Best practices

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.

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.

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",
)

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.