AIlauncher: LLM Deployment Gateway and Unified Interface Solution for Academic Research

AIlauncher is an LLM deployment tool for academic research developed by ICI-Laboratories. It provides an OpenAI-compatible API gateway, multi-backend support (llama.cpp/Ollama), model catalog management, and an automatic fallback mechanism. It aims to simplify the application of LLMs in research and production environments and solve the pain point of researchers frequently switching models and backends.

Original Author and Source: Maintained by ICI-Laboratories, the project is hosted on GitHub (link: https://github.com/ICI-Laboratories/AIlauncher), released on June 15, 2026.

Project Positioning: Evolved from a locally coupled llama.cpp server to an LLM application gateway layer, the core goal is to allow users to access via a single URL, with the gateway automatically resolving the engine and model.

Core Idea: Targeting academic scenario needs, it solves the problem of traditional deployment requiring separate endpoint configuration for each model, supporting both rapid prototype experiments and production stability.

Architecture Components: Includes model catalog (centralized management of model configurations and aliases), capability parser (intelligent request routing), multi-backend support (llama.cpp/Ollama), and OpenAI-compatible API (reducing migration costs).

Request Flow: Request arrives at the gateway endpoint → Capability parser analyzes the request → Selects target model → Forwards to corresponding backend → Returns response.

Key Features: Automatic fallback mechanism (automatically switches to backup models based on model capabilities), request logs (records interaction information in JSON Lines format), flexible configuration (single model/catalog mode, environment variable support).

Deployment Example: Production deployment command enables request logs and limits log length (e.g., lmserv serve --catalog deploy/models.server.json --port 8009 --request-log-path logs/requests.jsonl); optimized configuration for SARA applications (disable thinking mode, context length 4096, GPU acceleration, etc.).

Client Integration: Through the OpenAI-compatible API, Python examples can directly use the OpenAI client library for access, and existing tools (LangChain, LlamaIndex) can be used without modifying code.

Technical Value: Reduces technical barriers (no need for in-depth backend configuration), supports experimental reproducibility (detailed logs), flexible model management (rapid switching and A/B testing), production-ready features (automatic fallback, health checks).

Application Scenarios: Academic research prototype development, small-scale production deployment, multi-model comparison experiments, etc.

Current Status: The basic gateway architecture has been implemented, and the documentation system is complete (covering architecture, deployment, GPU optimization, etc.).

To-be-Implemented Features: Token-by-token streaming transmission, distributed load balancing, external tool connectors, observability metrics, performance evaluation.

Outlook: With the improvement of features, it is expected to become an important reference implementation for academic LLM infrastructure.