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Lloyal
Ships AI agents inside a local TypeScript app with built-in inference and no external APIs
About Lloyal
LLoyal provides a runtime environment for running AI agents directly within a TypeScript application on the user’s device. It eliminates the need for external API calls by embedding the model and its inference pipeline inside the app, allowing offline operation without requiring API keys or network access. The system supports multi-agent reasoning where multiple agents can share a single model instance, reducing memory and compute overhead by leveraging a shared key-value cache. Agents can be spawned efficiently using a single memory-copy operation, enabling parallel processing without proportional increases in cost or resource usage. The platform includes a library of prebuilt abilities that agents can use to interact with the world and perform tasks, with abilities ranging from simple tool calls to complex skills that incorporate context and lineage. Models are quantized and optimized for local execution, and the same application can run on a laptop, office workstation, or GPU cluster without modification. The system is designed for developers building AI-powered applications that require privacy, low latency, or offline functionality.
Key features
- Local inference without API keys
- Multi-agent runtime with shared KV cache
- Built-in abilities for agent actions
- Offline operation capability
- Cross-platform TypeScript app deployment
- Model quantization for local efficiency
- Memory-efficient agent spawning
- Integrated research and reasoning tools
Use cases
- Building private AI assistants that work offline
- Developing local AI agents for research or analysis
- Creating AI-powered applications for edge devices
Pros
- Runs entirely on-device with no external API dependencies
- Supports multi-agent reasoning with shared memory and compute
- Ships as a downloadable TypeScript app for offline use
- Includes prebuilt abilities for agent interactions
- Optimized for local execution with quantized models
Cons
- No cloud-based API or hosted service option
- Limited to TypeScript-based applications
- Requires local hardware capable of running large models
- No support for non-local deployment models
Lloyal videos
Frequently asked questions about Lloyal
What does Lloyal do?
Lloyal provides a runtime environment for running AI agents directly within a TypeScript application on the user’s device. It embeds the model and its inference pipeline inside the app, enabling offline operation without external API calls, API keys, or network access.
Who is Lloyal designed for?
Lloyal is designed for developers building AI-powered applications that require privacy, low latency, or offline functionality. It suits use cases where local execution, multi-agent reasoning, and shared resources are critical.
How does Lloyal handle multi-agent reasoning?
Lloyal supports multi-agent reasoning by allowing multiple agents to share a single model instance and a key-value cache. This reduces memory and compute overhead, as spawning additional agents involves minimal resource increases.
Can Lloyal run on different hardware setups?
Yes, the same Lloyal application can run on a laptop, office workstation, or GPU cluster without modification. The system is designed to adapt to various hardware environments seamlessly.
What are Abilities in Lloyal?
Abilities are prebuilt functions or skills that agents use to interact with the world and perform tasks. They run inside the model’s execution, enabling context-aware actions and state sharing among agents.
How do I get started with Lloyal?
Users can start by using the Lloyal CLI to create a new project, such as a desktop or web application. The system provides templates and a library of models and abilities to integrate directly into the app.
