Reflex

Reflex is an intelligent caching proxy for LLM APIs that sits between your applications and AI providers like OpenAI, Anthropic, and other large language model services. By intelligently caching and minifying responses, Reflex dramatically reduces your API costs while improving response latency.

Stop Paying for the Same Answer Twice

Every time your application sends a query to an LLM API, you're charged for tokens—both input and output. Many applications send similar or identical queries repeatedly: customer support bots answering common questions, coding assistants explaining the same concepts, or chatbots handling routine requests.

Reflex eliminates this redundancy. When a query comes in, Reflex checks its cache before forwarding to the LLM provider. If a matching response exists, it's returned instantly—no API call, no tokens consumed, no cost incurred.

Semantic Similarity Matching

Traditional caches only match exact queries. But users rarely ask the same question the same way twice. "How do I reset my password?" and "I forgot my password, how can I change it?" are semantically identical—and should return the same cached response.

Reflex uses vector embeddings and semantic search to understand query intent, not just query text. This dramatically increases your cache hit rate compared to exact-match caching, maximizing cost savings without sacrificing response quality or accuracy.

Response Minification

Beyond caching, Reflex optimizes token usage through intelligent response minification. LLM responses often contain verbose formatting, redundant phrasing, and unnecessary whitespace that consume tokens without adding value. Reflex compresses responses while preserving meaning, reducing the token footprint of every interaction.

Sub-100ms Response Latency

Cached responses are served in milliseconds, not the seconds typical of LLM API calls. For latency-sensitive applications—real-time chat interfaces, interactive coding tools, or customer-facing support systems—this speed improvement transforms the user experience.

Your users get instant responses for common queries while you save money. It's a win on both sides.

OpenAI-Compatible Drop-in Integration

Reflex exposes an OpenAI-compatible API endpoint, making integration effortless. Point your existing applications to Reflex instead of directly to your LLM provider, and start saving immediately. No code changes required beyond updating the API endpoint URL.

Works with any application or framework that supports the OpenAI API specification—LangChain, LlamaIndex, custom integrations, and more.

Multi-Tier Caching Architecture

Reflex employs a sophisticated multi-tier caching strategy to maximize hit rates while maintaining accuracy:

Exact Match Layer — Lightning-fast lookups for identical queries. In-memory storage ensures sub-millisecond response times for repeat requests.

Semantic Match Layer — Vector similarity search identifies semantically equivalent queries. Even when wording differs, Reflex recognizes the same intent and returns cached responses.

Verification Layer — Cross-encoder reranking validates semantic matches before returning cached responses. This minimizes false positives and ensures response accuracy.

How It Works

1. Query arrives — Your application sends a request to Reflex just like it would to any LLM API
2. Cache check — Reflex searches for exact matches, then semantic matches if none found
3. Cache hit — Matching response returned instantly, no API call made, no tokens consumed
4. Cache miss — Request forwarded to LLM provider, response cached for future queries
5. Response delivered — Your application receives the response in standard OpenAI format

The entire process is transparent to your application. Reflex handles caching, minification, and optimization automatically.

Perfect For

High-Volume AI Applications

Customer support chatbots, FAQ systems, and help desk assistants handle thousands of queries daily—many of them repetitive. Reflex ensures you're not paying for the same answer over and over again.

Development and Testing Environments

Building AI-powered applications means running countless test queries during development. Without caching, these tests burn through tokens and inflate costs. Reflex eliminates wasteful spending during iteration and debugging.

Cost-Conscious Engineering Teams

AI API costs can spiral quickly at scale. Reflex gives you control over spending without limiting functionality. Get more value from your AI budget by eliminating redundant API calls.

Latency-Sensitive Applications

Real-time applications demand fast responses. Cached queries return in milliseconds, enabling interactive experiences that would be impossible with standard API latency.

Multi-Tenant SaaS Platforms

When multiple users ask similar questions, Reflex's semantic matching maximizes cache sharing across your user base, multiplying savings as you scale.

Enterprise-Ready Features

• Transparent proxy architecture — No changes to your application logic required
• OpenAI API compatibility — Works with existing tooling and frameworks
• Semantic understanding — Goes beyond exact matching for higher hit rates
• Response minification — Reduces token consumption on every interaction
• Low latency — Sub-100ms responses for cached queries
• Scalable design — Handles high-throughput workloads efficiently