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Documentation Index

Fetch the complete documentation index at: https://docs.mem0.ai/llms.txt

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Mem0 rerankers rescore vector search hits so your agents surface the most relevant memories. Use this hub to decide when reranking helps, configure a provider, and fine-tune performance.
Reranking trades extra latency for better precision. Start once you have baseline search working and measure before/after relevance.

Understand Reranking

Configure Providers

Optimize Performance

Custom Prompts

Zero Entropy Guide

Sentence Transformers

Picking the Right Reranker

  • API-first when you need top quality and can absorb request costs (Cohere, Zero Entropy).
  • Self-hosted for privacy-sensitive deployments that must stay on your hardware (Sentence Transformer, Hugging Face).
  • LLM-driven when you need bespoke scoring logic or complex prompts.
  • Hybrid by enabling reranking only on premium journeys to control spend.

Implementation Checklist

  1. Confirm baseline search KPIs so you can measure uplift.
  2. Select a provider and add the reranker block to your config.
  3. Test latency impact with production-like query batches.
  4. Decide whether to enable reranking globally or per-search via the rerank flag.

Set Up Reranking

Example: Reranker Search