Conversation API - Build chatbots with memory using just an API
by•
Building AI chat features often means too much complexity — SDKs, databases, and infrastructure — just to support conversations and memory.
Conversations API removes that overhead.
Build stateful AI chat without managing backend systems. All chat data is stored for you — you only keep the conversation_id.
What it helps with
– AI chat memory & state
– Faster prompt iteration
– No backend setup
– Ideal for low-code builders
Built after helping builders stuck on AI chat infra instead of product.
Replies
HtmlDrag
The 'no glue code' promise is exactly what we need right now. Managing backend state for LLMs is such a headache. How does Amarsia handle long-term memory retrieval—is it vector-based or something more structured?
Amarsia
@htmldrag Thanks!
We don’t support long-term memory on our side yet, but if this is something multiple users are asking for, we’re happy to add it to our near-term roadmap.
Why is it superior to other products?
Amarsia
@vayne_kk It saves teams from building and maintaining all the infrastructure and code that comes with production-grade AI features.
We’ve seen that provider SDKs make it easy to build demo-ready AI, but things often break in production—with little visibility into what went wrong or why.
A big part of that pain comes from unnecessary custom code.
Interesting approach to handling conversation state. I'm curious about multilingual support — how does the API handle context and memory for non-English conversations? Also, does it offer any webhook integration for real-time events like new messages or conversation summaries?
Amarsia
@yamamoto7 Great question!!
Most modern LLMs are multilingual by default, and we support the latest models from multiple providers. As a result, non-English conversations are handled exactly the same as English ones—context is stored uniformly as chat history.
In addition, our knowledge base (RAG) feature is built to be language-agnostic. It supports multiple languages, diverse file types, and uses vision and OCR to extract and construct the right context for AI workflows.
We don’t currently support webhook events since our flow is designed as a one-time transaction. However, we do offer a streaming response API that delivers token-by-token output, similar to the ChatGPT experience.
how does Amarsia handle context window optimization as the conversation history grows?
Amarsia
@landy2 Great question!
This isn’t something we’ve optimized for yet, but given the amount of feedback and interest, optimizing the context window is something we plan to add to the conversation roadmap.
Good idea to include memory out of the box, that's a big time savings for a new chat setup!
Looking forward to checking this out! In my previous startup we dealt with conversation memory a lot and I still believe AI can help humans communicate better.
Didn't you use a database?
Sounds interesting! We’re actually building several AI startups, so we’ll take a look at your product with the team.