CEF.AI
Official@cef-ai
Offers a specialized framework for managing agent service lifecycles, persistent state storage, and inline model inference within enterprise environments.
Agent Skills by CEF.AI
Showing 5 vetted skills indexed across 1 GitHub repositories.
CEF-AIcef-cli
Manage CEF agent service lifecycles via CLI commands and cef.config.yaml.
CEF-AIcef-cubby-state
Store and manage persistent cubby state for CEF agents via the Cubby API.
CEF-AIcef-client-sdk
Bridge external applications to CEF agent services using the @cef-ai/client-sdk.
CEF-AIcef-inference
Run inline ML model inference in CEF agent handlers via context.models.
CEF-AIcef-coding
Generate inline CEF agent handlers and projects from natural language goals.
Frequently Asked Questions About CEF.AI
FAQPage SchemaWhat specific tasks does CEF.AI enable for developers?▼
CEF.AI enables developers to manage agent service lifecycles, maintain persistent state through Cubby storage, and execute inline model inference. It facilitates the generation of handler logic and project structures from natural language goals, bridging external applications to internal service architectures.
Which technical personas benefit from using CEF.AI?▼
CEF.AI is designed for backend engineers, machine learning practitioners, and systems architects building distributed agent-based services. It targets professionals requiring granular control over service configuration, state persistence, and integrated model execution within their existing infrastructure.
What are the primary prerequisites for deploying CEF.AI?▼
Deployment requires a configured environment supporting the CEF service lifecycle management. Users must define service parameters within a cef.config.yaml file and ensure connectivity to the Cubby state management system for persistent data operations.