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Documentation Index

Fetch the complete documentation index at: https://docs.memmachine.ai/llms.txt

Use this file to discover all available pages before exploring further.

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.

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Health and Versioning

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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']}")
Returns:
FieldTypeDescription
statusstrCurrent health state (e.g., "healthy", "degraded").
servicestrName of the service ("memmachine").
versionstrThe semantic version of the running server.

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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']}")

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Observability

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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()
Common Metrics Tracked:
  • 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.

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Environment Information

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get_info

Retrieves non-sensitive environmental information about the server instance. 
info = client.system.get_info()
FieldTypeDescription
environmentstrCurrent mode (e.g., "production", "development").
uptimefloatServer uptime in seconds.
providerstrThe cloud or infrastructure provider detected.

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Utility Methods

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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.