openmemory

name	openmemory
description	Manage persistent memory via OpenMemory MCP. TRIGGER when: user says "remember this", "save to memory", "store this", "recall", "what do you remember about", "check memory", "forget this", "delete memory", "clean up memory", or when agent forms a stable conclusion worth persisting. DO NOT TRIGGER when: user refers to system RAM, process memory, or asks to directly edit memory/MEMORY.md file.

OpenMemory — OpenMemory MCP Operation Playbook

1. Overview

This skill is the operational playbook for OpenMemory (MCP) — a primary persistent memory system; the file-based fallback is a secondary index only (one-line pointers, no full-text copies). Configure project_id based on your workspace (see §4 Step 2).

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2. HARD-GATE

Any memory operation (save/query/maintain) MUST first go through OpenMemory MCP before considering any file-based fallback. If the auto-memory system attempts a direct write to a local file first, intercept and redirect to this skill's flow.

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3. Flow Router

Trigger Signal	Route	When
User says "remember this", "save to memory", "store this" — or agent forms a stable conclusion worth persisting	SAVE	Post-task, or at user request
User says "recall", "what do you remember about", "check memory" — or agent needs historical context before answering	QUERY	Pre-task, or when context is insufficient
User says "forget this", "delete memory", "clean up memory" — or agent discovers outdated/wrong/duplicated memory	MAINTAIN	On discovery or user request

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4. SAVE Flow (5 Steps)

Step 1 — Classify sector

Content Type	Sector	Example
Events / incidents that happened	episodic	"2026-05-20 deployment 回滚"
Facts / knowledge / decisions / preferences	semantic	"服务 A 依赖 服务 B 的 API v2"
Procedures / methods / rules / constraints	procedural	"部署前必须运行测试套件"
User emotions / reactions / preferences	emotional	"用户不喜欢冗长的总结"
Cross-event patterns / insights	reflective	"部署失败通常与配置缓存有关"

Step 2 — Choose scope

Question	YES	NO
Is this specific to the current workspace/project?	openmemory_store_project (project_id: "{{PROJECT_ID}}")	Go to next question
Is this general engineering knowledge?	openmemory_store (global)	Default to project scope

Default bias: project scope (openmemory_store_project).

Note: Set {{PROJECT_ID}} to your actual project identifier (e.g. "my-project", "airflow-agent", "data-platform"). This is a per-workspace configuration.

Step 3 — Choose type

Situation	type parameter
Narrative knowledge, experiences, preferences	contextual
Structured triples that change over time	factual (with facts array: subject/predicate/object)
Both narrative and structured content	both

Step 4 — Compose tags

Attach 2–5 lowercase hyphen-separated tags. Examples:

• deployment, rollback, incident
• api, dependency, architecture
• user-preference, feedback
• pattern, decision, architecture
• workflow, ci-cd, config

Step 5 — Execute

1. Call the appropriate MCP tool (openmemory_store_project or openmemory_store) with sector, type, tags, and content.
2. Optionally add one line in a local index file pointing to the stored memory (never copy the full content).

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5. QUERY Flow (4 Steps)

Step 1 — Determine query strategy

Need	type parameter	sector parameter
Generic "what do you know about X"	contextual	Omit
"What happened" (events)	contextual	episodic
"How to do X" (procedures)	contextual	procedural
"Current state / status"	factual	Omit
"What you know + changes over time"	unified	Omit

Step 2 — Set parameters

Parameter	Value
project_id	"{{PROJECT_ID}}" (always, for project knowledge)
k (max results)	8 (default; adjust up for broad searches)
min_salience	Omit or set as needed (higher filters to more relevant results)

Step 3 — Execute query

Call openmemory_query with the determined parameters.

Step 4 — Reinforce if used

If the retrieved memory is directly used in the response, call openmemory_reinforce with a boost of 0.05–0.1 to strengthen its salience for future sessions.

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6. MAINTAIN Flow (3 Steps)

Step 1 — Scan

Call openmemory_list with:

• project_id: "{{PROJECT_ID}}" (omit for global scope)
• sector filter (optional)
• limit: 20 (adjust for larger scans)

Step 2 — Evaluate each entry

Condition	Action
Accurate and still relevant	openmemory_reinforce (boost 0.1–0.3)
Outdated / superseded	openmemory_delete then re-store corrected version
Duplicate (same info, different entry)	Delete the entry with lower salience
Noise / useless	openmemory_delete

Step 3 — Report

Summarize to the user: what was deleted, reinforced, or corrected, and why.

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7. Proactive Behaviors

Pre-task query

Before a non-trivial task, silently query OpenMemory for relevant context. Do not announce the query itself — only inform the user if the retrieved memory would affect the task's direction or decisions.

Post-task save

When the task produces a stable conclusion (not intermediate noise), save it. Briefly inform the user:

"已存入 openmemory: [one-line summary]"

Correction on discovery

If you notice an existing stored memory is factually wrong, immediately:

1. Delete the incorrect entry.
2. Save the corrected version.
3. Inform the user: "已更新 openmemory: [what changed]"

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8. Decision Flow

digraph memory_flow {
    rankdir=LR;
    node [shape=box, style=rounded, fontname="sans-serif"];
    edge [fontname="sans-serif", fontsize=10];

    trigger [label="🔄 Trigger\n(user signal /\nagent discovery)", shape=ellipse, style=filled, fillcolor="#E8F0FE"];
    route  [label="Route?", shape=diamond, style=filled, fillcolor="#FFF3CD"];

    save   [label="SAVE\n1. Classify sector\n2. Choose scope\n3. Choose type\n4. Compose tags\n5. Execute", fillcolor="#D4EDDA"];
    query  [label="QUERY\n1. Strategy\n2. Parameters\n3. Execute\n4. Reinforce", fillcolor="#CCE5FF"];
    maint  [label="MAINTAIN\n1. Scan\n2. Evaluate each\n3. Report changes", fillcolor="#F8D7DA"];

    done   [label="Done", shape=doublecircle, style=filled, fillcolor="#E2E3E5"];

    trigger -> route;
    route  -> save   [label="SAVE signal"];
    route  -> query  [label="QUERY signal"];
    route  -> maint  [label="MAINTAIN signal"];

    save   -> done;
    query  -> done;
    maint  -> done;
}

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9. Constraints (Anti-Pattern Table)

Anti-Pattern	Correct Practice
Write to a local file first without hitting OpenMemory	Always go through OpenMemory first
Use contextual type for everything	Use factual (with facts array) for structured triples
Default to global scope	Default to project scope
Query without project_id	Always include project_id: "{{PROJECT_ID}}" for project knowledge
Store conversational history / intermediate noise	Only store stable conclusions
Save without tags	Always attach 2–5 tags
Perform memory operations silently	Notify user on save; explicitly state when query returns empty