Best practices

Memory lifecycle management

• Use TTL for short-term memory – Set appropriate TTL values on memory entries to automatically expire transient information. For session context, use TTLs of 30 minutes to 24 hours. For long-term user preferences, use longer TTLs or persist indefinitely.

• Implement memory decay – Mem0 provides built-in decay mechanisms that remove irrelevant information over time. Configure these to prevent memory bloat as the agent accumulates more interactions.

• Deduplicate memories – Before storing a new memory, check if a similar memory already exists using vector similarity search. Update existing memories rather than creating duplicates.

Vector index configuration

• Choose the right index type – Use FLAT for smaller memory stores (under 100,000 entries) where exact search is feasible. Use HNSW for larger stores where approximate nearest neighbor search provides better performance at scale.

• Select appropriate dimensions – Match the embedding dimensions to your model. Amazon Titan Text Embeddings V2 produces 1024-dimensional vectors. OpenAI's text-embedding-3-small produces 1536-dimensional vectors.

• Use COSINE distance metric – For text embeddings from models like Amazon Titan and OpenAI, COSINE distance is typically the most appropriate metric for measuring semantic similarity.

Multi-user isolation

• Scope memories by user ID – Always include a user_id parameter when storing and searching memories to prevent information leaking between users.

• Use TAG filters for efficient isolation – When querying the vector index, use TAG filters (for example, @user_id:{user_123}) to pre-filter results by user before performing KNN search. This runs as a single atomic operation, providing both isolation and performance.

  # Example: TAG-filtered vector search for user isolation
  results = client.execute_command(
      "FT.SEARCH", "agent_memory",
      f"@user_id:{{{user_id}}}=>[KNN 5 @embedding $query_vec]",
      "PARAMS", "2", "query_vec", query_vec,
      "DIALECT", "2",
  )

Memory management at scale

• Set maxmemory policy – Configure maxmemory-policy allkeys-lru on your ElastiCache cluster to automatically evict least-recently-used memory entries when the cluster reaches its memory limit.

• Monitor memory usage – Use Amazon CloudWatch metrics to track memory utilization, cache hit rates, and vector search latency. Set alarms for high memory usage to proactively manage capacity.

• Plan for capacity – Each memory entry typically requires approximately 4–6 KB (embedding dimensions × 4 bytes + metadata). A 1 GB ElastiCache instance can store approximately 170,000–250,000 memory entries depending on embedding size and metadata.