workflowstacks
local-ai

Llama.rn: AI for React Native

Get AI capabilities for mobile apps with llama.rn, for founders using React Native
1,010 stars110 forksC++Guide quality 8/10Updated 7/28/2026100% free · open source
What it does

Llama.rn allows you to integrate the LLaMA AI model into your React Native applications, enabling local AI capabilities on mobile devices.

When to use it
  • •You're building a mobile app that requires AI-powered features like text analysis or generation without relying on remote servers.
  • •Your React Native project needs to support offline AI functionality, reducing dependence on internet connectivity.
  • •You want to leverage the LLaMA model's capabilities in a React Native environment for tasks such as language translation or summarization.
Ready-to-paste prompt
const LlamaRN = require('./LlamaRN'); LlamaRN.predict('This is a test input');
Heads up: Ensure you have the necessary C++ build tools and environment setup to compile llama.cpp, as the React Native binding relies on the native module being built correctly.
Saves to your device
Use with Claude
New

Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.

🛠️ Technical setup

Expect 20–40 minutes in a terminal — or let your AI agent drive it.

Try it instantly — no install
Claude Code
mkdir -p ~/.claude/skills/llama-rn && curl -fsSL https://workflowstacks.com/api/skills/llama-rn/claude-skill -o ~/.claude/skills/llama-rn/SKILL.md
Open in another AI app

Opens the app with this repo with the prompt ready to go — no copy-paste needed.

Connect the whole catalog (MCP)
claude mcp add --transport http workflowstacks https://workflowstacks.com/api/mcp

Adds a WorkflowStacks connector to Claude Code: search and load any skill here by chatting.

How Llama.rn: AI for React Native works
Codeflow
Free to inspect

Llama.rn: AI for React Native is a very large C++ project (~365k lines across 743 code files, plus 24 test files). It is a full software project: use it through its install path rather than reading it end to end. Last commit this month, MIT license, has a test suite.

Size
Very large codebase
~365k lines · 743 code files · days to read — use, don't read
Setup
Developer setup
A real software project. Use it via its install path; don't expect to read it all.
Runs on
Node.js
No API keys detected
C++ 54%C 39%TypeScript 4%Metal 2%
Where to start reading
  1. 1
    README.md
    Start here — what it does and how to install it
  2. 2
    AGENTS.md
    The instructions the AI actually follows
  3. 3
    src/index.ts
    Where the program starts running
  4. 4
    package.json
    Dependencies and the commands it exposes
  5. 5
    example/.gitignore
    A worked example — copy this to get going
What's in each folder
src/Core code — the actual logic11 files
example/Examples you can copy147 files
docs/Documentation7 files
cpp/ggml-opencl/Folder175 files
cpp/models/Data models & types152 files
cpp/ggml-hexagon/Folder95 files
cpp/codec/Folder92 files
cpp/tools/Helper scripts67 files
READMEHas testsDocumentedCI checksExamples includedMIT licenseUpdated this month
Quick Actions
Details
Creator
mybigday
Language
C++
Category
local-ai
Published
8/2/2023

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