Grab's Agent Framework LLM-Kit Accelerates AI Agent Production Deployment
Grab has implemented LLM-Kit, a framework that standardizes over 500 internal agent services. This system enhances service integration, evaluation, and secret handling, reducing the time to deploy new AI agents from two weeks to one hour. It centralizes infrastructure management, allowing runtime tool discovery and flexible model integration, while maintaining operational control. By Hien Luu
Grab has standardized over 500 internal agent services on LLM-Kit, an internal framework that provides a basic agent loop with evaluation, tracing, secret handling, and tool-server connections already wired in. This has accelerated AI agent production deployment from taking two weeks or more to just about an hour. LLM-Kit, which backs more than 500 services at Grab, eliminates the need for per-service decisions by providing scaffolding around existing infrastructure, such as secrets, tracing, service discovery, and evaluation.
The framework is intentionally not a new agent abstraction or a domain-specific language, but rather a centralized solution to the problem of solving these issues service by service.
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