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Bounded Memory
作者
canmaxfire
· GitHub ↗
· v1.1.1
· MIT-0
114
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0
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当前安装
7
版本数
在 OpenClaw 中安装
/install bounded-memory
功能描述
Provides fast full-text search of OpenClaw session histories with optional LLM summarization for recalling past conversations and decisions.
使用说明 (SKILL.md)
Bounded Memory
Gives your OpenClaw AI agent a perfect memory — it can recall anything you've ever discussed, even from months ago.
What It Does
Without this skill: each OpenClaw session starts fresh. The AI forgets everything.
With this skill: ask things like:
- "Did we discuss X before?"
- "What did we decide about Y?"
- "Find that conversation from last month"
Privacy Design
| What runs | How |
|---|---|
| Search indexing | ✅ 100% offline — SQLite only |
| Search execution | ✅ 100% offline — no network calls |
| LLM summarization | ⚠️ Opt-in only — use --llm flag to enable |
No external API calls by default. The --llm flag (disabled by default) sends snippets to your configured LLM for summarization — only when you explicitly ask for it.
Quick Start
# Index your conversations (first time)
python3 skills/session-search/scripts/index-sessions.py --agent main
# Search (fully offline)
python3 skills/session-search/scripts/search-sessions.py "what did we decide about the logo design"
# Search with AI summary (opt-in)
python3 skills/session-search/scripts/search-sessions.py "question" --llm
What It Solves
| Problem | Without | With Bounded Memory |
|---|---|---|
| "I asked this before but can't remember" | AI has no idea | Instant recall |
| "What did we decide in that meeting?" | Forgot | Searches all sessions |
| "Did I mention this before?" | No way to know | Searches everything |
Example
You: "Search our conversations about the robot project"
→ Found 3 discussions:
1. [Last week] We discussed the design direction...
2. [2 weeks ago] You asked about pricing...
3. [Last month] The AI suggested adding...
安全使用建议
This skill appears to do what it claims: index your OpenClaw session .jsonl files into a local SQLite FTS5 DB and search them, with optional LLM summaries only when you pass --llm. Before installing/running: (1) verify the skill source (registry metadata shows unknown/origin inconsistencies in README GitHub links); (2) inspect ~/.openclaw/openclaw.json to see which API keys/providers are stored (the script will use the first apiKey it finds when you enable --llm); (3) only use --llm if you consent to sending matched conversation snippets to the external endpoint (the code calls https://api.minimax.chat/v1); (4) consider where the DB will be stored (SESSION_SEARCH_DB_DIR or the skill db folder) and remove it if you want to revoke the indexed data. If provenance is important, confirm the repository and maintainer identity before installing.
能力标签
能力评估
Purpose & Capability
The skill claims to index and search OpenClaw session histories and the shipped scripts read ~/.openclaw/agents/*/sessions/*.jsonl, build an SQLite FTS5 DB, and search it. Access to agent session files and an on-disk DB is required and present. The optional LLM summary path that reads an API key from ~/.openclaw/openclaw.json or env vars is consistent with the described opt-in summarization feature.
Instruction Scope
SKILL.md instructs running the included index and search scripts. The scripts only read session files, write a local SQLite DB, and (only when --llm is used) send selected snippets to an external LLM endpoint. There is no instruction to read unrelated system files or exfiltrate data by default. Note: indexing can enumerate all agents when run with --all-agents.
Install Mechanism
No install spec is present (instruction-only); the scripts run as-is with Python and SQLite, which is proportional and low-risk. Nothing is downloaded or extracted by the skill installer.
Credentials
No required env vars are declared. The code optionally reads OPENCLAW_AGENTS_DIR and SESSION_SEARCH_DB_DIR to override paths (reasonable). For summarization it looks for an API key in ~/.openclaw/openclaw.json or env vars (MINIMAX_API_KEY, OPENAI_API_KEY). This is proportional to the optional LLM feature, but be aware it will pick the first provider apiKey present in your OpenClaw config — review that file so you know which key would be used when you enable --llm.
Persistence & Privilege
The skill does not request always:true and runs only when invoked. It writes its own DB file under the skill workspace (or overridden path) and does not modify other skills or global agent settings.
如何使用
- 确保已安装 OpenClaw（本地或 Docker 部署）
- 在对话框中输入安装命令：
/install bounded-memory - 安装完成后，直接呼叫该 Skill 的名称或使用
/bounded-memory触发 - 根据 Skill 的参数说明提供必要输入，即可获得结构化输出
版本历史
v1.1.1
Fix: LLM truly opt-in, remove sessions.db from git, add .gitignore
v1.1.0
Rewrite: plain language descriptions, user benefit focus instead of technical jargon
v1.0.4
Chore: convert all documentation to English
v1.0.3
Security: add transparency section, clarify LLM opt-in/out, declare API key access, fix doc inconsistencies
v1.0.2
Correct display name to Bounded Memory
v1.0.1
Fix: replace example queries with generic placeholders to prevent session data leakage
v1.0.0
Initial release: SQLite FTS5 session search, Hermes bounded-memory pattern for OpenClaw
元数据
常见问题
Bounded Memory 是什么？
Provides fast full-text search of OpenClaw session histories with optional LLM summarization for recalling past conversations and decisions. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 114 次。
如何安装 Bounded Memory？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install bounded-memory」即可一键安装，无需额外配置。
Bounded Memory 是免费的吗？
是的，Bounded Memory 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Bounded Memory 支持哪些平台？
Bounded Memory 跨平台运行，可在任意部署了 OpenClaw / Claude Code 的环境中使用（cross-platform）。
谁开发了 Bounded Memory？
由 canmaxfire（@canmaxfire）开发并维护，当前版本 v1.1.1。
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