Deploy resilient, stateful AI agent meshes in seconds. Zero infrastructure overhead, real-time telemetry, and sub-millisecond inter-agent consensus across edge clusters.
import { defineMesh, createAgent } from '@pulseflow/core';
export const mesh = defineMesh({
cluster: 'global-edge',
topology: 'raft-byzantine-v2',
maxLatencyMs: 8.5,
autoScale: { min: 12, max: 10000 }
});
export const researcher = createAgent({
name: 'researcher-agent',
model: 'gemini-3.8-flash',
memory: 'vector-stream-nvme',
sandbox: 'ebpf-isolated'
});
// Orchestrate collaborative task graph
await mesh.dispatch({
task: 'synthesize_dataset',
agents: [researcher, 'validator-v3'],
consensusRequired: true
});from pulseflow import AgentMesh, WorkerNode
mesh = AgentMesh(
region="eu-central",
sync_engine="ultra-fast-raft"
)
@mesh.subscribe(topic="financial.anomaly")
async def analyze_market_pulse(event):
swarm = await mesh.spawn_swarm(size=16)
results = await swarm.parallel_infer(
payload=event.payload,
verification_quorum=0.85
)
return results.commit_state()use pulseflow_core::{MeshEngine, NodeConfig};
#[tokio::main]
async fn main() -> Result<(), Box<dyn std::error::Error>> {
let node = MeshEngine::builder()
.heartbeat_interval_us(250)
.crypto_attestation(true)
.connect_cluster("wss://edge.pulseflow.io")
.await?;
node.stream_vectors_zero_copy().await
}Traditional microservices crumble under asynchronous AI agent workflows. PulseFlow replaces brittle message queues with a self-synchronizing agent mesh.
Every agent node maintains cryptographically verifiable memory states. When an agent experiences hallucinations or fails mid-trajectory, consensus nodes detect divergence and rollback in under 4ms.
Give agents unconstrained code-execution powers inside millisecond-booting micro-VMs isolated with kernel eBPF probes.
Synchronize dynamic context windows across thousands of agents without re-indexing embeddings from scratch.
Never lose a 4-hour agentic task because of rate limits or transient API drops. PulseFlow checkpoints step transitions deterministically to distributed durable ledgers.
Adjust fleet concurrency to calculate throughput, inter-agent consensus latency, and infrastructure cost reductions.
Define agent roles, communication protocols, memory persistence scopes, and execution constraints using Python or TypeScript SDKs.
A single command builds eBPF sandboxes and broadcasts verified agent binaries to ultra-low latency nodes closest to your users.
Watch live agent consensus in our visual dashboard. PulseFlow automatically balances compute loads and recovers divergent states.
Scale effortlessly from prototype agents to multi-tenant production fleets.
Join hundreds of AI engineering teams building resilient, decentralized agent meshes with PulseFlow.