The client.system namespace provides tools for monitoring the MemMachine server’s health and performance. These endpoints are primarily used for infrastructure orchestration (like Kubernetes liveness probes) and performance monitoring.
Health and Versioning
health
Performs a comprehensive health check of the MemMachine service and its connected dependencies (Vector Stores, Databases).
status = client.system.health()
print(f"Service: {status['service']} - Status: {status['status']}")
| Field | Type | Description |
|---|
status | str | Current health state (e.g., "healthy", "degraded"). |
service | str | Name of the service ("memmachine"). |
version | str | The semantic version of the running server. |
version
Returns detailed versioning information for the server and the API specification it supports.
v = client.system.version()
print(f"Running API Version: {v['api_version']}")
Observability
metrics
Exposes system-level performance metrics. By default, this returns metrics in the Prometheus text format, suitable for scraping by monitoring tools.
prometheus_data = client.system.metrics()
memmachine_request_duration_seconds: Latency of API calls.memmachine_memory_ingestion_total: Count of memories added.memmachine_vector_search_latency: Time taken for embedding and searching.
get_info
Retrieves non-sensitive environmental information about the server instance.
info = client.system.get_info()
| Field | Type | Description |
|---|
environment | str | Current mode (e.g., "production", "development"). |
uptime | float | Server uptime in seconds. |
provider | str | The cloud or infrastructure provider detected. |
Utility Methods
ping
A lightweight, low-latency check to verify that the network path to the server is open.
if client.system.ping():
print("Server is reachable")
If you are running MemMachine in a containerized environment (Docker/K8s), use the client.system.health() endpoint for your Liveness and Readiness probes.
