Frequently Asked Questions

Doesn't ChatGPT already have memory? Why would I need MemChain?

OpenAI's memory helps ChatGPT remember basic facts about you, not your application. It can't be scoped per project, user, or agent. MemChain gives you programmable memory-letting your application persist knowledge, structure it, share it, and reason over it with full control.

If I use OpenAI's memory feature, isn't that enough for my app?

No. OpenAI's memory is opaque and built for consumer-level interactions. MemChain gives you explicit memory control-you decide what gets saved, summarized, shared, or retrieved.

Can't I just store context in a database and pass it to the LLM?

You can store context manually, but you're missing everything MemChain enables for your application: persistent memories, threaded conversations, federation, summarization, audit trails, and observability.

What problem does MemChain solve that the LLM itself doesn't?

LLMs are stateless. They have no built-in ability to persist knowledge across time or interactions. Every prompt starts from scratch. MemChain fills this missing layer, enabling four critical capabilities: persistence, sharing, summarization, and agent-level reasoning over memory.

Isn't context just about using longer prompts or more tokens?

Prompt stuffing can work temporarily, but it's brittle. LLMs have token limits, and large prompts cost more and perform worse.

Can't I fine-tune a model with my data instead of using memory?

Fine-tuning is static. Memory is dynamic. You want your AI to learn from real-time interactions, not require retraining for every new insight.

Why not just build a memory layer using Redis, Postgres, or Pinecone?

You could, but you'd spend months re-inventing what MemChain already delivers: scoped memory, federation, summarization, audit trails, and a fully compliance infrastructure. Remember, building a fully compliant infrastructure isn't easy.

Isn't this just another vector database?

No. MemChain uses vector/semantic search, but it is not just a vector DB. It is a memory orchestration layer built for AI agents with support for summarization, federations, usage enforcement, and plan-based access.

What makes MemChain different from building our own memory management system?

Speed, security, and production-grade from day one. MemChain handles compliance, observability, access control, and scalability. Building that right is extremely costly, and most teams underestimate what it takes.

What features do I get by using MemChain instead of rolling my own?

Does MemChain handle multi-user and multi-agent scenarios?

Yes. It was designed specifically for multi-tenant, multi-agent, and multi-scope environments. Each memory is tied to a scope and tenant with strict access boundaries. Of course it can be used for single-agent environments too.

Can MemChain summarize or reflect on past interactions automatically?

Yes. You can trigger reflections or summarizations using APIs. This is useful for building agents that learn, review, or generate long-term insights.

How does MemChain handle privacy and compliance (e.g. GDPR, HIPAA)?

MemChain enforces tenant isolation via RLS, provides audit logs, data retention policies, and region-based data residency. It's architected for regulatory compliance.

We're still early—can't we add memory later if needed?

You can, but it becomes harder to retrofit. MemChain makes it easy to start with scoped memory now, even if you're small, and scale up as your app grows.

What if my application doesn't need long-term memory today?

That's fine. MemChain supports both ephemeral and persistent memory. You can start simple and introduce more memory capabilities incrementally.

Is MemChain overkill for a simple chatbot or assistant?

If you're just building a weekend project, maybe. But if you're building a product or a system that grows, you will absolutely need structured memory.

Why should I use MemChain instead of retrieval-augmented generation (RAG) techniques?

RAG is good for static content. MemChain enables context-aware, evolving memory based on live user interactions and agent behavior—not just document search.

How is MemChain better than just stuffing relevant documents into the prompt?

MemChain supports memory selection, summaries, time-aware retention, and importance ranking. Raw document stuffing doesn't scale or adapt.

Will MemChain lock me into a specific LLM provider or architecture?

No. MemChain is model-agnostic. You can use it with OpenAI, Anthropic, local models, or anything else. It simply becomes your memory API layer.

How hard is it to integrate MemChain into an existing AI system?

It takes a few API calls to start saving, retrieving, summarizing, or federating memories. You don't need to rebuild anything—just plug MemChain into your agent workflows.

Can I control what gets saved, how it's searched, and how it's shared between agents?

Yes. You control memory policies per scope, can configure federations, and filter memories during search or summarization.

What makes MemChain "enterprise-grade"?

How does MemChain handle multi-tenancy, usage limits, and feature gating?

Each tenant has configurable plans, rate limits, feature access, and scoped memory. You can enforce usage caps, audit access, and monitor everything in real time.

Is MemChain scalable and observable enough for production workloads?

Yes. It includes full metrics tracking, alerting, and performance monitoring. Memory reads/writes are optimized with indexable vector search and scoped queries.

What do you offer that OpenAI, Azure, or AWS don't already provide?

They offer inference. MemChain offers memory. We're not competing with model providers-we're the missing memory layer they don't give you. None of those platforms offer an opinionated, secure, tenant-aware memory management system for agents. MemChain is a purpose-built memory backbone for AI—something big clouds don't provide because it's not their focus.