## What Is a Language Model Client?

A Language Model client is a software component or application that interacts with a language model via a RESTful API. The client sends requests over HTTP(S), supplying a prompt and optional parameters, and then processes the response returned by the service. This architecture abstracts away the complexities of model hosting, scaling, and updates, allowing developers to focus on application logic.


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## Thin vs. Thick Clients

Language Model clients generally fall into two categories based on where and how much processing they handle: **Thin Clients** and **Thick Clients**.

### Thin Clients

A thin client is designed to be lightweight and stateless. It primarily acts as a straightforward conduit that relays user prompts and parameters directly to the language model service and passes the raw response back to the application, similar to how a remote control sends commands without processing them. Key characteristics include:

- **Minimal Processing**: Performs little to no transformation on the input prompt or the output response beyond basic formatting and validation.
- **Low Resource Usage**: Requires minimal CPU and memory, making it easy to deploy in resource-constrained environments like IoT devices or edge servers.
- **Model Support**: Supports both small-footprint models (e.g., `*-mini`, `*-nano`) for low-latency tasks and larger models (e.g., GPT O3 Pro, Sonnet 4 Opus) when higher accuracy or more complex reasoning is required.
- **Agentic Capabilities**: Supports function calls for agentic workflows, enabling dynamic tool or API integrations that allow the client to perform actions based on LLM responses.
- **Ease of Maintenance**: Simple codebase with few dependencies, leading to easier updates and debugging.
- **Self-Sufficiency**: Can operate independently without bundling additional applications, ideal for lightweight deployments.



**Use Case:** A CLI code assistant like **aider.chat**, which runs as a command-line tool, maintains session context, refines developer prompts, handles fallbacks, and integrates with local code repositories before sending requests to the LLM and processing responses for display in the terminal.

### Thick Clients

A thick client handles more logic locally before and after communicating with the LLM service. It may preprocess prompts, manage context, cache results, or post-process responses to enrich functionality. Key characteristics include:

- **Higher Resource Usage**: Requires more CPU, memory, and possibly GPU resources, as it performs advanced processing locally.
- **Model Requirements**: Typically designed to work with larger, full-weight models (e.g., GPT-4, Llama 65B), leveraging richer capabilities at the cost of increased latency and resource consumption.
- **Enhanced Functionality**: Offers capabilities like local caching for rate limiting, advanced analytics on responses, or integration with other local services (e.g., databases, file systems).
- **Inter-Client Communication**: Supports Model Context Protocol (MCP) or Agent-to-Agent (A2A) workflows, enabling coordination and task delegation among multiple agent instances.
- **Bundled Integration**: Often bundled or coupled with desktop or web applications to provide a richer user interface and additional features.



**Use Case:** A desktop application that manages multi-turn conversations, maintains state across sessions, and integrates user-specific data before sending refined prompts to the LLM and processing the returned content for display.


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Next, we can explore considerations such as security, scaling, and best practices for choosing between thin and thick clients.