ricable @ruvector/tiny-dancer
FastGRNN-based neural router for AI agent orchestration, achieving 10-microsecond routing decisions with circuit breaker patterns, uncertainty estimation, and hot-reload model updates.
Quick Reference
| Task | Code |
|---|---|
| Install | npx @ruvector/tiny-dancer@latest |
| Import | import { TinyDancer } from '@ruvector/tiny-dancer'; |
| Create | const dancer = new TinyDancer(); |
| Route | const route = await dancer.route(embedding); |
| Hot reload | await dancer.reload(newWeights); |
| Health | const health = dancer.healthCheck(); |
Installation
Hub install (recommended): npx agentic-flow@latest includes this package.
Standalone: npx @ruvector/tiny-dancer@latest
See Installation Guide for the full ecosystem.
Key API
TinyDancer
The main FastGRNN router class.
import { TinyDancer } from '@ruvector/tiny-dancer';
const dancer = new TinyDancer({
routes: ['code', 'chat', 'analysis', 'creative'],
hiddenSize: 64,
uncertaintyThreshold: 0.3,
});
Constructor Options:
| Option | Type | Default | Description |
|---|---|---|---|
routes |
string[] |
[] |
Named routing destinations |
hiddenSize |
number |
64 |
FastGRNN hidden state size |
inputSize |
number |
128 |
Input embedding dimensions |
uncertaintyThreshold |
number |
0.3 |
Uncertainty cutoff for fallback |
circuitBreaker |
boolean |
true |
Enable circuit breaker pattern |
failureThreshold |
number |
5 |
Failures before circuit opens |
recoveryTimeMs |
number |
30000 |
Circuit breaker recovery time |
warmupRequests |
number |
100 |
Requests before full confidence |
enableMetrics |
boolean |
true |
Collect routing metrics |
Methods:
| Method | Returns | Description |
|---|---|---|
route(embedding) |
Promise<RouteResult> |
Route based on input embedding |
routeBatch(embeddings) |
Promise<RouteResult[]> |
Batch routing |
reload(weights) |
Promise<void> |
Hot-reload model weights |
train(data, labels) |
Promise<TrainResult> |
Train the router |
healthCheck() |
HealthStatus |
Get health and circuit status |
getMetrics() |
RouterMetrics |
Get routing performance metrics |
reset() |
void |
Reset router state |
save(path) |
Promise<void> |
Save router model |
load(path) |
Promise<void> |
Load router model |
FastGRNN
The underlying Fast Gated Recurrent Neural Network cell.
import { FastGRNN } from '@ruvector/tiny-dancer';
const cell = new FastGRNN({
inputSize: 128,
hiddenSize: 64,
zetaInit: 1.0,
nuInit: -4.0,
});
Constructor Options:
| Option | Type | Default | Description |
|---|---|---|---|
inputSize |
number |
required | Input feature dimensions |
hiddenSize |
number |
required | Hidden state dimensions |
zetaInit |
number |
1.0 |
Zeta initialization value |
nuInit |
number |
-4.0 |
Nu initialization value |
wSparsity |
number |
0.0 |
W matrix sparsity (0.0-1.0) |
uSparsity |
number |
0.0 |
U matrix sparsity (0.0-1.0) |
gateNonlinearity |
string |
'sigmoid' |
Gate nonlinearity |
updateNonlinearity |
string |
'tanh' |
Update nonlinearity |
CircuitBreaker
Fault-tolerance pattern for routing failures.
import { CircuitBreaker } from '@ruvector/tiny-dancer';
const breaker = new CircuitBreaker({
failureThreshold: 5,
recoveryTimeMs: 30000,
});
States: 'closed' (normal), 'open' (failing, using fallback), 'half-open' (testing recovery)
Methods:
| Method | Returns | Description |
|---|---|---|
execute(fn) |
Promise<T> |
Execute with circuit protection |
getState() |
CircuitState |
Get current circuit state |
forceOpen() |
void |
Force circuit open |
forceClose() |
void |
Force circuit closed |
reset() |
void |
Reset failure counters |
Common Patterns
Ultra-Low-Latency Agent Routing
import { TinyDancer } from '@ruvector/tiny-dancer';
const dancer = new TinyDancer({
routes: ['haiku', 'sonnet', 'opus'],
hiddenSize: 32,
});
const result = await dancer.route(taskEmbedding);
console.log(`Route: ${result.route}, Latency: ${result.latencyUs}us`);
Routing with Uncertainty Fallback
import { TinyDancer } from '@ruvector/tiny-dancer';
const dancer = new TinyDancer({ uncertaintyThreshold: 0.4 });
const result = await dancer.route(embedding);
if (result.uncertain) {
// Fall back to more expensive but reliable routing
const fallbackResult = await expensiveRouter.route(input);
}
Hot-Reload Model Updates
import { TinyDancer } from '@ruvector/tiny-dancer';
const dancer = new TinyDancer({ routes: ['fast', 'quality'] });
// Update weights without downtime
const newWeights = await fetchLatestWeights();
await dancer.reload(newWeights);
// Zero-downtime update complete
RAN DDD Context
Bounded Context: RANO Optimization
References
- API reference: See references/commands.md
- Full README
- npm