tried integrating EverMemOS into production, infrastructure reality check

been working on adding persistent memory to our customer support chatbot. looked at several options and decided to try EverMemOS since the technical approach seemed solid.

the infrastructure requirements are pretty substantial. needs MongoDB, Elasticsearch, Milvus, AND Redis running. plus separate API keys for LLM and embedding services. definitely more complex than typical setups.

took about a day to get the docker-compose stack working smoothly. Milvus needed more memory than expected on our 8GB instances. ended up bumping to 12GB to handle all the dependencies comfortably. 

the actual integration wasnt too bad once everything was running. their API is straightforward, POST messages, GET search results. but the behind-the-scenes complexity is real.

what surprised me was how much the memory extraction process relies on LLM calls. every conversation gets processed through their memory layer which calls out to OpenAI/Gemini. adds latency and costs but the structured output is actually pretty good.

tried it with about 1000 real customer conversations. the memory recall is genuinely impressive, it connects context across sessions that our previous RAG setup completely missed. customer asks about "that issue from last week" and it actually finds the right conversation.

but the operational overhead is significant. monitoring 4 different databases, managing vector indexes, keeping embedding models updated. way more complex than just throwing conversation history into context.

for our use case the memory quality improvement justified the complexity. but if youre just doing simple Q&A, probably overkill.

the hybrid retrieval (semantic + keyword) works well. much better than pure vector search which misses exact matches sometimes.