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Smara Memory

该 Skill 让智能体借助 Smara Memory API 保存用户偏好与事实、按语义搜索记忆、获取用户完整上下文并删除指定记忆;所有操作以 curl 调用外部接口完成,需要配置 SMARA_API_KEY 环境变量,并遵循存储、检索、去重与矛盾处理的使用流程。

parallelromb
parallelromb
热度 40.29
最近更新 2026-05-11 17:38:07
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 smara-memory
Metadata
name
smara-memory
description
Persistent memory for AI agents — store, search, and recall user context via the Smara Memory API
version
1.0.0
author
parallelromb
metadata
{"openclaw":{"emoji":"🧠","tags":["smara","memory","persistence","recall"],"requires":{"env":["SMARA_API_KEY"]},"primaryEnv":"SMARA_API_KEY"}}

Smara Memory Skill

Gives your agent persistent memory across conversations. Store facts about users, search by meaning, and retrieve full context — powered by Smara's Ebbinghaus decay scoring.

When to use

  • When the agent learns something about a user that should persist (preferences, facts, context)
  • When the agent needs to recall what it knows about a user
  • When the agent should check if it already knows something before asking again
  • After meaningful conversations to extract and store key facts

Setup

  1. Get a free API key at https://smara.io
  2. Set SMARA_API_KEY in your environment

Actions

Store a memory

curl -X POST https://api.smara.io/v1/memories \
  -H "Authorization: Bearer $SMARA_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "user_id": "user_id_here",
    "fact": "User prefers dark mode and uses vim keybindings",
    "importance": 0.7
  }'

Search memories

curl "https://api.smara.io/v1/memories/search?user_id=user_id_here&query=editor+preferences&limit=5" \
  -H "Authorization: Bearer $SMARA_API_KEY"

Get full user context

curl "https://api.smara.io/v1/users/user_id_here/context" \
  -H "Authorization: Bearer $SMARA_API_KEY"

Delete a memory

curl -X DELETE "https://api.smara.io/v1/memories/MEMORY_ID" \
  -H "Authorization: Bearer $SMARA_API_KEY"

Instructions for the agent

  1. After conversations: Extract key facts (preferences, decisions, context) and store them as memories with relevant tags
  2. Before responding: Search for relevant memories to personalize responses
  3. Contradiction handling: Smara automatically handles contradictions — if a user changes a preference, just store the new one and the old one is soft-deleted
  4. Duplicate handling: Smara skips duplicates automatically — safe to store the same fact multiple times
  5. Decay scoring: Memories naturally lose weight over time. Recent, frequently-accessed memories rank higher. This is automatic.

Example workflow

User: "I switched to Neovim last week"

Agent thinks:
1. Search memories for "editor preferences" → finds "Uses vim keybindings"
2. Store new memory: "Switched to Neovim (from vim)" with tags ["preferences", "editor"]
3. Smara auto-detects contradiction with old vim memory → soft-deletes it
4. Respond acknowledging the switch

API Reference

Full docs: https://api.smara.io/docs/