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orailnoor/cross-platform-llm-client

A unified cross-platform AI client supporting seamless transitions between standard cloud APIs and on-device, offline execution of custom and uncensored language models. observed · 2026-08-28

github.com/orailnoor/cross-platform-llm-client · C++ · MIT (permissive) observed · 2026-08-28

Health v2 · maintenance only

72/100

  • Activity 93
  • Release rhythm 81
  • Longevity 9

Flags: young

How is this computed?

round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10) — computed 2026-09-03. Adoption (stars, forks) is never an input.

  • gap_med: 84
  • age_days: 130
  • days_rel: 44
  • days_push: 44
  • n_releases_24m: 2

Full methodology

Adoption not part of the score

1051 stars · 212 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

PrivateLM is a cross-platform AI chat client built with Flutter that unifies local on-device LLM inference (GGUF models with Vulkan GPU acceleration on Android) with cloud API access to providers like OpenAI, Anthropic, and Gemini. It supports multimodal text-and-image chat, persistent local storage of sessions, and automatic device-based configuration of context and token limits.

Use cases

  • run uncensored language models offline on my android phone
  • chat with local gguf models without internet
  • switch between local and cloud llm providers in one app
  • private ai chat that keeps data on device
  • run vision models like qwen2-vl on mobile
  • generate images on-device with a phone gpu
  • unified client for openai anthropic and gemini apis

When to choose

  • you want offline, on-device LLM inference on Android with GPU acceleration
  • you need a single client that falls back to multiple cloud providers
  • privacy matters and you want chats stored locally only
  • you want multimodal (text + image) chat with both local and cloud models

When to avoid

  • you need local inference on iOS or desktop, which is not supported yet
  • you require heavy cloud-only workloads beyond what phone hardware can run
  • you need a server-side or API-first integration rather than an end-user app

Facets

application · maturity active

llm-inference chatbot chat-interface image-processing http-client state-management file-upload large-language-models chatbots artificial-intelligence mobile-development privacy cross-platform cross-platform gguf on-device-inference vulkan-gpu flutter-app local-first multimodal-chat cloud-api-fallback uncensored-models hive-storage getx android mobile web-server flutter

1 source

Member repositories

RepositoryRoleHealth v2
orailnoor/cross-platform-llm-clientmain72

For agents

markdown · JSON · MCP: product_card(name="orailnoor/cross-platform-llm-client")

Data as of 2026-08-30T08:39:29.467469+00:00 · Report a problem