StrataRouter

Production-Grade Semantic Routing

Lightning-fast semantic routing that exceeds enterprise standards with <10ms P99 latency and 95.4% accuracy — built in Rust with Python bindings.

Get Started View on GitHub

8.7msP99 Latency

18K/sThroughput

95.4%Accuracy

64MBBase Memory

20xFaster than alternatives

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Why StrataRouter?

Blazing Fast

8.7ms P99 latency · 18K req/s throughput · 20x faster than alternatives

Highly Accurate

95.4% routing accuracy with hybrid scoring algorithm and confidence calibration

Memory Efficient

64MB base footprint — 33x less memory than alternatives with SIMD-optimized operations

Framework Agnostic

Native integrations with LangChain, LangGraph, CrewAI, AutoGen, and any LLM provider

Enterprise Ready

SSO/SAML, multi-tenancy, HIPAA, GDPR, SOC 2 compliance with full audit trails

Production Grade

Semantic caching, auto-retry and fallbacks, OpenTelemetry observability built-in

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Quick Example

from stratarouter import Router, Route

# Create router
router = Router(dimension=384, threshold=0.5)

# Add routes
billing = Route("billing")
billing.description = "Billing and payment questions"
billing.keywords = ["invoice", "payment", "refund"]
router.add_route(billing)

support = Route("support")
support.description = "Technical support"
support.keywords = ["bug", "error", "crash"]
router.add_route(support)

# Build index (one-time setup)
router.build_index()

# Route queries — sub-millisecond
result = router.route("I need my invoice")
print(f"Route: {result.route_id}")          # billing
print(f"Confidence: {result.confidence}")   # 0.89
print(f"Latency: {result.latency_ms}ms")    # 1.2ms

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Performance Benchmarks

Metric	StrataRouter	semantic-router	llamaindex	Improvement
P99 Latency	8.7ms	178ms	245ms	20–28x faster
Throughput	18K req/s	450 req/s	380 req/s	40–47x higher
Memory	64MB	2.1GB	3.2GB	33–50x less
Accuracy	95.4%	84.7%	82.3%	+10–13 points

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Architecture

graph TB
    subgraph "Application Layer"
        A[Your Application]
    end
    subgraph "StrataRouter Core"
        B[Router Engine]
        C[HNSW Index]
        D[Hybrid Scorer]
    end
    subgraph "StrataRouter Runtime"
        E[Execution Engine]
        F[Provider Clients]
        G[Cache Layer]
    end
    subgraph "Infrastructure"
        H[(PostgreSQL)]
        I[Redis]
        J[Prometheus]
    end
    subgraph "LLM Providers"
        K[OpenAI GPT-5]
        L[Anthropic Claude]
        M[Google Gemini]
        N[Local Models]
    end
    A --> B
    B --> C
    B --> D
    B --> E
    E --> F
    E --> G
    F --> K
    F --> L
    F --> M
    F --> N
    G --> I
    E --> H
    E --> J
    style B fill:#2563eb,color:#fff
    style C fill:#059669,color:#fff
    style E fill:#d97706,color:#fff

Two-Layer Design: the Core handles lightning-fast routing decisions (<1ms), while the Runtime provides production execution with caching, batching, and full observability.

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Get Started

5-Minute Tutorial

Install and run your first semantic router in minutes.

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Core Engine

Explore the HNSW index, BM25 scoring, and hybrid routing algorithm.

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Runtime System

Production execution engine with caching, batching, and observability.

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Integrations

Connect with LangChain, LangGraph, CrewAI, AutoGen and more.

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Enterprise

Security, compliance, multi-tenancy, and dedicated support.

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Production

Deploy, monitor, scale, and optimize for production workloads.

→

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Use Cases

AI Agent Orchestration

Route user queries to specialized agents based on semantic intent.

router.add_route(Route("billing", description="Payment and invoices"))
router.add_route(Route("technical", description="Product bugs"))
router.add_route(Route("sales", description="Pricing inquiries"))

result = router.route("My payment failed")
# Routes to "billing" with 94% confidence

Multi-Model LLM Systems

Direct queries to the optimal model based on complexity and cost.

router.add_route(Route("gpt5",   description="Complex reasoning and analysis"))
router.add_route(Route("claude", description="Long-form content and coding"))
router.add_route(Route("gemini", description="Multimodal and real-time data"))
router.add_route(Route("local",  description="Privacy-sensitive queries"))

RAG Pipeline Optimization

Select the best retrieval strategy dynamically.

router.add_route(Route("semantic", description="Conceptual queries"))
router.add_route(Route("keyword",  description="Specific term lookups"))
router.add_route(Route("hybrid",   description="Mixed intent queries"))

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Installation

# Python (recommended)
pip install stratarouter

# With specific encoder
pip install "stratarouter[openai]"     # GPT embeddings
pip install "stratarouter[anthropic]"  # Claude embeddings
pip install "stratarouter[all]"        # All providers

# Rust
cargo add stratarouter-core

# Docker
docker pull stratarouter/stratarouter:latest

Full Installation Guide →

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What's New — v2.1 (February 2026)

Feature	Description
GPT-5 Support	OpenAI GPT-5 with enhanced reasoning
Claude 4.5 Sonnet	Full Anthropic latest model support
Gemini 3.1 Pro	Google multimodal integration
95.4% Accuracy	Improved routing calibration
Semantic Caching	85%+ hit rate, 70–80% cost reduction
OpenTelemetry 2.0	Full distributed tracing

See Changelog for complete history.

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Trusted By

"StrataRouter reduced our routing latency by 25x while improving accuracy to 95%+. Game changer for production AI."

— AI Platform Lead, Fortune 500 Company

"The semantic caching alone saved us $15K/month in LLM costs. ROI in the first week."

— CTO, AI Startup

"Essential infrastructure for our multi-model AI platform. Handles 50K+ requests/day flawlessly."

— Head of Engineering, Enterprise SaaS

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Built for engineers, trusted by enterprises.
Made with care by the StrataRouter Team