Async on Yarang's Tech Lair

ZeroClaw Agent Runtime: Implementing a TCP/IP Architecture for High-Performance Multi-Agent Communication

Fri, 26 Jun 2026 09:01:09 +0900

Hello! As the open-source AI and agent ecosystem rapidly expands, there’s a growing interest in “multi-agent systems” where multiple agents collaborate beyond a single LLM. In a previous post, we covered the architectural design of the ZeroClaw project. This time, we aim to introduce the specific implementation details of how the actual ZeroClaw runtime internally handles inter-agent communication and achieves high performance by leveraging Rust’s powerful asynchronous runtime.

Challenges with Existing Communication and ZeroClaw’s Approach

Typical MCP (Model Context Protocol) or basic agent systems primarily handle communication through in-memory sharing within a single process or simple local function calls. However, as systems become more complex and agents become distributed, the following problems arise:

Scalability Limitations: Single-process memory buffers are finite, and bottlenecks occur when hundreds of agents send and receive messages simultaneously.
Increased Coupling: Communication between agents is tightly coupled with logic, making it difficult to replace or update specific agents.
Network Separation: If the communication methods differ between local development environments and cloud deployment environments, code modifications are required.

To address these issues, ZeroClaw adopts a hybrid approach that combines “file-based architecture design” with a “TCP/IP networking layer.” Agents declaratively record their state in the file system, and actual message transmission is handled via asynchronous TCP sockets.

Asynchronous Server Structure Using Rust Tokio

The core of ZeroClaw is its high-performance networking layer built on Rust’s tokio runtime. Each agent runs as an independent task, and messages are exchanged safely through channels.

Here’s a simplified example of the message broker, which is central to the ZeroClaw runtime.

1. Defining Basic Message Structures

First, we define the data structures for messages exchanged between agents. serde is used to automate serialization.

use serde::{Deserialize, Serialize};
use std::collections::HashMap;

#[derive(Debug, Clone, Serialize, Deserialize)]
pub struct AgentMessage {
 pub source: String, // Sender agent ID
 pub target: String, // Receiver agent ID (or "broadcast")
 pub payload: PayloadType,
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum PayloadType {
 Text(String),
 Data(HashMap<String, String>), // Map for flexible data transfer
 Control(ControlSignal),
}

#[derive(Debug, Clone, Serialize, Deserialize)]
pub enum ControlSignal {
 Start,
 Stop,
 HeartBeat,
}

2. Implementing Asynchronous TCP Handlers

Actual communication occurs through tokio::net::TcpListener. Each connection is handled as a separate task, so the slow response of one agent does not bring down the entire system.

use tokio::net::{TcpListener, TcpStream};
use tokio::io::{AsyncReadExt, AsyncWriteExt};
use tokio::sync::mpsc;

#[derive(Clone)]
struct AgentConfig {
 id: String,
}

pub async fn run_agent_server(config: AgentConfig) -> Result<(), Box<dyn std::error::Error>> {
 let listener = TcpListener::bind("127.0.0.1:8080").await?;
 println!("[{}] Agent Server listening on 127.0.0.1:8080", config.id);

 // Channel for message routing (a more complex router would be used in actual implementation)
 let (tx, mut rx) = mpsc::channel::<AgentMessage>(1000);

 // Receiving task (spawned)
 let config_clone = config.clone();
 tokio::spawn(async move {
 while let Ok((mut socket, addr)) = listener.accept().await {
 println!("[{}] Connection from {}", config_clone.id, addr);
 let tx_clone = tx.clone();
 tokio::spawn(async move {
 let mut buf = [0; 1024];
 loop {
 let n = match socket.read(&mut buf).await {
 Ok(n) if n == 0 => return, // Connection closed
 Ok(n) => n,
 Err(e) => {
 eprintln!("Failed to read from socket; err = {:?}", e);
 return;
 }
 };

 // Message parsing (simplified logic)
 let received_data = &buf[..n];
 // Deserialization would be performed here in reality
 println!("Received: {:?}", String::from_utf8_lossy(received_data));
 
 // ... (Business logic processing) ...
 }
 });
 }
 });

 // Example sending task (periodic heartbeat)
 tokio::spawn(async move {
 let mut interval = tokio::time::interval(tokio::time::Duration::from_secs(5));
 loop {
 interval.tick().await {
 let hb = AgentMessage {
 source: config.id.clone(),
 target: "broadcast".to_string(),
 payload: PayloadType::Control(ControlSignal::HeartBeat),
 };
 // Would actually write to a socket or send externally
 println!("[{}] Sending Heartbeat...", config.id);
 }
 }
 });

 Ok(())
}

This code demonstrates the basic framework for a ZeroClaw agent to asynchronously receive requests from other agents and periodically report its status. Since each connection is executed independently via tokio::spawn, thousands of concurrent connections can be handled efficiently.

Team Agent Communication and Protocol Design

Beyond simple 1:1 communication, ZeroClaw aims for team-level communication. This connects with the previously discussed “Team Agent Communication Architecture.”

Pub/Sub Pattern: Messages are delivered in bulk to a group of agents subscribed to a specific topic.
Peer-to-Peer (P2P): Minimizes latency by enabling direct communication between agents without going through a central server.

ZeroClaw’s protocol is encapsulated and transmitted over the TCP layer using JSON or binary formats (like MessagePack for performance-critical scenarios). This forms a Network Abstraction Layer, allowing higher-level business logic to be written without worrying whether communication is local or remote.

Conclusion: Towards Scalable Agent Systems

ZeroClaw is being designed as a true distributed system, going beyond a simple LLM wrapper. By combining Rust’s memory safety and zero-cost abstractions with tokio’s powerful asynchronous processing capabilities, we are building a stable and fast agent runtime.

In the next post, we will debug the flow of how an LLM makes decisions and performs actual actions through MCP tools on top of this communication architecture.

Reference Code and Resources

ZeroClaw GitHub Repository (preparing for open-source)
The Garbage Collection Handbook: The Art of Automatic Memory Management (2nd Ed) - A classic and authoritative text on memory management, recently mentioned in the news.

Thank you for reading!

[ZeroClaw] Rust Channel-Based IPC Design for Asynchronous Agent Runtime

Thu, 11 Jun 2026 09:00:42 +0900

[ZeroClaw] Rust Channel-Based IPC Design for Asynchronous Agent Runtime

Hello. Recently, while enhancing the ‘Multi-Agent Architecture’, the core of the ZeroClaw project, I would like to share my experience building a high-performance asynchronous communication (IPC, Inter-Process Communication) environment that goes beyond simple message passing.

The previous synchronous communication method increased coupling between agents and became a bottleneck (Single Point of Failure) that could halt the entire system due to the failure of one agent. To address this, we designed an event-driven, loosely coupled architecture using Rust’s powerful concurrency feature, the tokio runtime, and MPSC (Multi-Producer, Single-Consumer) channels.

In this post, we will examine how channels were designed and implemented to maximize the efficiency of the agent runtime, along with actual code.

1. Problems with the Existing Architecture and the Need for Asynchronous Design

Until now, ZeroClaw agents primarily used the synchronous RPC pattern, requesting messages and waiting for responses. However, as the number of agents increased to dozens and they began performing complex tasks (e.g., analyzing file-based architectures, processing large-scale logs), the following problems arose:

Blocking Issue: While Agent A waits for a response from Agent B, Agent A cannot perform other tasks.
Complex Error Propagation: When a specific agent crashed or timed out, it was complicated to propagate the error to agents higher up the call chain.

To solve this, we adopted an asynchronous pattern: “An agent sends a message and immediately performs other tasks. The processed result is received as an event.”

2. Event Loop Structure Using Rust’s `tokio::sync::mpsc`

The MPSC channel provided by Rust’s tokio crate ensures high throughput and low latency, making it ideal for agent runtimes. Each agent has its own Task (unit of work), which executes independently via tokio::spawn.

Core Design Points

Message Bus: Each agent holds a sender (tx) and a receiver (rx).
Event Loop: The receiver (rx) runs in an infinite loop (loop), asynchronously waiting (recv()) until a message arrives.

3. Practical Code: A Concrete Implementation Example

Now, let’s write code that demonstrates how agents communicate within the ZeroClaw runtime.

Step 1: Define the Message Protocol

First, we need to define the data structures that will be exchanged between agents. It is recommended to use an Enum to manage message types safely.

// Cargo.toml dependencies
// [dependencies]
// tokio = { version = "1", features = ["full"] }
// serde = { version = "1", features = ["derive"] }

use tokio::sync::mpsc;
use serde::{Serialize, Deserialize};

#[derive(Debug, Serialize, Deserialize)]
enum AgentMessage {
 TaskAssigned { task_id: String, description: String },
 TaskCompleted { task_id: String, result: String },
 StatusCheck,
}

Step 2: Implement Agent Struct and Runner

Each agent has a unique ID and a receiver (rx). The run method is the core function that manages the agent’s lifecycle.

struct Agent {
 id: String,
 rx: mpsc::Receiver<AgentMessage>,
}

impl Agent {
 fn new(id: String, rx: mpsc::Receiver<AgentMessage>) -> Self {
 Self { id, rx }
 }

 async fn run(mut self) {
 println!("[{}] Agent started. Listening for messages...", self.id);
 
 // Asynchronously wait for message reception
 while let Some(msg) = self.rx.recv().await {
 match msg {
 AgentMessage::TaskAssigned { task_id, description } => {
 println!("[{}] Received task: {} - {}", self.id, task_id, description);
 // Actual logic processing (e.g., file analysis, external API calls)
 // Here, for example, we assume it takes 1 second and then sends a completion message.
 }
 AgentMessage::StatusCheck => {
 println!("[{}] Status: Active", self.id);
 }
 _ => {}
 }
 }
 println!("[{}] Agent shutting down.", self.id);
 }
}

Step 3: Main Runtime and Channel Connection

In the main function, we create multiple agents, connect the channels, and then execute them in parallel using tokio::spawn.

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
 // 1. Create channel (capacity 32)
 let (tx, rx) = mpsc::channel::<AgentMessage>(32);

 // 2. Create and run agent (separated into asynchronous tasks)
 let agent_handle = {
 let agent = Agent::new("Agent-A".to_string(), rx);
 tokio::spawn(async move {
 agent.run().await;
 })
 };

 // 3. Send messages from the main runtime
 // Other agents or API servers can use this tx to send messages.
 
 // Assign task
 let _ = tx.send(AgentMessage::TaskAssigned {
 task_id: "T-101".to_string(),
 description: "Analyze server logs".to_string(),
 }).await;

 // Check status
 let _ = tx.send(AgentMessage::StatusCheck).await;

 // 4. Wait for the agent to complete its task (in a real environment, it would continue running)
 drop(tx); // Sender drops -> channel closes -> agent loop termination condition met
 
 agent_handle.await?;
 
 Ok(())
}

4. ZeroClaw Project Application Effects

As a result of applying the structure above to the ZeroClaw runtime, we achieved the following benefits:

Improved Parallel Processing Performance: Since agents run within their own tokio tasks, we were able to efficiently utilize CPU cores.
Reduced Coupling: The main logic doesn’t need to know the internal implementation of a specific agent; it simply calls tx.send.
Graceful Shutdown: By dropping tx, the channel closes, and the agent detects that no more messages will arrive, naturally exiting the while let loop and shutting down.

Conclusion

Rust’s ownership system and tokio’s asynchronous abstractions become powerful weapons in building multi-agent systems. In the next post, we will cover how these agents persist state when combined with a file-based architecture.

The development of ZeroClaw’s high-performance agent runtime continues.

MCP Server Performance Optimization: Building Ultra-Fast Processing Pipelines with Rust

Wed, 03 Jun 2026 09:00:50 +0900

MCP Server Performance Optimization: Building Ultra-Fast Processing Pipelines with Rust

As agent systems utilizing LLMs (Large Language Models) have become a hot topic in the development ecosystem, the importance of the supporting infrastructure, MCP (Model Context Protocol) servers, is growing. In a previous post, we discussed multi-agent architectures, mentioning high-performance runtimes like ZeroClaw. Today, we will focus on Rust-based optimization techniques to dramatically improve the Throughput and Latency of the MCP server itself.

Simply saying “it’s fast” is not enough. The key is how efficiently the server manages resources when thousands of Tool Calls arrive simultaneously. In particular, as the current trend of AI agents collects RSS feeds or real-time data, I/O bound operations often become a bottleneck.

This article will introduce concrete methods for eliminating bottlenecks and writing safe yet fast code by leveraging Tokio, Rust’s powerful asynchronous runtime.

1. Problem Definition: Single-Threaded Bottleneck

Basically, simple MCP servers written in Python or Node.js often rely on a single-threaded event loop. This is advantageous for I/O-heavy tasks, but it has clear limitations when processing MCP tools that involve data manipulation or complex logic, as it only utilizes one CPU core.

For example, when building an automated blog system, if CPU usage reaches 100% during the processing of large amounts of images or log parsing, other requests will pile up in the queue. We need to solve this with multi-threaded asynchronous processing.

2. Asynchronous Processing with Rust and Tokio

Rust can achieve both safety and performance through ‘Zero-cost abstractions’. Let’s parallelize the core logic of the MCP server, tool execution, using tokio::spawn.

Basic Setup (Cargo.toml)

First, add Tokio to your dependencies file.

[dependencies]
tokio = { version = "1", features = ["full"] }
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"

Asynchronous Handler Implementation Example

The following code shows a simple MCP server handler structure that processes incoming requests by separating them into distinct tasks.

use tokio::net::TcpListener;
use tokio::io::{AsyncReadExt, AsyncWriteExt};
use serde::{Deserialize, Serialize};
use std::sync::Arc;

// Define the MCP request message structure
#[derive(Debug, Deserialize)]
struct McpRequest {
 id: String,
 method: String,
 params: serde_json::Value,
}

#[derive(Debug, Serialize)]
struct McpResponse {
 id: String,
 result: serde_json::Value,
}

// Function to simulate heavy processing
async fn process_heavy_tool(params: serde_json::Value) -> Result<String, String> {
 // In a real environment, this would involve database lookups or file I/O
 // Simulate asynchronous waiting with tokio::time::sleep
 tokio::time::sleep(tokio::time::Duration::from_secs(2)).await;
 Ok(format!("Processed: {}", params))
}

#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
 let listener = TcpListener::bind("127.0.0.1:8080").await?;
 println!("MCP Server listening on {}", listener.local_addr()?);

 loop {
 let (mut socket, _) = listener.accept().await?;

 // Create a new task for each incoming connection (parallel processing)
 tokio::spawn(async move {
 let mut buf = [0; 1024];

 loop {
 let n = match socket.read(&mut buf).await {
 Ok(n) if n == 0 => return, // Connection closed
 Ok(n) => n,
 Err(e) => {
 eprintln!("Failed to read from socket; err = {:?}", e);
 return;
 }
 };

 let req_str = String::from_utf8_lossy(&buf[0..n]);
 
 // JSON parsing and processing logic (error handling omitted)
 if let Ok(req) = serde_json::from_str::<McpRequest>(&req_str) {
 let req_id = req.id.clone();
 
 // Core logic: spawn an asynchronous function for non-blocking processing
 let handle = tokio::spawn(async move {
 process_heavy_tool(req.params).await
 });

 // Wait for the result and respond (using channels is recommended in actual implementations)
 if let Ok(Ok(result)) = handle.await {
 let resp = McpResponse {
 id: req_id,
 result: serde_json::json!(result),
 };
 
 if let Ok(serialized) = serde_json::to_string(&resp) {
 let _ = socket.write_all(serialized.as_bytes()).await;
 }
 }
 }
 }
 });
 }
}

The core of this code is that tokio::spawn allows each request to execute independently without blocking the main loop.

3. Memory Optimization through Streaming

When processing large files or transferring logs, loading all data into memory (RAM) is fatal. Using Rust’s Stream allows you to process data in chunks, maintaining consistent memory usage.

This can be particularly useful in tasks like [blog-api-server] logging improvements or Cloud Monitor operations.

use futures::stream::{Stream, StreamExt};
use std::pin::Pin;

// Stream for generating virtual log data
fn log_stream() -> Pin<Box<dyn Stream<Item = String> + Send>> {
 Box::pin(async_stream::stream! {
 for i in 0..1000 {
 yield format!("Log entry #{}\n", i);
 tokio::time::sleep(tokio::time::Duration::from_millis(10)).await;
 }
 })
}

// Stream processing example
async fn process_logs() {
 let mut stream = log_stream();
 while let Some(log_entry) = stream.next().await {
 // Process line by line in real-time (write to file or send)
 println!("Processing: {}", log_entry);
 }
}

4. Conclusion: Towards ZeroClaw

The ZeroClaw runtime we aim for must provide asynchronous processing and memory safety as its core features. We need to go beyond simply porting existing Python scripts to Rust and actively leverage Tokio’s scheduling and Zero-copy serialization to withstand an environment where thousands of agents communicate concurrently.

In the next post, we will discuss how to design agent-to-agent communication protocols that operate on these high-performance servers.

High-Performance MCP Server Implemented with Rust: Asynchronous Messaging Using ZeroMQ

Sun, 10 May 2026 09:01:24 +0900

High-Performance MCP Server Implemented with Rust: Asynchronous Messaging Using ZeroMQ

Recently, while working on the ZeroClaw project, we identified a need to overcome the performance limitations of MCP (Model Context Protocol) servers. Traditional HTTP-based communication struggles to move beyond a single request-response pattern, and real-time communication between numerous LLM agents is prone to bottlenecks. In this post, we will cover the process of building an ultra-lightweight, ultra-fast MCP server by leveraging Rust’s powerful asynchronous processing capabilities and ZeroMQ (ØMQ).

Why ZeroMQ? (Comparison with TCP Socket Limitations)

In the previous blog-api-server architecture, we defined and used protocols directly using standard TCP streams. However, in a multi-agent environment, the following issues arise:

Connection Management Complexity: As the number of agents grows into the dozens, managing the Accept loop and socket states requires unsafe code or complex state machines.
Message Enveloping: TCP is a byte stream. We must implement Length-Prefixing directly to demarcate message boundaries, which is a common source of bugs.

ZeroMQ abstracts away the complexity of this lower socket layer while providing a faster “User Space” transport layer than TCP. Notably, using the ipc (Inter-Process Communication) protocol can completely eliminate network stack overhead for localhost communication.

Architecture Design: ZeroMQ PUB/SUB Pattern

For this implementation, we will use the Publish/Subscribe (PUB/SUB) pattern for loose coupling between agents. When one agent changes its state (Publishes), a message is immediately broadcast to other agents subscribing to that topic.

Key Dependencies (Cargo.toml)

In the Rust ecosystem, ZeroMQ is available through the zmq crate. While tokio-zmq can be used for integration with asynchronous runtimes, for pure performance, leveraging ZeroMQ’s poll functionality is often more stable.

[dependencies]
zmq = "0.10"
serde = { version = "1.0", features = ["derive"] }
serde_json = "1.0"
tokio = { version = "1", features = ["full"] }

Implementation: High-Performance MCP Router

The code below is an example implementation of an MCP Router that acts as a simple message broker. It serves as a central hub for multiple LLM agents (teams) to communicate with each other.

1. Define Message Protocol

First, let’s define the format of messages exchanged between agents. We ensure compatibility by using JSON serialization.

use serde::{Deserialize, Serialize};

#[derive(Debug, Serialize, Deserialize)]
struct McpMessage {
 pub source_id: String,
 pub target_topic: String,
 pub payload: String,
 pub timestamp: i64,
}

2. Asynchronous ZeroMQ Context and Socket Setup

ZeroMQ’s Context is thread-safe, so it’s common practice to create and share a single instance globally throughout the application.

use std::time::SystemTime;
use zmq::{Context, Socket, SocketType};

struct McpRouter {
 context: Context,
 frontend: Socket, // Agents connect here
 // Can add sockets for other patterns if needed
}

impl McpRouter {
 fn new() -> Result<Self, zmq::Error> {
 let context = Context::new();
 let frontend = context.socket(SocketType::SUB)?;
 
 // Set to receive all messages (no filtering)
 frontend.set_subscribe(b"")?;
 
 Ok(McpRouter { context, frontend })
 }

 fn start(&self, endpoint: &str) -> Result<(), zmq::Error> {
 self.frontend.bind(endpoint)?;
 println!("[ZeroClaw Router] Listening on {}", endpoint);

 let mut msg = zmq::Message::new();
 loop {
 // Can use poll for non-blocking receive
 // Here, a simple blocking receive example
 match self.frontend.recv(&mut msg) {
 Ok(_) => {
 let data = msg.as_str().unwrap();
 // Message parsing and routing logic
 if let Ok(mcp_msg) = serde_json::from_str::<McpMessage>(data) {
 self.route_message(mcp_msg);
 }
 }
 Err(e) => {
 eprintln!("Receive Error: {}", e);
 }
 }
 }
 }

 fn route_message(&self, msg: McpMessage) {
 // Implement actual routing logic
 println!("Routing from {} on topic {}", msg.source_id, msg.target_topic);
 // e.g., save to database, forward to another socket, etc.
 }
}

3. Agent Publisher Implementation

Now, let’s write the code for individual agents to send messages. When using tokio, you need to execute ZeroMQ’s blocking functions in a separate thread to avoid blocking the asynchronous runtime.

use std::thread;

fn spawn_publisher_agent(id: &str, endpoint: &str) {
 let context = Context::new();
 let sender = context.socket(SocketType::PUB).expect("Failed to create PUB socket");
 sender.connect(endpoint).expect("Failed to connect to Router");

 // Run in a separate thread to isolate from the Tokio runtime
 thread::spawn(move || {
 let counter = 0..100;
 for i in counter {
 let msg = McpMessage {
 source_id: id.to_string(),
 target_topic: "general".to_string(),
 payload: format!("Message #{} from agent {}", i, id),
 timestamp: SystemTime::now().duration_since(SystemTime::UNIX_EPOCH).unwrap().as_secs() as i64,
 };

 let json_str = serde_json::to_string(&msg).unwrap();
 // ZeroMQ automatically retries or queues sending if it fails (set High-water mark if needed)
 sender.send(json_str.as_bytes(), 0).expect("Failed to send");
 
 thread::sleep(std::time::Duration::from_millis(100));
 }
 });
}

Performance Optimization and IPC Utilization

The true power of a Rust ZeroMQ server is unleashed when using the IPC (Inter-Process Communication) transport. While TCP involves packets traversing the network stack and looping back, IPC uses Unix Domain Sockets or Windows Named Pipes for communication at a level close to memory copying.

To use the ipc protocol, change the endpoint as follows:

// Use IPC instead of TCP
let endpoint = "ipc:///tmp/mcp_router.ipc";
router.start(endpoint);

Just as modern languages like Bun and Zig gain attention for their system-level optimizations, the combination of Rust and ZeroMQ is one of the most powerful tools for building the “system bus” for LLM applications.

Conclusion

In this post, as part of the ZeroClaw project, we explored how to build a high-performance MCP server using Rust and ZeroMQ. Beyond simple HTTP requests, this architecture offers significant advantages in scalability and performance for environments requiring real-time messaging between agents.

In the next post, we will discuss the Circuit Breaker pattern and error handling strategies to add reliability to this messaging system.

Async on Yarang's Tech Lair

ZeroClaw Agent Runtime: Implementing a TCP/IP Architecture for High-Performance Multi-Agent Communication

Challenges with Existing Communication and ZeroClaw’s Approach

Asynchronous Server Structure Using Rust Tokio

1. Defining Basic Message Structures

2. Implementing Asynchronous TCP Handlers

Team Agent Communication and Protocol Design

Conclusion: Towards Scalable Agent Systems

Reference Code and Resources

[ZeroClaw] Rust Channel-Based IPC Design for Asynchronous Agent Runtime

[ZeroClaw] Rust Channel-Based IPC Design for Asynchronous Agent Runtime

1. Problems with the Existing Architecture and the Need for Asynchronous Design

2. Event Loop Structure Using Rust’s tokio::sync::mpsc

Core Design Points

3. Practical Code: A Concrete Implementation Example

Step 1: Define the Message Protocol

Step 2: Implement Agent Struct and Runner

Step 3: Main Runtime and Channel Connection

4. ZeroClaw Project Application Effects

Conclusion

MCP Server Performance Optimization: Building Ultra-Fast Processing Pipelines with Rust

MCP Server Performance Optimization: Building Ultra-Fast Processing Pipelines with Rust

1. Problem Definition: Single-Threaded Bottleneck

2. Asynchronous Processing with Rust and Tokio

Basic Setup (Cargo.toml)

Asynchronous Handler Implementation Example

3. Memory Optimization through Streaming

4. Conclusion: Towards ZeroClaw

High-Performance MCP Server Implemented with Rust: Asynchronous Messaging Using ZeroMQ

High-Performance MCP Server Implemented with Rust: Asynchronous Messaging Using ZeroMQ

Why ZeroMQ? (Comparison with TCP Socket Limitations)

Architecture Design: ZeroMQ PUB/SUB Pattern

Key Dependencies (Cargo.toml)

Implementation: High-Performance MCP Router

1. Define Message Protocol

2. Asynchronous ZeroMQ Context and Socket Setup

3. Agent Publisher Implementation

Performance Optimization and IPC Utilization

Conclusion

References

2. Event Loop Structure Using Rust’s `tokio::sync::mpsc`