OmniRoute Auto-Combo Engine

For Users: Looking for a quick start? See the Auto-Combo User Guide for simple explanations and examples.

Self-managing model chains with adaptive scoring + zero-config auto-routing

Zero-Config Auto-Routing (auto/ prefix)

NEW: No combo creation required. Use auto/ prefix directly in any client.

Quick Examples

How to use:

# Any IDE or CLI tool that supports OpenAI format
Base URL: http://localhost:20128/v1
API Key:  <your-endpoint-key>

# In your code/config, set model to:
model: "auto"                 # balanced default
model: "auto/coding"          # best for coding tasks
model: "auto/fast"            # fastest available
model: "auto/cheap"           # cheapest per token

What happens:

1. OmniRoute detects auto/ prefix in src/sse/handlers/chat.ts
2. Queries all active provider connections from the database
3. Filters to those with valid credentials (API key or OAuth token)
4. Determines the model per connection (connection.defaultModel or provider's first model)
5. Builds a virtual combo in-memory (not stored in DB)
6. Routes using the selected variant's weight profile + LKGP strategy

Key properties:

• ✅ Always-on: No toggle, no combo creation, no configuration needed
• ✅ Dynamic: Reflects current connected providers automatically
• ✅ Session stickiness: LKGP ensures last successful provider is prioritized
• ✅ Multi-account aware: Each provider connection becomes a separate candidate
• ✅ No DB writes: Virtual combo exists only for the request, zero persistence overhead

Behind the scenes:

Request: { model: "auto/coding" }
   ↓
src/sse/handlers/chat.ts detects prefix
   ↓
createVirtualAutoCombo('coding') → candidatePool from active connections
   ↓
handleComboChat (same engine as persisted combos)
   ↓
Auto-scoring selects best provider/model per request

Implementation files:

How It Works (Persisted Auto-Combos)

The Auto-Combo Engine dynamically selects the best provider/model for each request using a 9-factor scoring function (defined in open-sse/services/autoCombo/scoring.ts → DEFAULT_WEIGHTS). All weights sum to 1.0.

Source: diagrams/auto-combo-9factor.mmd

Sum: 0.22 + 0.17 + 0.17 + 0.13 + 0.08 + 0.08 + 0.05 + 0.05 + 0.05 = 1.0 (validated by validateWeights()).

Mode Packs

Four pre-defined weight profiles in open-sse/services/autoCombo/modePacks.ts. Each pack overrides the default weights to bias selection toward a specific goal. Below are the full weight tables per pack (each row sums to 1.0).

Notes:

• tierAffinity and specificityMatch are not set in mode packs — calculateScore() treats them as ?? 0 when absent.
• Each pack's emphasis at a glance:
  • ship-fast → latencyInv 0.32 + health 0.28 (low-latency, healthy connections)
  • cost-saver → costInv 0.37 (cheapest tokens win)
  • quality-first → taskFit 0.37 + stability 0.15 (best model for the task, consistent)
  • offline-friendly → quota 0.37 + health 0.28 (max headroom regardless of speed/cost)

All Routing Strategies

OmniRoute's combo engine supports 14 routing strategies (declared in src/shared/constants/routingStrategies.ts → ROUTING_STRATEGY_VALUES). The Auto Combo engine itself is exposed under the auto strategy; the others are available for persisted combos.

⭐ = New in v3.8.0

Virtual Auto-Combo Factory

The Auto Combo engine doesn't require pre-defined combos. Instead, open-sse/services/autoCombo/virtualFactory.ts builds candidates on-the-fly:

1. Pulls getProviderConnections({ isActive: true }) (all enabled connections)
2. Filters to those with valid credentials (API key or non-expired OAuth token via hasUsableOAuthToken())
3. Cross-references with getProviderRegistry() for model availability + pricing
4. For each tuple (provider, model, connection), builds a VirtualAutoComboCandidate
5. Picks connection.defaultModel (or the registry's first model) as the dispatch target
6. Scores each candidate using the 9-factor scorePool() and the variant's weight pack
7. Returns the resulting in-memory AutoComboConfig for handleComboChat() — never persisted to DB

This means adding a new provider with auto/* enabled automatically expands the candidate pool — no manual combo editing needed. The virtual combo is rebuilt per request, so newly-added or newly-healthy connections are picked up immediately.

Self-Healing

• Temporary exclusion: Score < 0.2 → excluded for 5 min (progressive backoff, max 30 min)
• Circuit breaker awareness: OPEN → auto-excluded; HALF_OPEN → probe requests
• Incident mode: >50% OPEN → disable exploration, maximize stability
• Cooldown recovery: After exclusion, first request is a "probe" with reduced timeout

Bandit Exploration

5% of requests (configurable) are routed to random providers for exploration. Disabled in incident mode.

API

There is no dedicated POST /api/combos/auto endpoint — Auto-Combo is consumed in two ways:

1. Zero-config (recommended): Send any chat completion request with model: "auto" or model: "auto/<variant>". The virtual factory builds the combo per request — no persistence, no API calls needed.

2. Persisted combo with strategy: "auto": Create a regular combo via POST /api/combos and set strategy: "auto" plus config.auto.weights / config.auto.candidatePool. The same scoring engine is used; the combo is stored in combos and reusable by ID.

# Zero-config usage (no combo creation)
curl -X POST http://localhost:20128/v1/chat/completions \
  -H "Authorization: Bearer <key>" \
  -H "Content-Type: application/json" \
  -d '{"model":"auto/coding","messages":[{"role":"user","content":"Hello"}]}'

# Persisted auto combo via the regular combos endpoint
curl -X POST http://localhost:20128/api/combos \
  -H "Content-Type: application/json" \
  -d '{"id":"my-auto","name":"Auto Coder","strategy":"auto","config":{"auto":{"candidatePool":["anthropic","google","openai"],"weights":{"quota":0.15,"health":0.3,"costInv":0.05,"latencyInv":0.35,"taskFit":0.1,"stability":0,"tierPriority":0.05}}}}'

Auto router strategies

Persisted strategy: "auto" combos can set config.routerStrategy (or legacy config.auto.routerStrategy) to one of:

• rules — default weighted scoring
• cost / eco — cheapest healthy provider
• latency / fast — lowest p95 latency with reliability penalty
• sla-aware / sla — prefer candidates that satisfy p95 latency, error-rate, and optional cost SLOs
• lkgp — last known good provider first

Router strategies in detail

The auto-combo engine exposes 5 pluggable RouterStrategy implementations that you can swap via config.routerStrategy (or the legacy config.auto.routerStrategy). Each strategy picks one provider from the candidate pool, given a RoutingContext (task type, tool/vision hints, token estimate, optional SLA policy, optional last-known-good provider).

1. rules (default) — 6-factor weighted scoring

Wraps the existing scoring engine. Filters out OPEN circuit-breaker candidates, then runs scorePool() with the current task type and getTaskFitness(), picking the top-scoring provider.

class RulesStrategyImpl implements RouterStrategy {
  readonly name = "rules";
  readonly description =
    "6-factor weighted scoring: quota, health, cost, latency, taskFit, stability";

  select(pool, context) {
    const eligible = pool.filter((c) => c.circuitBreakerState !== "OPEN");
    const ranked = scorePool(eligible.length > 0 ? eligible : pool, context.taskType, undefined, getTaskFitness);
    return { provider: ranked[0].provider, /* ... */ };
  }
}

When to use: Default. Use when you want a balanced trade-off across all signals.

Alias: rules (no alias)

2. cost / eco — cheapest healthy provider

Sorts the candidate pool by costPer1MTokens (ascending) and picks the cheapest. Filters out OPEN candidates first.

class CostStrategyImpl implements RouterStrategy {
  readonly name = "cost";
  readonly description = "Always selects cheapest available provider";

  select(pool, context) {
    const healthy = pool.filter((c) => c.circuitBreakerState !== "OPEN");
    const sorted = [...healthy].sort((a, b) => a.costPer1MTokens - b.costPer1MTokens);
    return { provider: sorted[0].provider, /* ... */ };
  }
}

When to use: Cost-sensitive workloads, batch processing, or background jobs.

Aliases: cost, eco

3. latency / fast — lowest p95 latency with reliability penalty

Sorts by p95LatencyMs + (errorRate * 1000). The error-rate penalty ensures unreliable providers are ranked lower even if their nominal latency is low.

class LatencyStrategyImpl implements RouterStrategy {
  readonly name = "latency";
  readonly description = "Prioritizes lowest p95 latency with reliability weighting";

  select(pool, context) {
    const healthy = pool.filter((c) => c.circuitBreakerState !== "OPEN");
    const sorted = [...healthy].sort((a, b) =>
      (a.p95LatencyMs + a.errorRate * 1000) - (b.p95LatencyMs + b.errorRate * 1000)
    );
    return { provider: sorted[0].provider, /* ... */ };
  }
}

When to use: Latency-sensitive workloads like real-time chat, autocomplete, or interactive coding assistants.

Aliases: latency, fast

4. sla-aware / sla — latency/error/cost SLO compliance

Scores each candidate by how well it satisfies the configured SLO policy:

When hardConstraints: true, candidates are sorted primarily by violation score (how far they exceed any SLO), then by composite score. Otherwise it's just the composite score.

class SLAStrategyImpl implements RouterStrategy {
  readonly name = "sla-aware";
  readonly description = "Selects the provider most likely to satisfy latency, error-rate, and cost SLOs";

  select(pool, context) {
    // ... scores each candidate against policy: { targetP95Ms, maxErrorRate, maxCostPer1MTokens, hardConstraints }
  }
}

SLA fields (set on the combo config):

{
  "strategy": "auto",
  "config": {
    "routerStrategy": "sla-aware",
    "slaTargetP95Ms": 1500,
    "slaMaxErrorRate": 0.05,
    "slaMaxCostPer1MTokens": 5,
    "slaHardConstraints": true
  }
}

When to use: Production workloads with strict latency, error-rate, or cost budgets.

Aliases: sla-aware, sla

5. lkgp — last known good provider first

Tries the last known good provider (if set) first, then falls back to the rules strategy. Useful for session stickiness — the same provider handles follow-up requests in a conversation.

class LKGPStrategyImpl implements RouterStrategy {
  readonly name = "lkgp";
  readonly description = "Tries last known good provider first, then falls back to rules";

  select(pool, context) {
    if (context.lkgpEnabled === false) {
      return getStrategy("rules").select(pool, context);
    }

    if (context.lastKnownGoodProvider) {
      const candidates = pool.filter(
        (c) => c.provider === context.lastKnownGoodProvider && c.circuitBreakerState !== "OPEN"
      );
      if (candidates.length > 0) {
        return { provider: candidates[0].provider, /* ... */ };
      }
    }

    // Fallback to rules strategy
    return getStrategy("rules").select(pool, context);
  }
}

When to use: Multi-turn conversations where you want the same provider to handle follow-up requests (e.g., for caching, context continuity, or pricing consistency).

Alias: lkgp (no alias)

Custom router strategies

You can register your own RouterStrategy implementation via the public API:

import { registerStrategy, type RouterStrategy } from "@omniroute/open-sse/services/autoCombo/routerStrategy";

class MyCustomStrategy implements RouterStrategy {
  readonly name = "my-custom";
  readonly description = "My custom routing strategy";

  select(pool, context) {
    // Your routing logic here
    return {
      provider: pool[0].provider,
      model: pool[0].model,
      strategy: this.name,
      reason: "MyCustomStrategy: ...",
      candidatesConsidered: pool.length,
      finalScore: 1.0,
    };
  }
}

registerStrategy("my-custom", new MyCustomStrategy());

Then use it:

{
  "strategy": "auto",
  "config": {
    "routerStrategy": "my-custom"
  }
}

Router strategy selection guide

SLA-aware fields:

{
  "strategy": "auto",
  "config": {
    "routerStrategy": "sla-aware",
    "slaTargetP95Ms": 1500,
    "slaMaxErrorRate": 0.05,
    "slaMaxCostPer1MTokens": 5,
    "slaHardConstraints": true
  }
}

Task Fitness

30+ models scored across 6 task types (coding, review, planning, analysis, debugging, documentation). Supports wildcard patterns (e.g., *-coder → high coding score).

Auto Variants Recap

Including the bare auto (default) plus the 6 AutoVariant values declared in autoPrefix.ts, there are 7 invokable model IDs:

auto, auto/coding, auto/fast, auto/cheap, auto/offline, auto/smart, auto/lkgp

(AutoVariant itself enumerates 6 values; the 7th option is "no variant" — bare auto — handled by parseAutoPrefix() as variant: undefined.)

How tiers fit Auto-Combo

The 9-factor scoring function (open-sse/services/autoCombo/scoring.ts) treats tier membership as one signal via the tierPriority weight. Default weights (from DEFAULT_WEIGHTS):

Tier alone does not force Tier 1 first — if Tier 1 latency is bad or cost-vs-quality is suboptimal, Tier 2 wins. To force tier ordering, use combo strategy priority and arrange providers by tier.

To strongly favor Tier 1 (subscription), increase tierPriority weight:

{
  "strategy": "auto",
  "config": { "auto": { "weights": { "tierPriority": 0.3, "costInv": 0.05 } } }
}

See docs/marketing/TIERS.md for tier definitions and provider classification.

Files