ferranpons/Llamatik — True on-device AI for Kotlin Multiplatform (Android, iOS, Desktop, JVM, WASM).

Description and use

**Brief summary**
Llamatik is an open‑source Kotlin Multiplatform library that brings on‑device large‑language‑model (LLM), speech‑to‑text, and image‑generation capabilities to Android, iOS, desktop, JVM, and WebAssembly apps by wrapping llama.cpp, whisper.cpp and stable‑diffusion.cpp. It enables developers to embed powerful AI features locally, without relying on cloud APIs, making internal knowledge bases searchable and usable by conversational assistants.

**Value**

• **Privacy & latency** – All inference runs on the device, so sensitive corporate data never leaves the user’s hardware and responses are returned instantly.
• **Cross‑platform consistency** – A single Kotlin codebase powers AI features on mobile, desktop and web, reducing duplicate effort and maintenance.
• **Cost‑effective prototyping** – By leveraging the lightweight C++ back‑ends (llama, whisper, stable‑diffusion), teams can experiment with LLM‑driven search, document grounding, and multimodal assistants without paying for external API usage.

**Practical adoption path**

1. **Proof‑of‑concept** – Clone the repo, follow the README to build the native binaries for the target platform(s), and run the supplied example that indexes a small knowledge base and answers queries.
2. **Integration scaffolding** – Wrap the provided Kotlin APIs in your existing data‑access layer (e.g., a local SQLite or vector store) and expose a simple service that your UI or chatbot can call.
3. **Iterative scaling** – Replace the demo model with a larger, domain‑specific LLM or fine‑tuned whisper model, and benchmark memory/CPU usage on representative devices.
4. **Production hardening** – Add error handling, model‑download verification, and automated model updates; package the native binaries with your app’s release pipeline.

**Production readiness**
Llamatik sits at a medium readiness level. It is mature enough for internal prototypes and low‑traffic production workloads, as evidenced by 116 GitHub stars and recent activity (updated 2026‑05‑11). However, the integration steps are not fully documented; teams should allocate time to verify the build process for each target platform, assess binary size and runtime resource consumption, and establish a maintenance plan for the underlying C++ libraries. Once those checks are completed, Llamatik can be safely used in internal tools and, with additional testing, in customer‑facing applications.

Problem

ferranpons/Llamatik helps make internal knowledge searchable and usable by assistants.

Outcome

Medium: useful for prototypes or internal workflows, with dependency and maintenance checks before production.

Workflow

• index knowledge bases
• improve search over documents
• ground assistant answers

Evidence

True on-device AI for Kotlin Multiplatform (Android, iOS, Desktop, JVM, WASM). LLM, Speech-to-Text and Image Generation — powered by llama.cpp, whisper.cpp and stable-diffusion.cpp.

Source