---

name: m-flow description: "Operate M-flow memory, graph-routed retrieval, ingestion pipelines, service integrations, MCP, and storage backends for the mflow-ai package." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

M-flow

Use this repo skill when a task involves M-flow / mflow-ai: persistent memory for agents, graph-routed RAG, episodic/procedural memory, the m_flow Python package, the mflow CLI, the FastAPI service, web UI, MCP server, or storage backend configuration.

Fast orientation

M-flow stores knowledge as layered memory, then retrieves by graph-routed Bundle Search. The common in-process workflow is:

1. await m_flow.add(...) or await m_flow.ingest(...) to register data.
2. await m_flow.memorize(...) to build Episode / Facet / FacetPoint / Entity graph memory.
3. await m_flow.query(...) or await m_flow.search(...) to retrieve context or an LLM-written answer.

Install the public package with pip install mflow-ai, or install a checkout with pip install -e .. Python 3.10-3.13 is supported by project metadata; use Python 3.11 when choosing a conservative inspection/development runtime.

Minimal checks:

python -c "import m_flow; print(m_flow.__version__)"
mflow --help
python scripts/check_mflow_env.py

Route by task

If the user asks about...	Read
Python APIs, CLI commands, add/memorize/query/ingest, datasets, manual graph insertion, delete/update/prune, config facade	sub-skills/core-memory-api/SKILL.md
Input formats, loader selection, preferred_loaders, chunking, content_type, content routing, procedural learning, custom pipelines	sub-skills/ingestion-pipelines/SKILL.md
Recall modes, SearchConfig, episodic Bundle Search, graph/vector/relational/cache backend tuning, empty/noisy results, Cypher	sub-skills/retrieval-graph-search/SKILL.md
FastAPI, web UI, Docker Compose, auth, settings, MCP, cloud sync, workers, face-aware playground, service status	sub-skills/service-integrations/SKILL.md
Cross-cutting environment variables, optional extras, credential/service prerequisites	references/configuration.md
Install/import, credentials, DB locks, optional dependency, logging, or safe-operation recovery	references/troubleshooting.md
Staleness and source snapshot checks	references/repo-provenance.md

Operating guardrails

• M-flow API calls are mostly asynchronous; wrap examples in asyncio.run() or use an existing event loop.
• TRIPLET_COMPLETION and graph construction need LLM/embedding credentials; EPISODIC, PROCEDURAL, CHUNKS_LEXICAL, and CYPHER retrieve from data that must already be memorized.
• Defaults are local file-backed storage: SQLite relational metadata, LanceDB vectors, and Kuzu graph storage. External services need matching extras, environment variables, and service health checks.
• Do not run destructive operations (delete, prune, hard deletion, DB migrations, service stop/kill) without explicit user scope and confirmation.
• Treat optional browser scraping, face recognition, cloud sync, MCP remote API mode, Modal workers, and external DB providers as optional surfaces; verify credentials/services before claiming they work.
• Keep generated helper scripts safe by default. Use live workflow flags only when the user accepts local storage writes and credential use.