Persistent Agent Memory 1.0.1

该 Skill 借助 Coral Bricks 的语义检索 API 为 Agent 增加跨会话的持久记忆：用 coral_store 存储事实与偏好，用 coral_retrieve 按语义相似度召回，用 coral_delete_matching 按查询删除记忆。文本被嵌入为 768 维向量，结果按余弦相似度排序，全部记忆存于默认集合。仅用于记忆管理，不用于网页、文件或代码搜索。

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热度 87.02

最近更新 2026-05-11 17:32:05

分类：知识依赖：第三方 API / 代码执行应用场景：笔记整理 / 工作流

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add persistent-agent-memory-1-0-1

Metadata

name: persistent-agent-memory
description: Add persistent memory to any agent so it can remember prior work, maintain context across sessions, and continue long-running workflows.
metadata: {"openclaw":{"requires":{"env":["CORAL_API_KEY"],"bins":["curl","python3"]},"primaryEnv":"CORAL_API_KEY","homepage":"https://coralbricks.ai","privacyPolicy":"https://www.coralbricks.ai/privacy","emoji":"🧠"}}

Persistent Agent Memory

Memory storage and retrieval powered by Coral Bricks. Store facts, preferences, and context; retrieve them later by meaning. All memories are stored in the default collection.

Use when: (1) remembering facts or preferences for later, (2) recalling stored memories by topic or intent, (3) forgetting/removing memories matching a query.

NOT for: web search, file system search, or code search — use other tools for those.

Setup

Set your API key (get one at https://coralbricks.ai):

export CORAL_API_KEY="ak_..."

Optionally override the API URL (defaults to https://search-api.coralbricks.ai):

export CORAL_API_URL="https://search-api.coralbricks.ai"

Tools

coral_store — Store a memory

Store text with optional metadata for later retrieval by meaning.

scripts/coral_store "text to store" [metadata_json]

text (required): Content to remember
metadata_json (optional): JSON string of metadata, e.g. '{"source":"chat","topic":"fitness"}'

Output: JSON with status (e.g. {"status": "success"}).

Example:

scripts/coral_store "User prefers over-ear headphones with noise cancellation"
scripts/coral_store "Q3 revenue was $2.1M" '{"source":"report"}'

coral_retrieve — Retrieve memories by meaning

Retrieve stored memories by semantic similarity. Returns matching content ranked by relevance.

scripts/coral_retrieve "query" [k]

query (required): Natural language query describing what to recall
k (optional, default 10): Number of results to return

Output: JSON with results array, each containing text and score.

Example:

scripts/coral_retrieve "wireless headphones preference" 5
scripts/coral_retrieve "quarterly revenue" 10

coral_delete_matching — Forget memories by query

Remove memories that match a semantic query. Specify what to forget by meaning.

scripts/coral_delete_matching "query"

query (required): Natural language query describing memories to remove

Output: JSON confirming the operation completed.

Example:

scripts/coral_delete_matching "dark mode preference"
scripts/coral_delete_matching "forget my workout notes"

Privacy

Notes

All memories are stored in the default collection; collections are not exposed to the agent
All text is embedded into 768-dimensional vectors for semantic matching
Results are ranked by cosine similarity (higher score = more relevant)
Stored memories persist across sessions
The metadata field is free-form JSON; use it to tag memories for easier filtering
For more details and examples, see Persistent Agent Memory for AI Agents

Indexing delay (store then retrieve)

In rare cases, memories can take up to 1 second to become retrievable right after storage.