---

name: core-memory-api description: "Use M-flow public Python APIs and mflow CLI for core memory workflows, data management, manual ingestion, and troubleshooting." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Core Memory API

Use this sub-skill when a task needs the public m_flow Python package or the mflow CLI for core memory operations: add data, memorize it into graph memory, query/search it, run one-step ingest, manage datasets/data, update/delete/prune, manual graph insertion, or process-local configuration.

Route first

• Core add -> memorize -> query/search workflows: read references/workflows.md.
• Exact Python signatures, return shapes, exported names, and data-management APIs: read references/api-reference.md.
• CLI commands and flags: read references/cli-reference.md.
• Errors, credentials, invalid kwargs, empty results, or destructive-operation cautions: read references/troubleshooting.md.
• Loader internals, content routing, chunking, custom Stage pipelines, and preferred_loaders details belong in ../ingestion-pipelines/SKILL.md.
• Retrieval scoring, RecallMode tuning, episodic display controls, Cypher, and storage/backend tuning belong in ../retrieval-graph-search/SKILL.md.
• FastAPI, MCP, UI, Docker, auth service deployment, and server lifecycle belong in ../service-integrations/SKILL.md.

Operating model

M-flow's in-process API is asynchronous. Treat add() as raw data registration, memorize() as memory-graph construction, and query()/search() as retrieval over already memorized data. ingest() is the convenience API that performs add() then memorize() and returns an IngestResult status object.

Prefer stable dataset names such as project_notes_2026q1; pass the same name to add/ingest, memorize, and query. Use created_at when importing historical content so later retrieval can preserve temporal intent. Use run_in_background only when the caller can poll or otherwise wait before querying. Never call delete, update, datasets.delete_dataset, or prune without an explicit scope and confirmation from the user.

Safe bundled checker

Run the bundled smoke checker in dry-run mode before live examples:

python scripts/core_workflow_smoke.py --help
python scripts/core_workflow_smoke.py

It only imports m_flow, checks public exports/config visibility, reports whether an LLM credential appears configured, and prints a guarded plan. Live DB writes require an explicit flag:

python scripts/core_workflow_smoke.py --run-live --dataset-name skill_smoke_core

Live mode calls add(), memorize(), and query() and may create local graph, vector, relational, and file-storage data for the selected dataset.