memory-providers

Name: memory-providers
Availability: InStock
Author: Emergent

Manage LLM provider credentials, model catalogs, and usage reporting for the Emergent platform.

1|Updated Feb 6, 2026

Search Tags:#llm #credentials #api keys #google ai #model management #vertex ai

Ready to run

npx skills add https://github.com/emergent-company/emergent.memory --skill memory-providers

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memory-providers
Source: https://github.com/emergent-company/emergent.memory/tree/main/tools/cli/internal/skillsfs/skills/memory-providers
Command: npx skills add https://github.com/emergent-company/emergent.memory --skill memory-providers

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill simplifies the complex process of configuring and managing access to various Large Language Models (LLMs) and their associated credentials, ensuring seamless integration with your AI workflows.

Core Features & Use Cases

Credential Management: Securely configure API keys and service accounts for LLM providers like Google AI and Vertex AI.
Model Catalog: Browse and select available generative and embedding models from configured providers.
Usage Monitoring: Track token consumption and estimated costs for LLM usage.
Use Case: A developer needs to switch from using Google AI to Vertex AI for a project. They can use this Skill to easily configure the new Vertex AI credentials, select the appropriate models, and verify the setup without manual intervention.

Quick Start

Configure Vertex AI credentials for your project using the provided service account key file.

Frequently Asked Questions about memory-providers

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I configure LLM provider credentials for Google AI and Vertex AI?▼

You can configure LLM provider credentials by inputting API keys or service account key files. This enables secure access to Google AI and Vertex AI providers, including project-level overrides for your specific environment.

Can I test live connections to configured LLM providers?▼

Yes, you can test live connections to configured LLM providers. The system verifies connectivity to Google AI and Vertex AI setups, ensuring your API keys or service accounts are valid before deploying models.

How do I browse available generative and embedding models?▼

You can browse available generative and embedding models through the model catalog. This feature lists all models associated with your configured Google AI and Vertex AI providers for easy selection.

How do I track token consumption and estimated costs for LLM usage?▼

You can track token consumption and estimated costs via the usage monitoring feature. It provides detailed reporting on token usage and cost breakdowns for all models accessed through your configured providers.

Can I override LLM credentials at the project level?▼

Yes, you can override LLM credentials at the project level. This allows you to specify different API keys or service accounts for Google AI and Vertex AI across various individual projects.

What is the best way to switch from Google AI to Vertex AI?▼

The best way to switch providers is to configure new Vertex AI credentials using a service account key file. You can then select appropriate models and verify the setup by testing the live connection.