Pulsing

中文文档

Pulsing is a distributed actor framework that provides a communication backbone for building distributed systems, with specialized support for AI applications.

🚀 Zero Dependencies — Pure Rust + Tokio, no NATS/etcd/Redis

🌐 Auto Discovery — Built-in Gossip protocol for cluster management

🔀 Location Transparent — Same API for local and remote Actors

⚡ Streaming Ready — Native support for LLM streaming responses

🤖 Agent Friendly — Integrates with AutoGen, LangGraph out of the box

🚀 Get Started in 5 Minutes

Installation

pip install pulsing

Your First Multi-Agent Application

import asyncio
import pulsing as pul
from pulsing.agent import runtime

@pul.remote
class Greeter:
    def __init__(self, display_name: str):
        self.display_name = display_name

    def greet(self, message: str) -> str:
        return f"[{self.display_name}] Received: {message}"

    async def chat_with(self, peer_name: str, message: str) -> str:
        # Use Greeter.resolve() to get a typed proxy
        peer = await Greeter.resolve(peer_name)
        return await peer.greet(f"From {self.display_name}: {message}")

async def main():
    async with runtime():
        # Create two agents
        alice = await Greeter.spawn(display_name="Alice", name="alice")
        bob = await Greeter.spawn(display_name="Bob", name="bob")

        # Agent communication
        reply = await alice.chat_with("bob", "Hello!")
        print(reply)  # [Bob] Received: From Alice: Hello!

asyncio.run(main())

That's it! @pul.remote turns a regular class into a distributed Actor, and Greeter.resolve() enables agents to discover and communicate with each other.

💡 I want to...

Scenario	Example	Description
Quick start	examples/quickstart/	Get started in 10 lines
Multi-Agent collaboration	examples/agent/pulsing/	AI debate, brainstorming, role-playing
Distributed LLM inference	pulsing actor router/vllm	GPU cluster inference service
Integrate AutoGen	examples/agent/autogen/	One line to go distributed
Integrate LangGraph	examples/agent/langgraph/	Execute graphs across nodes

🎯 Core Capabilities

1. Multi-Agent Collaboration

Multiple AI Agents working in parallel and communicating:

from pulsing.agent import agent, runtime, llm

@agent(role="Researcher", goal="Deep analysis")
class Researcher:
    async def analyze(self, topic: str) -> str:
        client = await llm()
        return await client.ainvoke(f"Analyze: {topic}")

@agent(role="Reviewer", goal="Evaluate proposals")
class Reviewer:
    async def review(self, proposal: str) -> str:
        client = await llm()
        return await client.ainvoke(f"Review: {proposal}")

async with runtime():
    researcher = await Researcher.spawn(name="researcher")
    reviewer = await Reviewer.spawn(name="reviewer")

    # Parallel work and collaboration
    analysis = await researcher.analyze("AI trends")
    feedback = await reviewer.review(analysis)

# Run MBTI personality discussion example
python examples/agent/pulsing/mbti_discussion.py --mock --group-size 6

# Run parallel idea generation example
python examples/agent/pulsing/parallel_ideas_async.py --mock --n-ideas 5

2. One Line to Distributed

Develop locally, scale seamlessly to clusters:

# Standalone mode (development)
async with runtime():
    agent = await MyAgent.spawn(name="agent")

# Distributed mode (production) — just add address
async with runtime(addr="0.0.0.0:8001"):
    agent = await MyAgent.spawn(name="agent")

# Other nodes auto-discover
async with runtime(addr="0.0.0.0:8002", seeds=["node1:8001"]):
    agent = await resolve("agent")  # Cross-node transparent call

3. LLM Inference Service

Out-of-the-box GPU cluster inference:

# Start Router (OpenAI-compatible API)
pulsing actor pulsing.actors.Router --addr 0.0.0.0:8000 --http_port 8080 --model_name my-llm

# Start vLLM Worker (can have multiple)
pulsing actor pulsing.actors.VllmWorker --model Qwen/Qwen2.5-0.5B --addr 0.0.0.0:8002 --seeds 127.0.0.1:8000

# Test
curl http://localhost:8080/v1/chat/completions \
  -d '{"model": "my-llm", "messages": [{"role": "user", "content": "Hello"}]}'

4. Agent Framework Integration

Have existing AutoGen/LangGraph code? One-line migration:

# AutoGen: Replace runtime
from pulsing.autogen import PulsingRuntime
runtime = PulsingRuntime(addr="0.0.0.0:8000")

# LangGraph: Wrap the graph
from pulsing.langgraph import with_pulsing
distributed_app = with_pulsing(app, seeds=["gpu-server:8001"])

📚 Example Guide

examples/
├── quickstart/              # ⭐ 5-minute quickstart
│   └── hello_agent.py       #    First Agent
├── agent/
│   ├── pulsing/             # ⭐⭐ Multi-Agent apps
│   │   ├── mbti_discussion.py      # MBTI personality discussion
│   │   └── parallel_ideas_async.py # Parallel idea generation
│   ├── autogen/             # AutoGen integration
│   └── langgraph/           # LangGraph integration
├── python/                  # ⭐⭐ Basic examples
│   ├── ping_pong.py         #    Actor basics
│   ├── cluster.py           #    Cluster communication
│   └── ...
└── rust/                    # Rust examples

🔧 Technical Features

• Zero external dependencies: Pure Rust + Tokio, no NATS/etcd/Redis needed
• Gossip protocol: Built-in SWIM protocol for node discovery and failure detection
• Location transparency: Same API for local and remote Actors
• Streaming messages: Native support for streaming requests/responses (LLM-ready)
• Type safety: Rust Behavior API provides compile-time message type checking

📦 Project Structure

Pulsing/
├── crates/                   # Rust core
│   ├── pulsing-actor/        #   Actor System
│   └── pulsing-py/           #   Python bindings
├── python/pulsing/           # Python package
│   ├── actor/                #   Actor API
│   ├── agent/                #   Agent toolkit
│   ├── autogen/              #   AutoGen integration
│   └── langgraph/            #   LangGraph integration
├── examples/                 # Example code
└── docs/                     # Documentation

🛠️ Development

# Development build
maturin develop

# Run tests
pytest tests/python/
cargo test --workspace

📄 License

Apache-2.0