EverMemory

EverMemory is the deterministic memory plugin for OpenClaw. It gives the agent persistent memory, user understanding, and governed self-improvement without hiding the storage or decision process.

What to do first

When the user is new to EverMemory, start with onboarding.
When the user asks to remember something important, store it with an explicit kind.
When the user asks for prior context, recall before answering.
When the user asks for debugging, auditing, cleanup, backup, or recovery, use the governance and IO tools instead of guessing.

Core model

Layer 1: Memory

Store durable facts, decisions, preferences, constraints, lessons, and project context.
Recall by keyword, structured filters, or hybrid retrieval.
Archive stale or superseded memories and restore them with review/apply gates.

Layer 2: Understanding

Build a user profile from explicit statements and stable interaction patterns.
Track behavior rules and preference hints that can shape future responses.
Generate session briefings so a new session starts with continuity.

Layer 3: Proactivity

Extract intent and reflections from interaction history.
Consolidate duplicate or stale memories.
Explain why a write, recall, archive, or rule action happened.

Tool map

EverMemory has 16 core capabilities. In the current OpenClaw plugin, 15 are exposed as tool commands, and onboarding is registered as profile_onboard. Smartness exists in the SDK/status layer but is not currently registered as a standalone OpenClaw tool.

Capability	OpenClaw tool name	When to use
Store memory	`evermemory_store`	User asks to remember a fact, decision, preference, or lesson
Recall memory	`evermemory_recall`	User asks what happened before, what they prefer, or what was decided
Consolidate memory	`evermemory_consolidate`	Cleanup, dedupe, archive stale memory
Status	`evermemory_status`	Inspect counts, DB path, activity, continuity KPIs
Smartness report	Not host-registered	Mention as internal/SDK capability, do not invent a tool call
Session briefing	`evermemory_briefing`	Generate startup continuity context
Rules	`evermemory_rules`	Read or manage promoted behavior rules
Profile	`evermemory_profile`	Read or recompute user profile
Explainability	`evermemory_explain`	Audit why EverMemory wrote, recalled, restored, or promoted something
Export	`evermemory_export`	Backup memory to snapshot or text export
Import	`evermemory_import`	Review or apply imported snapshot/text
Archive review	`evermemory_review`	Inspect archived or superseded items before restore
Restore	`evermemory_restore`	Recover archived memory with review/apply
Intent analysis	`evermemory_intent`	Analyze the likely user intent for a message
Reflection	`evermemory_reflect`	Generate lessons, warnings, or candidate rules
Onboarding	`profile_onboard`	First-run questionnaire and initial profile setup

Tool usage guidance

`evermemory_store`

Use for explicit long-term facts. Prefer concise, high-value content and a correct kind.

Example:

{
  "content": "Technical decision: replace Webpack with Vite.",
  "kind": "decision"
}

Store when the user says:

"记住这个决定"
"以后按这个偏好来"
"这个坑以后别再踩"

`evermemory_recall`

Use before answering when the user asks about prior context, preferences, constraints, or project continuity.

Example:

{
  "query": "Vite migration decision",
  "limit": 5
}

`evermemory_status`

Use for health checks and operator-style visibility. It returns memory counts, archive counts, profile/rule/reflection state, recent debug activity, and continuity KPIs.

`evermemory_briefing`

Use at session start or when the user asks for a summary of who they are, current constraints, and active project context.

`profile_onboard`

Use for first-run setup. Ask the questions, collect answers, then submit them. Do not skip onboarding if no profile exists and the user wants personalized memory behavior.

`evermemory_profile`

Use to inspect current user understanding. Prefer recompute: true when the user asks for a refreshed profile after many new interactions.

`evermemory_rules`

Use for behavior rules and guardrails. Prefer read/review paths before mutating rules.

`evermemory_explain`

Use when the user asks "why did you remember this", "why was this recalled", "why was this archived", or "why did this rule trigger".

`evermemory_export` and `evermemory_import`

Export for backup or migration.
Import with mode: "review" first.
Only use apply after the user clearly confirms.

`evermemory_review` and `evermemory_restore`

Review archived memory before restoring.
Prefer mode: "review" first.
Restore only the specific IDs the user approves.

`evermemory_intent`, `evermemory_reflect`, `evermemory_consolidate`

Use these as maintenance and self-improvement tools:

evermemory_intent for intent labeling and routing insight.
evermemory_reflect for lessons, warnings, and candidate rules.
evermemory_consolidate for dedupe and stale-memory cleanup.

Recommended workflows

First use

用户: 开始使用 EverMemory
动作: 调用 profile_onboard
结果: 完成初始化问卷，建立基础画像

Remember a decision

用户: 记住我们决定用 Vite 替代 Webpack
动作: 调用 evermemory_store
建议 kind: decision

Recall previous context

用户: 回忆一下我们上次怎么定的
动作: 先调用 evermemory_recall，再基于召回结果回答

Export backup

用户: 导出所有记忆为 JSON
动作: 调用 evermemory_export，并使用 format=json（OpenClaw 注册层）

Recovery

用户: 把之前归档掉的 TypeScript 偏好恢复回来
动作: 先调用 evermemory_review 找候选，再调用 evermemory_restore

Guardrails

Do not claim a standalone evermemory_smartness tool exists unless the host actually registers it.
In the current repository, onboarding is profile_onboard, not evermemory_onboard.
Prefer review before apply for import and restore.
Recall before answering if the user explicitly asks about previous decisions, preferences, or history.
Store only durable, high-signal information. Avoid writing transient chatter as memory.
When a tool returns governed results, explain them plainly instead of exposing raw internals unless the user asks.

Configuration notes

Common environment variables for semantic retrieval:

EVERMEMORY_EMBEDDING_PROVIDER: local, openai, or none
EVERMEMORY_LOCAL_MODEL: local embedding model, default Xenova/all-MiniLM-L6-v2
OPENAI_API_KEY: required when the embedding provider uses OpenAI

Common plugin config fields:

databasePath
bootTokenBudget
maxRecall
debugEnabled
semantic.enabled
semantic.maxCandidates
semantic.minScore
intent.useLLM
intent.fallbackHeuristics

evermemory

Installation