OSLAH is a private, local-first AI automation platform for Windows and macOS. It empowers developers and teams to run, share, and orchestrate private LLMs locally on their own machines without transmitting any data to third-party cloud providers.
OSLAH is built on an Open-Core distribution model. The core orchestration features are 100% free and open-source under the MIT license, while advanced multi-user routing, network administration, and enterprise security features require a commercial license.
| Feature | 🍃 Free Core Edition | 🏆 Enterprise Pro Edition |
|---|---|---|
| Local LLM Chat Interface | ✅ Yes | ✅ Yes |
| Offline RAG Document Indexing | ✅ Yes | ✅ Yes |
| Ollama Autopilot Provisioning | ✅ Yes | ✅ Yes |
| Hardware Workload Telemetry | ✅ Yes | ✅ Yes |
| LAN HTTP Server Gateway | ❌ No | ✅ Yes |
| Cupertino Switch Control Center | ❌ No | ✅ Yes |
| Admin vs Employee Role Separation | ❌ No | ✅ Yes |
| Administrative Control Endpoints | ❌ No | ✅ Yes |
| SQLite User Log Telemetry | ❌ No | ✅ Yes |
OSLAH is uniquely positioned to capture the offline enterprise AI market by eliminating dependency on cloud infrastructures. For a deep-dive look at our business model, go-to-market strategy, and investor pitch deck, please refer directly to: 📄 OSLAH Corporate Strategy Presentation
- Commercial Licensing & Support: Tailored subscriptions for local offices wishing to run local sandboxed AI gateways with priority technical SLA support.
- 100% Air-Gapped Compliance: Designed specifically for secure environments (such as healthcare, banking, and legal firms) that require strictly zero internet access and zero telemetry leakage.
- Hardware Optimization: Maximizes the utilization of existing office workstation graphics cards (GPUs) to run lightweight local models (DeepSeek-R1, Llama 3) without ongoing token fees.
OSLAH is distributed as a native executable. We use Inno Setup to package and build clean installers for Windows desktop.
- Navigate to the InnoOutput/ directory.
- Launch
OSLAH_Setup.exe. - Follow the installation wizard steps to create desktop shortcuts and run the application.
Ensure you have Flutter SDK and Inno Setup installed.
- Clone the Repository:
git clone https://github.com/beezyman-studio/OSLAH.git cd oslah - Install Dart Dependencies:
flutter pub get
- Compile Release Binary:
flutter build windows --release
- Compile Setup Installer:
Open a terminal and run the Inno Setup compiler compiler:
The output installer will be generated at
iscc oslah_installer.iss
InnoOutput/OSLAH_Setup.exe.
When setting up OSLAH, you can choose and download the local LLM model weights matching your hardware profiles:
| Model Option | Parameters | Core Strengths (PROS) | Drawbacks (CONS) | Malayalam Support | CPU / RAM Profile |
|---|---|---|---|---|---|
| DeepSeek-R1 | 7B |
🧠 Logical step-by-step thinking (<think> tags). Excellent math & coding capabilities. |
⏳ Slower initial token stream due to reasoning cycles. | Excellent 🌟 (Superb translations & comprehension) | Moderate-High (8GB+ RAM / VRAM) |
| Llama 3 | 8B |
💬 Highly conversational, fast streams. Extremely robust general knowledge base. | 🌐 Lacks native reasoning outputs. | Basic |
Moderate-High (8GB+ RAM / VRAM) |
| Phi-3 | 3.8B |
⚡ Ultra-lightweight & fast. Runs efficiently on basic laptop CPU configurations. | 📉 Limited reasoning scope. Smaller context. | Poor ❌ (Basic vocabulary matching only) | Low-Lightweight (4GB+ RAM / VRAM) |
OSLAH is designed with local enterprise security compliance in mind:
- Incoming requests outside the host require a valid API header token:
X-OSLAH-KeyorAuthorization: Bearer <API_KEY>. - Access rules are verified by the internal role-based middleware (
AdminvsEmployeekeys). - Incoming payloads are parsed using a strict sequential queue to prevent local memory overflow or CPU exhaustion DOS attacks.
This project is licensed under the MIT License - see the LICENSE file for details. Custom Enterprise modules are subject to commercial licensing terms.
BeezyMan Studio Kerala