Reminiscence

Reminiscence eliminates redundant computations by matching queries semantically instead of exact strings. Built for LLM applications, RAG pipelines, and multi-agent systems where users express identical intent through different phrasing.

Why semantic caching matters

Traditional caching requires exact string matches. Two semantically identical queries like “Analyze Q3 sales” and “Show third quarter revenue” both trigger expensive LLM API calls.

Reminiscence uses sentence transformers to understand query meaning. Semantically similar requests return cached results, reducing API costs and latency.

Core capabilities

Semantic matching

FastEmbed with multilingual transformers recognizes equivalent queries regardless of phrasing.

Hybrid approach

Combines semantic similarity with exact context matching for precise cache control.

Production-grade

Multiple eviction policies, TTL, health checks, and OpenTelemetry built-in.

Type-safe

Handles DataFrames, numpy arrays, nested structures. Scales to 100K+ entries.

Built for AI systems

• LLM applications with variable user input
• Multi-agent workflows requiring shared context
• RAG pipelines caching retrieval and generation
• Data analysis workflows with expensive operations