WebLLM Agent Demo
Experience client-side AI inference with zero latency and complete privacy
Built with WebLLM open source technology by the MLC-AI team, integrated by Virgent AI for enterprise applications.
Select WebLLM Model
Choose an AI model to download and run in your browser. All models run locally with complete privacy.
Qwen2.5 0.5B
Lightweight model perfect for testing and basic conversations
TinyLlama 1.1B
Lightweight model perfect for testing and basic conversations
Llama 2 7B Chat
Balanced model for general conversation and complex tasks
WebLLM Architecture
Client-side AI inference with complete privacy and control • Powered by WebLLM and integrated by Virgent AI
WebLLM Client-Side AI Architecture
┌─────────────────┐ WebGL/GPU ┌──────────────────┐ Model Storage ┌─────────────────┐
│ Browser Runtime │ ◄──────────────► │ WebLLM Engine │ ◄─────────────► │ IndexedDB Cache │
│ (WASM/JS) │ │ (MLC-AI Runtime) │ │ (Local Models) │
└─────────────────┘ └────────┬─────────┘ └─────────────────┘
│ │
│ Token Generation │ Model Download
▼ ▼
┌─────────────────┐ User Input ┌──────────────────┐ Model Loading ┌──────────────────┐
│ Application UI │ ──────────────► │ Conversation │ ◄─────────────► │ CDN/Model Hub │
│ (React/Chat) │ │ Manager │ │ (HuggingFace) │
└─────────────────┘ └──────────────────┘ └──────────────────┘Virgent AI Value Propositions
Zero server dependency after model download (Cost: $0/month vs $1000s in API fees)
Complete data privacy (HIPAA/SOX compliant - never leaves device)
Sub-100ms response times with local GPU acceleration
No API costs, rate limiting, or network dependencies
Offline functionality for air-gapped or remote environments
Perfect for sensitive data processing in healthcare, legal, finance
Enterprise Integration Points
Embed in existing web applications with zero infrastructure changes
Mobile app integration via WebView or React Native
Desktop app integration via Electron or Tauri
OpenAI-compatible API format for easy migration
Custom model fine-tuning on proprietary data
Hybrid cloud/local routing based on data sensitivity
🎮 Try WebLLM in Action: Agent Sandbox
Want to see WebLLM powering real multi-agent interactions? Check out our Agent Sandbox - a Pokemon-style testbed where AI agents collaborate in real-time. Switch between API mode (20 message limit) and WebLLM mode (unlimited, free) to compare performance and cost.
Watch them collaborate autonomously
See conversations unfold naturally
No API costs or message limits
Technical Foundation
WebLLM: github.com/mlc-ai/web-llm (Apache 2.0 License)
MLC-LLM: mlc.ai/mlc-llm (Universal deployment for LLMs)
WebAssembly: webassembly.org (W3C standard)
WebGPU: w3.org/TR/webgpu (GPU acceleration standard)
Virgent AI Custom Implementation Services
Fine-tuned models for domain-specific use cases
Hybrid architecture design and deployment
Performance optimization and monitoring
Compliance and security assessments
Contact: hello@virgent.ai | virgent.ai/contact
Current AI Challenges
Privacy & Security Risks
Sensitive data sent to external AI APIs creates compliance issues for healthcare, legal, and financial organizations
Escalating API Costs
High-volume AI usage leads to unpredictable monthly bills and budget overruns, especially for customer-facing applications
Network Dependency
Internet outages, API downtime, or poor connectivity breaks AI-powered features and disrupts business operations
Latency & Rate Limits
Network delays and API throttling create poor user experiences in real-time applications and interactive features
WebLLM Solutions
Complete Data Privacy
AI processing happens entirely in the browser - sensitive data never leaves your device, ensuring HIPAA/SOX compliance
Zero Ongoing Costs
Eliminate monthly API bills and usage fees - once deployed, WebLLM runs indefinitely without per-token charges
Offline Capabilities
AI features work without internet connectivity, perfect for remote work, field operations, or air-gapped environments
Sub-100ms Response Times
Local GPU processing delivers instant responses for real-time applications, gaming, and interactive experiences
Important Security Considerations for WebLLM
Before deploying WebLLM or any AI technology in production, understand the security implications
While WebLLM enables powerful client-side AI inference with complete privacy and zero latency, it's crucial to understand the security risks before deploying any LLM-based system in production. Don't just slap WebLLM (or any technology you don't fully understand) onto your project without proper security review.
Privacy Risks of WebLLMs
Random Walk AI has published a comprehensive analysis of WebLLM attack vectors including prompt injection, insecure output handling, zero-shot learning attacks, homographic attacks, and model poisoning. Learn about these vulnerabilities and mitigation strategies before deploying.
Read: Understanding the Privacy Risks of WebLLMs →Official WebLLM Documentation
The official WebLLM library by MLC AI provides high-performance in-browser LLM inference. Review their security guidelines, best practices, and implementation details before production use.
View: WebLLM on GitHub →Need Help Implementing Securely?
Virgent AI specializes in secure AI agent implementation with proper guardrails, evaluation, and observability. We help enterprises deploy LLM-based systems safely with comprehensive security reviews, threat modeling, and production-ready architectures.
Contact us for secure AI implementation →⚠️ This Demo Is For Research & Learning Only
This WebLLM demo showcases client-side AI capabilities in a controlled environment. Production deployments require additional security measures including input sanitization, output validation, rate limiting, content filtering, and comprehensive security audits. Always consult with AI security experts before deploying LLM-based systems that handle sensitive data.
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