Amarsia
Ship AI fast, iterate safely — no glue code.
405 followers
Ship AI fast, iterate safely — no glue code.
405 followers
Amarsia is a reliable AI platform to ship AI features without breaking things.
Build and deploy AI workflows as production-ready APIs without SDKs, databases, or infrastructure. Conversations, state, and outputs are stored automatically so iteration feels safe, not risky. With predictable behavior, versioned changes, and clear visibility, teams move faster with confidence — and spend less time debugging AI in production.
This is the 4th launch from Amarsia. View more
Conversation API
Launched this week
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.
Free Options
Launch Team / Built With
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.
Can we bring our own API keys for different models, or is the billing centralized through Amarsia's infrastructure?
Amarsia
@lightninglx Everything is currently centralized on Amarsia’s infrastructure. If there’s meaningful demand, we can explore offering a self-hosted version in the future.
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.
Really like the direction you’re taking here.
From my experience building and shipping multiple tools, conversation memory and state management is where most AI projects get messy fast — too many SDKs, databases, and custom glue code just to keep context stable.
The “just configure and get an API” approach feels very practical, especially for indie builders and small teams who want to focus on product value, not infra.
Curious to see how this scales with longer conversations and multi-session users, but this is a solid abstraction layer. Nice work 👏
Didn't you use a database?