When building multi-agent swarms, where specialized agents (Researcher, Coder, Reviewer, Tester) collaborate, sharing intermediate state via flat text prompts creates bottlenecking and state drift.
1. The Multi-Agent Coordination Challenge
If Agent A finds a bug root cause and hands off to Agent B, Agent B shouldn't re-parse raw logs. Agent A should write its findings directly into a shared memory graph that Agent B can query instantly.
2. Shared Memory Architecture
Axonio acts as the central memory message bus for multi-agent frameworks like LangChain, LangGraph, and OpenClaw.
3. Full Swarm Orchestration Code
multi-agent-swarm.ts
typescript
import { Axonio } from '@clawdb/sdk';
import { AgentResearcher, AgentCoder } from './agents';
const claw = new Axonio({ apiKey: process.env.CLAWDB_API_KEY });
const namespace = 'swarm_workspace_402';
export async function runSwarmTask(userGoal: string) {
// Step 1: Research Agent investigates and stores findings
const researchNotes = await AgentResearcher.explore(userGoal);
await claw.memory.add({
namespace,
content: `Research Finding: ${researchNotes}`,
metadata: { agent: 'Researcher' },
});
// Step 2: Coding Agent queries shared memory and implements fix
const findings = await claw.memory.query({ namespace, query: userGoal });
const codeFix = await AgentCoder.implement(findings.results[0].content);
console.log('Swarm Task Completed:', codeFix);
}