memory-providers

name	memory-providers
description	Configure LLM provider credentials (Google AI, Vertex AI) and browse available models. Use when setting up or changing the AI provider for a Memory org.
metadata	{"author":"emergent","version":"3.0"}

Manage LLM provider credentials, model catalog, and usage reporting using memory provider.

Rules

• Project context is auto-discovered — the CLI walks up the directory tree to find .env.local containing MEMORY_PROJECT or MEMORY_PROJECT_ID. If .env.local is present anywhere above the current directory, --project is not needed. Only pass --project <id> explicitly when overriding or when no .env.local exists.

Commands

Configure a provider (org-level)

# Google AI (API key)
memory provider configure google-ai --api-key "AIza..."

# Vertex AI (service account)
memory provider configure vertex-ai \
  --key-file /path/to/sa.json \
  --gcp-project "my-gcp-project" \
  --location "us-central1"

Stores encrypted credentials, syncs the model catalog from the live API, auto-selects the best generative and embedding models, and runs a live test — all in one atomic operation. Scoped to the organization.

Optional flags:

• --generative-model string Override auto-selected generative model
• --embedding-model string Override auto-selected embedding model
• --org-id string Organization ID (auto-detected from config)

Configure a provider (project-level override)

memory provider configure-project vertex-ai \
  --key-file /path/to/sa.json \
  --gcp-project "my-gcp-project" \
  --location "us-central1"

Stores a project-specific provider config that overrides the org-level config for this project. Uses the same flags as configure.

To remove the project override and fall back to org config:

memory provider configure-project <provider> --remove

List configured providers

memory provider list

Shows which providers are configured for the org and their model selections.

Test a provider

memory provider test <provider>
# provider is one of: google-ai, vertex-ai

Runs a live generate call to confirm credentials are valid and the model responds.

List available models

memory provider models
memory provider models google-ai --type generative

Shows the cached model catalog available from configured providers.

View usage and estimated cost

memory provider usage
memory provider usage --project <id>
memory provider usage --since 2024-01-01

Shows LLM token consumption and estimated cost breakdown by model and time period.

Workflow

1. Initial setup: provider configure <provider> — configures credentials, syncs models, and validates in one step
2. Verify: provider list to confirm the config is stored
3. Test live: provider test <provider> to confirm the model responds
4. Browse models: provider models to see what's available
5. Monitor costs: provider usage regularly to track token consumption

Choosing a provider

Provider	Command	Key format	Requires GCP project?
Google AI	configure google-ai --api-key	AIza...	No
Vertex AI	configure vertex-ai --key-file --gcp-project --location	service account JSON	Yes

Notes

• Credentials are scoped to the organization — configure requires no --project flag
• Credentials and model selection are stored and updated atomically in one call
• Model auto-selection picks the best available model from the live catalog; override with --generative-model / --embedding-model
• Credentials are stored server-side (encrypted); never written to the CLI config file
• Vertex AI requires a GCP project with the Vertex AI API enabled and a service account with the aiplatform.user role
• Project-level overrides (configure-project) inherit from the org when no project row exists; use --remove to revert to org config