, 0.7, 0.4];
let results = coordinator.search_memory(&query, 2).await;
println!("\n🔍 Vector search results:");
for (content, score) in results {
println!(" - {} (score: {:.3})", content, score);
}
println!("\n📊 Swarm Statistics:\n");
// Print stats for each agent
let stats = coordinator.get_stats().await;
println!(
" Coordinator: {} patterns, {} memories",
stats.total_patterns, stats.total_memories
);
for worker in &workers {
let stats = worker.get_stats().await;
println!(
" {}: {} patterns, confidence: {:.1}%",
worker.id(),
stats.total_patterns,
stats.avg_confidence * 100.0
);
}
println!("\n✨ Demo complete!\n");
Ok(())
}

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//! Tensor compression for efficient network transfer
//!
//! Uses LZ4 compression with optional quantization for vector data.
use crate::{Result, SwarmError};
use serde::{Deserialize, Serialize};
/// Compression level for tensor data
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum CompressionLevel {
/// No compression (fastest)
None,
/// Fast LZ4 compression (default)
Fast,
/// High compression ratio
High,
/// Quantize to 8-bit then compress
Quantized8,
/// Quantize to 4-bit then compress
Quantized4,
}
impl Default for CompressionLevel {
fn default() -> Self {
CompressionLevel::Fast
}
}
/// Tensor codec for compression/decompression
pub struct TensorCodec {
level: CompressionLevel,
}
impl TensorCodec {
/// Create new codec with default compression
pub fn new() -> Self {
Self {
level: CompressionLevel::Fast,
}
}
/// Create codec with specific compression level
pub fn with_level(level: CompressionLevel) -> Self {
Self { level }
}
/// Compress data
pub fn compress(&self, data: &[u8]) -> Result<Vec<u8>> {
match self.level {
CompressionLevel::None => Ok(data.to_vec()),
CompressionLevel::Fast | CompressionLevel::High => {
let compressed = lz4_flex::compress_prepend_size(data);
Ok(compressed)
}
CompressionLevel::Quantized8 | CompressionLevel::Quantized4 => {
// For quantized, just use LZ4 on the raw data
// Real implementation would quantize floats first
let compressed = lz4_flex::compress_prepend_size(data);
Ok(compressed)
}
}
}
/// Decompress data
pub fn decompress(&self, data: &[u8]) -> Result<Vec<u8>> {
match self.level {
CompressionLevel::None => Ok(data.to_vec()),
_ => {
lz4_flex::decompress_size_prepended(data)
.map_err(|e| SwarmError::Compression(e.to_string()))
}
}
}
/// Compress f32 tensor with quantization
pub fn compress_tensor(&self, tensor: &[f32]) -> Result<CompressedTensor> {
match self.level {
CompressionLevel::Quantized8 => {
let (quantized, scale, zero_point) = quantize_8bit(tensor);
let compressed = lz4_flex::compress_prepend_size(&quantized);
Ok(CompressedTensor {
data: compressed,
original_len: tensor.len(),
quantization: Some(QuantizationParams {
bits: 8,
scale,
zero_point,
}),
})
}
CompressionLevel::Quantized4 => {
let (quantized, scale, zero_point) = quantize_4bit(tensor);
let compressed = lz4_flex::compress_prepend_size(&quantized);
Ok(CompressedTensor {
data: compressed,
original_len: tensor.len(),
quantization: Some(QuantizationParams {
bits: 4,
scale,
zero_point,
}),
})
}
_ => {
// No quantization, just compress raw bytes
let bytes: Vec<u8> = tensor
.iter()
.flat_map(|f| f.to_le_bytes())
.collect();
let compressed = self.compress(&bytes)?;
Ok(CompressedTensor {
data: compressed,
original_len: tensor.len(),
quantization: None,
})
}
}
}
/// Decompress tensor back to f32
pub fn decompress_tensor(&self, compressed: &CompressedTensor) -> Result<Vec<f32>> {
let decompressed = lz4_flex::decompress_size_prepended(&compressed.data)
.map_err(|e| SwarmError::Compression(e.to_string()))?;
match &compressed.quantization {
Some(params) if params.bits == 8 => {
Ok(dequantize_8bit(&decompressed, params.scale, params.zero_point))
}
Some(params) if params.bits == 4 => {
Ok(dequantize_4bit(&decompressed, compressed.original_len, params.scale, params.zero_point))
}
_ => {
// Raw f32 bytes
let tensor: Vec<f32> = decompressed
.chunks_exact(4)
.map(|c| f32::from_le_bytes([c[0], c[1], c[2], c[3]]))
.collect();
Ok(tensor)
}
}
}
/// Get compression ratio estimate for level
pub fn estimated_ratio(&self) -> f32 {
match self.level {
CompressionLevel::None => 1.0,
CompressionLevel::Fast => 0.5,
CompressionLevel::High => 0.3,
CompressionLevel::Quantized8 => 0.15,
CompressionLevel::Quantized4 => 0.08,
}
}
}
impl Default for TensorCodec {
fn default() -> Self {
Self::new()
}
}
/// Compressed tensor with metadata
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct CompressedTensor {
pub data: Vec<u8>,
pub original_len: usize,
pub quantization: Option<QuantizationParams>,
}
/// Quantization parameters for dequantization
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct QuantizationParams {
pub bits: u8,
pub scale: f32,
pub zero_point: f32,
}
/// Quantize f32 to 8-bit
fn quantize_8bit(tensor: &[f32]) -> (Vec<u8>, f32, f32) {
if tensor.is_empty() {
return (vec![], 1.0, 0.0);
}
let min_val = tensor.iter().cloned().fold(f32::INFINITY, f32::min);
let max_val = tensor.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
let scale = (max_val - min_val) / 255.0;
let zero_point = min_val;
let quantized: Vec<u8> = tensor
.iter()
.map(|&v| {
if scale == 0.0 {
0u8
} else {
((v - zero_point) / scale).clamp(0.0, 255.0) as u8
}
})
.collect();
(quantized, scale, zero_point)
}
/// Dequantize 8-bit back to f32
fn dequantize_8bit(quantized: &[u8], scale: f32, zero_point: f32) -> Vec<f32> {
quantized
.iter()
.map(|&q| (q as f32) * scale + zero_point)
.collect()
}
/// Quantize f32 to 4-bit (packed, 2 values per byte)
fn quantize_4bit(tensor: &[f32]) -> (Vec<u8>, f32, f32) {
if tensor.is_empty() {
return (vec![], 1.0, 0.0);
}
let min_val = tensor.iter().cloned().fold(f32::INFINITY, f32::min);
let max_val = tensor.iter().cloned().fold(f32::NEG_INFINITY, f32::max);
let scale = (max_val - min_val) / 15.0;
let zero_point = min_val;
// Pack two 4-bit values per byte
let mut packed = Vec::with_capacity((tensor.len() + 1) / 2);
for chunk in tensor.chunks(2) {
let v0 = if scale == 0.0 {
0u8
} else {
((chunk[0] - zero_point) / scale).clamp(0.0, 15.0) as u8
};
let v1 = if chunk.len() > 1 && scale != 0.0 {
((chunk[1] - zero_point) / scale).clamp(0.0, 15.0) as u8
} else {
0u8
};
packed.push((v0 << 4) | v1);
}
(packed, scale, zero_point)
}
/// Dequantize 4-bit back to f32
fn dequantize_4bit(packed: &[u8], original_len: usize, scale: f32, zero_point: f32) -> Vec<f32> {
let mut result = Vec::with_capacity(original_len);
for &byte in packed {
let v0 = (byte >> 4) as f32 * scale + zero_point;
let v1 = (byte & 0x0F) as f32 * scale + zero_point;
result.push(v0);
if result.len() < original_len {
result.push(v1);
}
}
result
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_lz4_compression() {
let codec = TensorCodec::with_level(CompressionLevel::Fast);
let data = b"Hello, RuVector Edge! This is test data for compression.";
let compressed = codec.compress(data).unwrap();
let decompressed = codec.decompress(&compressed).unwrap();
assert_eq!(decompressed, data);
}
#[test]
fn test_8bit_quantization() {
let codec = TensorCodec::with_level(CompressionLevel::Quantized8);
let tensor: Vec<f32> = (0..100).map(|i| i as f32 / 100.0).collect();
let compressed = codec.compress_tensor(&tensor).unwrap();
let decompressed = codec.decompress_tensor(&compressed).unwrap();
// Check approximate equality (quantization introduces small errors)
for (orig, dec) in tensor.iter().zip(decompressed.iter()) {
assert!((orig - dec).abs() < 0.01);
}
}
#[test]
fn test_4bit_quantization() {
let codec = TensorCodec::with_level(CompressionLevel::Quantized4);
let tensor: Vec<f32> = (0..100).map(|i| i as f32 / 100.0).collect();
let compressed = codec.compress_tensor(&tensor).unwrap();
let decompressed = codec.decompress_tensor(&compressed).unwrap();
assert_eq!(decompressed.len(), tensor.len());
// 4-bit has more error, but should be within bounds
for (orig, dec) in tensor.iter().zip(decompressed.iter()) {
assert!((orig - dec).abs() < 0.1);
}
}
}

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//! Distributed intelligence synchronization
//!
//! Sync Q-learning patterns, trajectories, and learning state across swarm agents.
use crate::{Result, SwarmError, compression::TensorCodec};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
/// Learning pattern with Q-value
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Pattern {
pub state: String,
pub action: String,
pub q_value: f64,
pub visits: u64,
pub last_update: u64,
pub confidence: f64,
}
impl Pattern {
pub fn new(state: &str, action: &str) -> Self {
Self {
state: state.to_string(),
action: action.to_string(),
q_value: 0.0,
visits: 0,
last_update: 0,
confidence: 0.0,
}
}
/// Merge with another pattern (federated learning style)
pub fn merge(&mut self, other: &Pattern, weight: f64) {
let total_visits = self.visits + other.visits;
if total_visits > 0 {
// Weighted average based on visits
let self_weight = self.visits as f64 / total_visits as f64;
let other_weight = other.visits as f64 / total_visits as f64;
self.q_value = self.q_value * self_weight + other.q_value * other_weight * weight;
self.visits = total_visits;
self.confidence = (self.confidence + other.confidence * weight) / 2.0;
}
}
}
/// Learning trajectory for decision transformer
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct Trajectory {
pub id: String,
pub steps: Vec<TrajectoryStep>,
pub total_reward: f64,
pub success: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TrajectoryStep {
pub state: String,
pub action: String,
pub reward: f64,
pub timestamp: u64,
}
/// Complete learning state for sync
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct LearningState {
pub agent_id: String,
pub patterns: HashMap<String, Pattern>,
pub trajectories: Vec<Trajectory>,
pub algorithm_stats: HashMap<String, AlgorithmStats>,
pub version: u64,
pub timestamp: u64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AlgorithmStats {
pub algorithm: String,
pub updates: u64,
pub avg_reward: f64,
pub convergence: f64,
}
impl Default for LearningState {
fn default() -> Self {
Self {
agent_id: String::new(),
patterns: HashMap::new(),
trajectories: Vec::new(),
algorithm_stats: HashMap::new(),
version: 0,
timestamp: chrono::Utc::now().timestamp_millis() as u64,
}
}
}
/// Intelligence synchronization manager
pub struct IntelligenceSync {
local_state: Arc<RwLock<LearningState>>,
peer_states: Arc<RwLock<HashMap<String, LearningState>>>,
codec: TensorCodec,
merge_threshold: f64,
}
impl IntelligenceSync {
/// Create new intelligence sync manager
pub fn new(agent_id: &str) -> Self {
let mut state = LearningState::default();
state.agent_id = agent_id.to_string();
Self {
local_state: Arc::new(RwLock::new(state)),
peer_states: Arc::new(RwLock::new(HashMap::new())),
codec: TensorCodec::new(),
merge_threshold: 0.1, // Only merge if delta > 10%
}
}
/// Get local learning state
pub async fn get_state(&self) -> LearningState {
self.local_state.read().await.clone()
}
/// Update local pattern
pub async fn update_pattern(&self, state: &str, action: &str, reward: f64) {
let mut local = self.local_state.write().await;
let key = format!("{}|{}", state, action);
let pattern = local.patterns.entry(key).or_insert_with(|| Pattern::new(state, action));
// Q-learning update
let alpha = 0.1;
pattern.q_value = pattern.q_value + alpha * (reward - pattern.q_value);
pattern.visits += 1;
pattern.last_update = chrono::Utc::now().timestamp_millis() as u64;
pattern.confidence = 1.0 - (1.0 / (pattern.visits as f64 + 1.0));
local.version += 1;
}
/// Serialize state for network transfer
pub async fn serialize_state(&self) -> Result<Vec<u8>> {
let state = self.local_state.read().await;
let json = serde_json::to_vec(&*state)
.map_err(|e| SwarmError::Serialization(e.to_string()))?;
// Compress for transfer
self.codec.compress(&json)
}
/// Deserialize and merge peer state
pub async fn merge_peer_state(&self, peer_id: &str, data: &[u8]) -> Result<MergeResult> {
// Decompress
let json = self.codec.decompress(data)?;
let peer_state: LearningState = serde_json::from_slice(&json)
.map_err(|e| SwarmError::Serialization(e.to_string()))?;
// Store peer state
{
let mut peers = self.peer_states.write().await;
peers.insert(peer_id.to_string(), peer_state.clone());
}
// Merge patterns
let mut local = self.local_state.write().await;
let mut merged_count = 0;
let mut new_count = 0;
for (key, peer_pattern) in &peer_state.patterns {
if let Some(local_pattern) = local.patterns.get_mut(key) {
// Merge existing pattern
let delta = (peer_pattern.q_value - local_pattern.q_value).abs();
if delta > self.merge_threshold {
local_pattern.merge(peer_pattern, 0.5);
merged_count += 1;
}
} else {
// New pattern from peer
local.patterns.insert(key.clone(), peer_pattern.clone());
new_count += 1;
}
}
local.version += 1;
Ok(MergeResult {
peer_id: peer_id.to_string(),
merged_patterns: merged_count,
new_patterns: new_count,
local_version: local.version,
})
}
/// Get best action for state using aggregated knowledge
pub async fn get_best_action(&self, state: &str, actions: &[String]) -> Option<(String, f64)> {
let local = self.local_state.read().await;
let mut best_action = None;
let mut best_q = f64::NEG_INFINITY;
for action in actions {
let key = format!("{}|{}", state, action);
if let Some(pattern) = local.patterns.get(&key) {
if pattern.q_value > best_q {
best_q = pattern.q_value;
best_action = Some((action.clone(), pattern.confidence));
}
}
}
best_action
}
/// Get sync delta (only changed patterns since version)
pub async fn get_delta(&self, since_version: u64) -> LearningState {
let local = self.local_state.read().await;
let mut delta = LearningState {
agent_id: local.agent_id.clone(),
version: local.version,
timestamp: chrono::Utc::now().timestamp_millis() as u64,
..Default::default()
};
// Only include patterns updated since version
for (key, pattern) in &local.patterns {
if pattern.last_update > since_version {
delta.patterns.insert(key.clone(), pattern.clone());
}
}
delta
}
/// Get aggregated stats across all peers
pub async fn get_swarm_stats(&self) -> SwarmStats {
let local = self.local_state.read().await;
let peers = self.peer_states.read().await;
let mut total_patterns = local.patterns.len();
let mut total_visits = 0u64;
let mut avg_confidence = 0.0;
for pattern in local.patterns.values() {
total_visits += pattern.visits;
avg_confidence += pattern.confidence;
}
for peer in peers.values() {
total_patterns += peer.patterns.len();
}
let pattern_count = local.patterns.len();
if pattern_count > 0 {
avg_confidence /= pattern_count as f64;
}
SwarmStats {
total_agents: peers.len() + 1,
total_patterns,
total_visits,
avg_confidence,
local_version: local.version,
}
}
}
/// Result of merging peer state
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MergeResult {
pub peer_id: String,
pub merged_patterns: usize,
pub new_patterns: usize,
pub local_version: u64,
}
/// Aggregated swarm statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SwarmStats {
pub total_agents: usize,
pub total_patterns: usize,
pub total_visits: u64,
pub avg_confidence: f64,
pub local_version: u64,
}
#[cfg(test)]
mod tests {
use super::*;
#[tokio::test]
async fn test_pattern_update() {
let sync = IntelligenceSync::new("test-agent");
sync.update_pattern("edit_ts", "coder", 0.8).await;
sync.update_pattern("edit_ts", "coder", 0.9).await;
let state = sync.get_state().await;
let pattern = state.patterns.get("edit_ts|coder").unwrap();
assert!(pattern.q_value > 0.0);
assert_eq!(pattern.visits, 2);
}
#[tokio::test]
async fn test_best_action() {
let sync = IntelligenceSync::new("test-agent");
sync.update_pattern("edit_ts", "coder", 0.5).await;
sync.update_pattern("edit_ts", "reviewer", 0.9).await;
let actions = vec!["coder".to_string(), "reviewer".to_string()];
let best = sync.get_best_action("edit_ts", &actions).await;
assert!(best.is_some());
assert_eq!(best.unwrap().0, "reviewer");
}
}

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//! # RuVector Edge - Distributed AI Swarm Communication
//!
//! Edge AI swarm communication using `ruv-swarm-transport` with RuVector intelligence.
//!
//! ## Features
//!
//! - **WebSocket Transport**: Remote swarm communication
//! - **SharedMemory Transport**: High-performance local IPC
//! - **WASM Support**: Run in browser/edge environments
//! - **Intelligence Sync**: Distributed Q-learning across agents
//! - **Memory Sharing**: Shared vector memory for RAG
//! - **Tensor Compression**: Efficient pattern transfer
//!
//! ## Quick Start
//!
//! ```rust,no_run
//! use ruvector_edge::{SwarmAgent, SwarmConfig, Transport};
//!
//! #[tokio::main]
//! async fn main() {
//! let config = SwarmConfig::default()
//! .with_transport(Transport::WebSocket)
//! .with_agent_id("agent-001");
//!
//! let agent = SwarmAgent::new(config).await.unwrap();
//! agent.join_swarm("ws://coordinator:8080").await.unwrap();
//!
//! // Sync learning patterns
//! agent.sync_patterns().await.unwrap();
//! }
//! ```
pub mod transport;
pub mod intelligence;
pub mod memory;
pub mod compression;
pub mod protocol;
pub mod agent;
// Re-exports
pub use agent::{SwarmAgent, AgentRole};
pub use transport::{Transport, TransportConfig};
pub use intelligence::{IntelligenceSync, LearningState, Pattern};
pub use memory::{SharedMemory, VectorMemory};
pub use compression::{TensorCodec, CompressionLevel};
pub use protocol::{SwarmMessage, MessageType};
use serde::{Deserialize, Serialize};
use uuid::Uuid;
/// Swarm configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SwarmConfig {
pub agent_id: String,
pub agent_role: AgentRole,
pub transport: Transport,
pub coordinator_url: Option<String>,
pub sync_interval_ms: u64,
pub compression_level: CompressionLevel,
pub max_peers: usize,
pub enable_learning: bool,
pub enable_memory_sync: bool,
}
impl Default for SwarmConfig {
fn default() -> Self {
Self {
agent_id: Uuid::new_v4().to_string(),
agent_role: AgentRole::Worker,
transport: Transport::WebSocket,
coordinator_url: None,
sync_interval_ms: 1000,
compression_level: CompressionLevel::Fast,
max_peers: 100,
enable_learning: true,
enable_memory_sync: true,
}
}
}
impl SwarmConfig {
pub fn with_transport(mut self, transport: Transport) -> Self {
self.transport = transport;
self
}
pub fn with_agent_id(mut self, id: impl Into<String>) -> Self {
self.agent_id = id.into();
self
}
pub fn with_role(mut self, role: AgentRole) -> Self {
self.agent_role = role;
self
}
pub fn with_coordinator(mut self, url: impl Into<String>) -> Self {
self.coordinator_url = Some(url.into());
self
}
}
/// Error types for edge swarm operations
#[derive(Debug, thiserror::Error)]
pub enum SwarmError {
#[error("Transport error: {0}")]
Transport(String),
#[error("Connection failed: {0}")]
Connection(String),
#[error("Serialization error: {0}")]
Serialization(String),
#[error("Compression error: {0}")]
Compression(String),
#[error("Sync error: {0}")]
Sync(String),
#[error("Agent not found: {0}")]
AgentNotFound(String),
#[error("Configuration error: {0}")]
Config(String),
}
pub type Result<T> = std::result::Result<T, SwarmError>;
/// Prelude for convenient imports
pub mod prelude {
pub use crate::{
SwarmAgent, SwarmConfig, SwarmError, Result,
Transport, AgentRole, MessageType,
IntelligenceSync, SharedMemory,
CompressionLevel,
};
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_config_builder() {
let config = SwarmConfig::default()
.with_agent_id("test-agent")
.with_transport(Transport::SharedMemory)
.with_role(AgentRole::Coordinator);
assert_eq!(config.agent_id, "test-agent");
assert!(matches!(config.transport, Transport::SharedMemory));
assert!(matches!(config.agent_role, AgentRole::Coordinator));
}
}

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//! Shared vector memory for distributed RAG
//!
//! Enables agents to share vector embeddings and semantic memories across the swarm.
use crate::{Result, SwarmError, compression::TensorCodec};
use serde::{Deserialize, Serialize};
use std::collections::HashMap;
use std::sync::Arc;
use tokio::sync::RwLock;
/// Vector memory entry
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct VectorEntry {
pub id: String,
pub content: String,
pub embedding: Vec<f32>,
pub metadata: HashMap<String, String>,
pub timestamp: u64,
pub owner_agent: String,
pub access_count: u64,
}
impl VectorEntry {
pub fn new(id: &str, content: &str, embedding: Vec<f32>, owner: &str) -> Self {
Self {
id: id.to_string(),
content: content.to_string(),
embedding,
metadata: HashMap::new(),
timestamp: chrono::Utc::now().timestamp_millis() as u64,
owner_agent: owner.to_string(),
access_count: 0,
}
}
/// Compute cosine similarity with query vector
pub fn similarity(&self, query: &[f32]) -> f32 {
if self.embedding.len() != query.len() {
return 0.0;
}
let mut dot = 0.0f32;
let mut norm_a = 0.0f32;
let mut norm_b = 0.0f32;
for (a, b) in self.embedding.iter().zip(query.iter()) {
dot += a * b;
norm_a += a * a;
norm_b += b * b;
}
if norm_a == 0.0 || norm_b == 0.0 {
return 0.0;
}
dot / (norm_a.sqrt() * norm_b.sqrt())
}
}
/// Shared vector memory across swarm
pub struct VectorMemory {
entries: Arc<RwLock<HashMap<String, VectorEntry>>>,
agent_id: String,
max_entries: usize,
codec: TensorCodec,
}
impl VectorMemory {
/// Create new vector memory
pub fn new(agent_id: &str, max_entries: usize) -> Self {
Self {
entries: Arc::new(RwLock::new(HashMap::new())),
agent_id: agent_id.to_string(),
max_entries,
codec: TensorCodec::new(),
}
}
/// Store a vector entry
pub async fn store(&self, content: &str, embedding: Vec<f32>) -> Result<String> {
let id = uuid::Uuid::new_v4().to_string();
let entry = VectorEntry::new(&id, content, embedding, &self.agent_id);
let mut entries = self.entries.write().await;
// Evict oldest if at capacity
if entries.len() >= self.max_entries {
if let Some(oldest_id) = entries
.iter()
.min_by_key(|(_, e)| e.timestamp)
.map(|(id, _)| id.clone())
{
entries.remove(&oldest_id);
}
}
entries.insert(id.clone(), entry);
Ok(id)
}
/// Search for similar vectors
pub async fn search(&self, query: &[f32], top_k: usize) -> Vec<(VectorEntry, f32)> {
let mut entries = self.entries.write().await;
let mut results: Vec<_> = entries
.values_mut()
.map(|entry| {
entry.access_count += 1;
let score = entry.similarity(query);
(entry.clone(), score)
})
.collect();
results.sort_by(|a, b| b.1.partial_cmp(&a.1).unwrap_or(std::cmp::Ordering::Equal));
results.truncate(top_k);
results
}
/// Get entry by ID
pub async fn get(&self, id: &str) -> Option<VectorEntry> {
let mut entries = self.entries.write().await;
if let Some(entry) = entries.get_mut(id) {
entry.access_count += 1;
Some(entry.clone())
} else {
None
}
}
/// Delete entry
pub async fn delete(&self, id: &str) -> bool {
let mut entries = self.entries.write().await;
entries.remove(id).is_some()
}
/// Serialize all entries for sync
pub async fn serialize(&self) -> Result<Vec<u8>> {
let entries = self.entries.read().await;
let data: Vec<_> = entries.values().cloned().collect();
let json = serde_json::to_vec(&data)
.map_err(|e| SwarmError::Serialization(e.to_string()))?;
self.codec.compress(&json)
}
/// Merge entries from peer
pub async fn merge(&self, data: &[u8]) -> Result<usize> {
let json = self.codec.decompress(data)?;
let peer_entries: Vec<VectorEntry> = serde_json::from_slice(&json)
.map_err(|e| SwarmError::Serialization(e.to_string()))?;
let mut entries = self.entries.write().await;
let mut merged = 0;
for entry in peer_entries {
if !entries.contains_key(&entry.id) {
if entries.len() < self.max_entries {
entries.insert(entry.id.clone(), entry);
merged += 1;
}
}
}
Ok(merged)
}
/// Get memory stats
pub async fn stats(&self) -> MemoryStats {
let entries = self.entries.read().await;
let total_vectors = entries.len();
let total_dims: usize = entries.values().map(|e| e.embedding.len()).sum();
let avg_dims = if total_vectors > 0 {
total_dims / total_vectors
} else {
0
};
let total_accesses: u64 = entries.values().map(|e| e.access_count).sum();
MemoryStats {
total_entries: total_vectors,
avg_dimensions: avg_dims,
total_accesses,
memory_bytes: total_dims * 4, // f32 = 4 bytes
}
}
}
/// Memory statistics
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoryStats {
pub total_entries: usize,
pub avg_dimensions: usize,
pub total_accesses: u64,
pub memory_bytes: usize,
}
/// Shared memory segment for high-performance local IPC
pub struct SharedMemory {
name: String,
size: usize,
// In real implementation, this would use mmap or shared memory
buffer: Arc<RwLock<Vec<u8>>>,
}
impl SharedMemory {
/// Create or attach to shared memory segment
pub fn new(name: &str, size: usize) -> Result<Self> {
Ok(Self {
name: name.to_string(),
size,
buffer: Arc::new(RwLock::new(vec![0u8; size])),
})
}
/// Write data at offset
pub async fn write(&self, offset: usize, data: &[u8]) -> Result<()> {
let mut buffer = self.buffer.write().await;
if offset + data.len() > self.size {
return Err(SwarmError::Transport("Buffer overflow".into()));
}
buffer[offset..offset + data.len()].copy_from_slice(data);
Ok(())
}
/// Read data at offset
pub async fn read(&self, offset: usize, len: usize) -> Result<Vec<u8>> {
let buffer = self.buffer.read().await;
if offset + len > self.size {
return Err(SwarmError::Transport("Buffer underflow".into()));
}
Ok(buffer[offset..offset + len].to_vec())
}
/// Get segment info
pub fn info(&self) -> SharedMemoryInfo {
SharedMemoryInfo {
name: self.name.clone(),
size: self.size,
}
}
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SharedMemoryInfo {
pub name: String,
pub size: usize,
}
#[cfg(test)]
mod tests {
use super::*;
#[tokio::test]
async fn test_vector_memory() {
let memory = VectorMemory::new("test-agent", 100);
let embedding = vec![0.1, 0.2, 0.3, 0.4];
let id = memory.store("test content", embedding.clone()).await.unwrap();
let results = memory.search(&embedding, 5).await;
assert!(!results.is_empty());
assert!(results[0].1 > 0.99); // Should be almost identical
let entry = memory.get(&id).await;
assert!(entry.is_some());
assert_eq!(entry.unwrap().content, "test content");
}
#[tokio::test]
async fn test_shared_memory() {
let shm = SharedMemory::new("test-segment", 1024).unwrap();
let data = b"Hello, Swarm!";
shm.write(0, data).await.unwrap();
let read = shm.read(0, data.len()).await.unwrap();
assert_eq!(read, data);
}
}

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//! Swarm communication protocol
//!
//! Defines message types and serialization for agent communication.
use crate::intelligence::LearningState;
use serde::{Deserialize, Serialize};
use uuid::Uuid;
/// Message types for swarm communication
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum MessageType {
/// Agent joining the swarm
Join,
/// Agent leaving the swarm
Leave,
/// Heartbeat/ping
Ping,
/// Heartbeat response
Pong,
/// Sync learning patterns
SyncPatterns,
/// Request patterns from peer
RequestPatterns,
/// Sync vector memories
SyncMemories,
/// Request memories from peer
RequestMemories,
/// Broadcast task to swarm
BroadcastTask,
/// Task result
TaskResult,
/// Coordinator election
Election,
/// Coordinator announcement
Coordinator,
/// Error message
Error,
}
/// Swarm message envelope
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct SwarmMessage {
pub id: String,
pub message_type: MessageType,
pub sender_id: String,
pub recipient_id: Option<String>, // None = broadcast
pub payload: MessagePayload,
pub timestamp: u64,
pub ttl: u32, // Time-to-live in hops
}
impl SwarmMessage {
/// Create new message
pub fn new(message_type: MessageType, sender_id: &str, payload: MessagePayload) -> Self {
Self {
id: Uuid::new_v4().to_string(),
message_type,
sender_id: sender_id.to_string(),
recipient_id: None,
payload,
timestamp: chrono::Utc::now().timestamp_millis() as u64,
ttl: 10,
}
}
/// Create directed message
pub fn directed(
message_type: MessageType,
sender_id: &str,
recipient_id: &str,
payload: MessagePayload,
) -> Self {
Self {
id: Uuid::new_v4().to_string(),
message_type,
sender_id: sender_id.to_string(),
recipient_id: Some(recipient_id.to_string()),
payload,
timestamp: chrono::Utc::now().timestamp_millis() as u64,
ttl: 10,
}
}
/// Create join message
pub fn join(agent_id: &str, role: &str, capabilities: Vec<String>) -> Self {
Self::new(
MessageType::Join,
agent_id,
MessagePayload::Join(JoinPayload {
agent_role: role.to_string(),
capabilities,
version: env!("CARGO_PKG_VERSION").to_string(),
}),
)
}
/// Create leave message
pub fn leave(agent_id: &str) -> Self {
Self::new(MessageType::Leave, agent_id, MessagePayload::Empty)
}
/// Create ping message
pub fn ping(agent_id: &str) -> Self {
Self::new(MessageType::Ping, agent_id, MessagePayload::Empty)
}
/// Create pong response
pub fn pong(agent_id: &str) -> Self {
Self::new(MessageType::Pong, agent_id, MessagePayload::Empty)
}
/// Create pattern sync message
pub fn sync_patterns(agent_id: &str, state: LearningState) -> Self {
Self::new(
MessageType::SyncPatterns,
agent_id,
MessagePayload::Patterns(PatternsPayload {
state,
compressed: false,
}),
)
}
/// Create pattern request message
pub fn request_patterns(agent_id: &str, since_version: u64) -> Self {
Self::new(
MessageType::RequestPatterns,
agent_id,
MessagePayload::Request(RequestPayload {
since_version,
max_entries: 1000,
}),
)
}
/// Create task broadcast message
pub fn broadcast_task(agent_id: &str, task: TaskPayload) -> Self {
Self::new(MessageType::BroadcastTask, agent_id, MessagePayload::Task(task))
}
/// Create error message
pub fn error(agent_id: &str, error: &str) -> Self {
Self::new(
MessageType::Error,
agent_id,
MessagePayload::Error(ErrorPayload {
code: "ERROR".to_string(),
message: error.to_string(),
}),
)
}
/// Serialize to bytes
pub fn to_bytes(&self) -> Result<Vec<u8>, serde_json::Error> {
serde_json::to_vec(self)
}
/// Deserialize from bytes
pub fn from_bytes(data: &[u8]) -> Result<Self, serde_json::Error> {
serde_json::from_slice(data)
}
/// Check if message is expired (based on timestamp)
pub fn is_expired(&self, max_age_ms: u64) -> bool {
let now = chrono::Utc::now().timestamp_millis() as u64;
now - self.timestamp > max_age_ms
}
/// Decrement TTL for forwarding
pub fn decrement_ttl(&mut self) -> bool {
if self.ttl > 0 {
self.ttl -= 1;
true
} else {
false
}
}
}
/// Message payload variants
#[derive(Debug, Clone, Serialize, Deserialize)]
#[serde(tag = "type")]
pub enum MessagePayload {
Empty,
Join(JoinPayload),
Patterns(PatternsPayload),
Memories(MemoriesPayload),
Request(RequestPayload),
Task(TaskPayload),
TaskResult(TaskResultPayload),
Election(ElectionPayload),
Error(ErrorPayload),
Raw(Vec<u8>),
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct JoinPayload {
pub agent_role: String,
pub capabilities: Vec<String>,
pub version: String,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct PatternsPayload {
pub state: LearningState,
pub compressed: bool,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct MemoriesPayload {
pub entries: Vec<u8>, // Compressed vector entries
pub count: usize,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct RequestPayload {
pub since_version: u64,
pub max_entries: usize,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TaskPayload {
pub task_id: String,
pub task_type: String,
pub description: String,
pub parameters: serde_json::Value,
pub priority: u8,
pub timeout_ms: u64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TaskResultPayload {
pub task_id: String,
pub success: bool,
pub result: serde_json::Value,
pub execution_time_ms: u64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ElectionPayload {
pub candidate_id: String,
pub priority: u64,
pub term: u64,
}
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct ErrorPayload {
pub code: String,
pub message: String,
}
#[cfg(test)]
mod tests {
use super::*;
#[test]
fn test_message_serialization() {
let msg = SwarmMessage::join("agent-001", "worker", vec!["compute".to_string()]);
let bytes = msg.to_bytes().unwrap();
let decoded = SwarmMessage::from_bytes(&bytes).unwrap();
assert_eq!(decoded.sender_id, "agent-001");
assert!(matches!(decoded.message_type, MessageType::Join));
}
#[test]
fn test_ttl_decrement() {
let mut msg = SwarmMessage::ping("agent-001");
assert_eq!(msg.ttl, 10);
assert!(msg.decrement_ttl());
assert_eq!(msg.ttl, 9);
msg.ttl = 0;
assert!(!msg.decrement_ttl());
}
}

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//! Transport layer abstraction over ruv-swarm-transport
//!
//! Provides unified interface for WebSocket, SharedMemory, and WASM transports.
use crate::{Result, SwarmError};
use serde::{Deserialize, Serialize};
use std::sync::Arc;
use tokio::sync::{mpsc, RwLock};
/// Transport types supported
#[derive(Debug, Clone, Copy, PartialEq, Eq, Serialize, Deserialize)]
pub enum Transport {
/// WebSocket for remote communication
WebSocket,
/// SharedMemory for local high-performance IPC
SharedMemory,
/// WASM-compatible transport for browser
#[cfg(feature = "wasm")]
Wasm,
}
impl Default for Transport {
fn default() -> Self {
Transport::WebSocket
}
}
/// Transport configuration
#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct TransportConfig {
pub transport_type: Transport,
pub buffer_size: usize,
pub reconnect_interval_ms: u64,
pub max_message_size: usize,
pub enable_compression: bool,
}
impl Default for TransportConfig {
fn default() -> Self {
Self {
transport_type: Transport::WebSocket,
buffer_size: 1024,
reconnect_interval_ms: 5000,
max_message_size: 16 * 1024 * 1024, // 16MB
enable_compression: true,
}
}
}
/// Unified transport handle
pub struct TransportHandle {
pub(crate) transport_type: Transport,
pub(crate) sender: mpsc::Sender<Vec<u8>>,
pub(crate) receiver: Arc<RwLock<mpsc::Receiver<Vec<u8>>>>,
pub(crate) connected: Arc<RwLock<bool>>,
}
impl TransportHandle {
/// Create new transport handle
pub fn new(transport_type: Transport) -> Self {
let (tx, rx) = mpsc::channel(1024);
Self {
transport_type,
sender: tx,
receiver: Arc::new(RwLock::new(rx)),
connected: Arc::new(RwLock::new(false)),
}
}
/// Check if connected
pub async fn is_connected(&self) -> bool {
*self.connected.read().await
}
/// Send raw bytes
pub async fn send(&self, data: Vec<u8>) -> Result<()> {
self.sender
.send(data)
.await
.map_err(|e| SwarmError::Transport(e.to_string()))
}
/// Receive raw bytes
pub async fn recv(&self) -> Result<Vec<u8>> {
let mut rx = self.receiver.write().await;
rx.recv()
.await
.ok_or_else(|| SwarmError::Transport("Channel closed".into()))
}
}
/// WebSocket transport implementation
pub mod websocket {
use super::*;
/// WebSocket connection state
pub struct WebSocketTransport {
pub url: String,
pub handle: TransportHandle,
}
impl WebSocketTransport {
/// Connect to WebSocket server
pub async fn connect(url: &str) -> Result<Self> {
let handle = TransportHandle::new(Transport::WebSocket);
// In real implementation, use ruv-swarm-transport's WebSocket
// For now, create a mock connection
tracing::info!("Connecting to WebSocket: {}", url);
*handle.connected.write().await = true;
Ok(Self {
url: url.to_string(),
handle,
})
}
/// Send message
pub async fn send(&self, data: Vec<u8>) -> Result<()> {
self.handle.send(data).await
}
/// Receive message
pub async fn recv(&self) -> Result<Vec<u8>> {
self.handle.recv().await
}
}
}
/// SharedMemory transport for local IPC
pub mod shared_memory {
use super::*;
/// Shared memory segment
pub struct SharedMemoryTransport {
pub name: String,
pub size: usize,
pub handle: TransportHandle,
}
impl SharedMemoryTransport {
/// Create or attach to shared memory
pub fn new(name: &str, size: usize) -> Result<Self> {
let handle = TransportHandle::new(Transport::SharedMemory);
tracing::info!("Creating shared memory: {} ({}KB)", name, size / 1024);
Ok(Self {
name: name.to_string(),
size,
handle,
})
}
/// Write to shared memory
pub async fn write(&self, offset: usize, data: &[u8]) -> Result<()> {
if offset + data.len() > self.size {
return Err(SwarmError::Transport("Buffer overflow".into()));
}
self.handle.send(data.to_vec()).await
}
/// Read from shared memory
pub async fn read(&self, _offset: usize, _len: usize) -> Result<Vec<u8>> {
self.handle.recv().await
}
}
}
/// WASM-compatible transport
#[cfg(feature = "wasm")]
pub mod wasm_transport {
use super::*;
use wasm_bindgen::prelude::*;
/// WASM transport using BroadcastChannel or postMessage
#[wasm_bindgen]
pub struct WasmTransport {
channel_name: String,
handle: TransportHandle,
}
impl WasmTransport {
pub fn new(channel_name: &str) -> Result<Self> {
let handle = TransportHandle::new(Transport::Wasm);
Ok(Self {
channel_name: channel_name.to_string(),
handle,
})
}
pub async fn broadcast(&self, data: Vec<u8>) -> Result<()> {
self.handle.send(data).await
}
pub async fn receive(&self) -> Result<Vec<u8>> {
self.handle.recv().await
}
}
}
/// Transport factory
pub struct TransportFactory;
impl TransportFactory {
/// Create transport based on type
pub async fn create(config: &TransportConfig, url: Option<&str>) -> Result<TransportHandle> {
match config.transport_type {
Transport::WebSocket => {
let url = url.ok_or_else(|| SwarmError::Config("URL required for WebSocket".into()))?;
let ws = websocket::WebSocketTransport::connect(url).await?;
Ok(ws.handle)
}
Transport::SharedMemory => {
let shm = shared_memory::SharedMemoryTransport::new(
"ruvector-swarm",
config.buffer_size * 1024,
)?;
Ok(shm.handle)
}
#[cfg(feature = "wasm")]
Transport::Wasm => {
let wasm = wasm_transport::WasmTransport::new("ruvector-channel")?;
Ok(wasm.handle)
}
}
}
}