The Privacy-First Alternative to Ollama

🔒 Local AI Without the Lock-in 🚀

Shimmy will be free forever. No asterisks. No "free for now." No pivot to paid.

💝 Support Shimmy's Growth

🚀 If Shimmy helps you, consider sponsoring — 100% of support goes to keeping it free forever.

• $5/month: Coffee tier ☕ - Eternal gratitude + sponsor badge
• $25/month: Bug prioritizer 🐛 - Priority support + name in SPONSORS.md
• $100/month: Corporate backer 🏢 - Logo placement + monthly office hours
• $500/month: Infrastructure partner 🚀 - Direct support + roadmap input

🎯 Become a Sponsor | See our amazing sponsors 🙏

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Drop-in OpenAI API Replacement for Local LLMs

Shimmy is a 5.1MB single-binary that provides 100% OpenAI-compatible endpoints for GGUF models. Point your existing AI tools to Shimmy and they just work — locally, privately, and free.

🤔 What are you building with Shimmy?

New developer tools and specifications included! Whether you're forking Shimmy for your application or integrating it as a service, we now provide:

• 🔧 Integration Templates: Copy-paste guidance for embedding Shimmy in your projects
• 📋 Development Specifications: GitHub Spec-Kit methodology for planning Shimmy-based features
• 🛡️ Architectural Guarantees: Constitutional principles ensuring Shimmy stays reliable and lightweight
• 📖 Complete Documentation: Everything you need to build on Shimmy's foundation

Building something cool with Shimmy? These tools help you do it systematically and reliably.

🚀 GitHub Spec-Kit Integration

Shimmy now includes GitHub's brand-new Spec-Kit methodology – specification-driven development that just launched in September 2025! Get professional-grade development workflows:

• 🏗️ Systematic Development: /specify → /plan → /tasks → implement
• 🤖 AI-Native Workflow: Works with Claude Code, GitHub Copilot, and other AI assistants
• 📋 Professional Templates: Complete specification and planning frameworks
• 🛡️ Constitutional Protection: Built-in governance and architectural validation

📖 Complete Developer Guide → • 🛠️ Learn GitHub Spec-Kit →

Try it in 30 seconds

# 1) Install + run
cargo install shimmy --features huggingface
shimmy serve &

# 2) See models and pick one
shimmy list

# 3) Smoke test the OpenAI API
curl -s http://127.0.0.1:11435/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{
        "model":"REPLACE_WITH_MODEL_FROM_list",
        "messages":[{"role":"user","content":"Say hi in 5 words."}],
        "max_tokens":32
      }' | jq -r '.choices[0].message.content'

🚀 Works with Your Existing Tools

No code changes needed - just change the API endpoint:

• VSCode Extensions: Point to http://localhost:11435
• Cursor Editor: Built-in OpenAI compatibility
• Continue.dev: Drop-in model provider
• Any OpenAI client: Python, Node.js, curl, etc.

Use with OpenAI SDKs

• Node.js (openai v4)

import OpenAI from "openai";

const openai = new OpenAI({
  baseURL: "http://127.0.0.1:11435/v1",
  apiKey: "sk-local", // placeholder, Shimmy ignores it
});

const resp = await openai.chat.completions.create({
  model: "REPLACE_WITH_MODEL",
  messages: [{ role: "user", content: "Say hi in 5 words." }],
  max_tokens: 32,
});

console.log(resp.choices[0].message?.content);

• Python (openai>=1.0.0)

from openai import OpenAI

client = OpenAI(base_url="http://127.0.0.1:11435/v1", api_key="sk-local")

resp = client.chat.completions.create(
    model="REPLACE_WITH_MODEL",
    messages=[{"role": "user", "content": "Say hi in 5 words."}],
    max_tokens=32,
)

print(resp.choices[0].message.content)

⚡ Zero Configuration Required

• Auto-discovers models from Hugging Face cache, Ollama, local dirs
• Auto-allocates ports to avoid conflicts
• Auto-detects LoRA adapters for specialized models
• Just works - no config files, no setup wizards

🎯 Perfect for Local Development

• Privacy: Your code never leaves your machine
• Cost: No API keys, no per-token billing
• Speed: Local inference, sub-second responses
• Reliability: No rate limits, no downtime

Quick Start (30 seconds)

Installation

🪟 Windows

# RECOMMENDED: Use pre-built binary (no build dependencies required)
curl -L https://github.com/Michael-A-Kuykendall/shimmy/releases/latest/download/shimmy.exe -o shimmy.exe

# OR: Install from source (requires LLVM/Clang)
# First install build dependencies:
winget install LLVM.LLVM
# Then install shimmy:
cargo install shimmy --features huggingface

⚠️ Windows Notes:

• Pre-built binary recommended to avoid build dependency issues
• If Windows Defender flags the binary, add an exclusion or use cargo install
• For cargo install: Install LLVM first to resolve libclang.dll errors

🍎 macOS / 🐧 Linux

# Install from crates.io
cargo install shimmy --features huggingface

GPU Acceleration

Shimmy supports multiple GPU backends for accelerated inference:

🖥️ Available Backends

Backend	Hardware	Installation
CUDA	NVIDIA GPUs	cargo install shimmy --features llama-cuda
Vulkan	Cross-platform GPUs	cargo install shimmy --features llama-vulkan
OpenCL	AMD/Intel/Others	cargo install shimmy --features llama-opencl
MLX	Apple Silicon	cargo install shimmy --features mlx
All GPUs	Everything	cargo install shimmy --features gpu

🔍 Check GPU Support

# Show detected GPU backends
shimmy gpu-info

⚡ Usage Notes

• GPU backends are automatically detected at runtime
• Falls back to CPU if GPU is unavailable
• Multiple backends can be compiled in, best one selected automatically
• Use --gpu-backend <backend> to force specific backend

Get Models

Shimmy auto-discovers models from:

• Hugging Face cache: ~/.cache/huggingface/hub/
• Ollama models: ~/.ollama/models/
• Local directory: ./models/
• Environment: SHIMMY_BASE_GGUF=path/to/model.gguf

# Download models that work out of the box
huggingface-cli download microsoft/Phi-3-mini-4k-instruct-gguf --local-dir ./models/
huggingface-cli download bartowski/Llama-3.2-1B-Instruct-GGUF --local-dir ./models/

Start Server

# Auto-allocates port to avoid conflicts
shimmy serve

# Or use manual port
shimmy serve --bind 127.0.0.1:11435

Point your AI tools to the displayed port — VSCode Copilot, Cursor, Continue.dev all work instantly.

📦 Download & Install

Package Managers

• Rust: cargo install shimmy
• VS Code: Shimmy Extension
• npm: npm install -g shimmy-js (coming soon)
• Python: pip install shimmy (coming soon)

Direct Downloads

• GitHub Releases: Latest binaries
• Docker: docker pull shimmy/shimmy:latest (coming soon)

🍎 macOS Support

Full compatibility confirmed! Shimmy works flawlessly on macOS with Metal GPU acceleration.

# Install dependencies
brew install cmake rust

# Install shimmy
cargo install shimmy

✅ Verified working:

• Intel and Apple Silicon Macs
• Metal GPU acceleration (automatic)
• Xcode 17+ compatibility
• All LoRA adapter features

Integration Examples

VSCode Copilot

{
  "github.copilot.advanced": {
    "serverUrl": "http://localhost:11435"
  }
}

Continue.dev

{
  "models": [{
    "title": "Local Shimmy",
    "provider": "openai", 
    "model": "your-model-name",
    "apiBase": "http://localhost:11435/v1"
  }]
}

Cursor IDE

Works out of the box - just point to http://localhost:11435/v1

Why Shimmy Will Always Be Free

I built Shimmy to retain privacy-first control on my AI development and keep things local and lean.

This is my commitment: Shimmy stays MIT licensed, forever. If you want to support development, sponsor it. If you don't, just build something cool with it.

💡 Shimmy saves you time and money. If it's useful, consider sponsoring for $5/month — less than your Netflix subscription, infinitely more useful for developers.

API Reference

Endpoints

• GET /health - Health check
• POST /v1/chat/completions - OpenAI-compatible chat
• GET /v1/models - List available models
• POST /api/generate - Shimmy native API
• GET /ws/generate - WebSocket streaming

CLI Commands

shimmy serve                    # Start server (auto port allocation)
shimmy serve --bind 127.0.0.1:8080  # Manual port binding
shimmy list                     # Show available models  
shimmy discover                 # Refresh model discovery
shimmy generate --name X --prompt "Hi"  # Test generation
shimmy probe model-name         # Verify model loads

Technical Architecture

• Rust + Tokio: Memory-safe, async performance
• llama.cpp backend: Industry-standard GGUF inference
• OpenAI API compatibility: Drop-in replacement
• Dynamic port management: Zero conflicts, auto-allocation
• Zero-config auto-discovery: Just works™

🚀 Advanced Features

• Smart Model Preloading: Background loading with usage tracking for instant model switching
• Response Caching: LRU + TTL cache delivering 20-40% performance gains on repeat queries
• Integration Templates: One-command deployment for Docker, Kubernetes, Railway, Fly.io, FastAPI, Express
• Request Routing: Multi-instance support with health checking and load balancing
• Advanced Observability: Real-time metrics with self-optimization and Prometheus integration

Community & Support

• 🐛 Bug Reports: GitHub Issues
• 💬 Discussions: GitHub Discussions
• 📖 Documentation: docs/ • Engineering Methodology • OpenAI Compatibility Matrix • Benchmarks (Reproducible)
• 💝 Sponsorship: GitHub Sponsors

Star History

🚀 Momentum Snapshot

📦 Sub-20MB single binary (34x smaller than Ollama)
🌟 stars and climbing fast
⏱ <1s startup
🦀 100% Rust, no Python

📰 As Featured On

🔥 Hacker News • Front Page Again • IPE Newsletter

Companies: Need invoicing? Email [email protected]

⚡ Performance Comparison

Tool	Binary Size	Startup Time	Memory Usage	OpenAI API
Shimmy	10-20MB	<100ms	50MB	100%
Ollama	680MB	5-10s	200MB+	Partial
llama.cpp	89MB	1-2s	100MB	None

Quality & Reliability

Shimmy maintains high code quality through comprehensive testing:

• Comprehensive test suite with property-based testing
• Automated CI/CD pipeline with quality gates
• Runtime invariant checking for critical operations
• Cross-platform compatibility testing

See our testing approach for technical details.

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License & Philosophy

MIT License - forever and always.

Philosophy: Infrastructure should be invisible. Shimmy is infrastructure.

Testing Philosophy: Reliability through comprehensive validation and property-based testing.

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Forever maintainer: Michael A. Kuykendall
Promise: This will never become a paid product
Mission: Making local AI development frictionless