When I started building Waggle, a multi-agent orchestration framework, the first question was: which language? TypeScript was the obvious choice — it's what most AI tooling is built in. But I chose Go, and after shipping the first version, I'm convinced it was the right call.
Why Go for AI Agents?
Multi-agent systems are fundamentally concurrent. You have agents running in parallel, passing messages, waiting on external APIs, and coordinating on shared state. Go's goroutines and channels are purpose-built for exactly this pattern.
In TypeScript, you'd reach for Promise.all, worker threads, or a message queue library. In Go, it's idiomatic:
type Agent struct {
ID string
Inbox chan Message
Outbox chan Message
handler HandlerFunc
}
func (a *Agent) Run(ctx context.Context) {
for {
select {
case msg := <-a.Inbox:
response, err := a.handler(ctx, msg)
if err != nil {
// handle error
continue
}
a.Outbox <- response
case <-ctx.Done():
return
}
}
}
The Orchestrator Pattern
Waggle uses a central orchestrator that routes messages between agents based on capability declarations. Each agent registers what it can do, and the orchestrator decides which agent handles each task.
type Orchestrator struct {
agents map[string]*Agent
router Router
mu sync.RWMutex
}
func (o *Orchestrator) Dispatch(ctx context.Context, task Task) (Result, error) {
o.mu.RLock()
agent, err := o.router.Route(task, o.agents)
o.mu.RUnlock()
if err != nil {
return Result{}, fmt.Errorf("routing failed: %w", err)
}
return agent.Execute(ctx, task)
}
Handling Agent Failures
The hardest part of multi-agent systems isn't the happy path — it's failure handling. What happens when an agent times out? When it returns a malformed response? When the LLM it's calling is rate-limited?
Waggle uses a circuit breaker pattern per agent, with configurable retry policies and fallback agents. This keeps the system resilient without requiring every caller to implement their own retry logic.
Observability
Go's structured logging (via slog) and the expvar package make it straightforward to expose agent metrics: messages processed, error rates, latency percentiles. For a production deployment, these feed directly into Datadog or Prometheus.
What I'd Do Differently
If I were starting over, I'd invest earlier in a proper event sourcing model for agent state. The current approach uses in-memory state with periodic snapshots, which works but makes replay and debugging harder than it should be.
Try It
Waggle is open source and actively maintained. If you're building multi-agent systems and want a lightweight Go framework that doesn't require a PhD to understand, give it a look.