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Ourmem

作者 yhyyz · GitHub ↗ · v1.1.1 · MIT-0
cross-platform ⚠ suspicious
214
总下载
0
收藏
0
当前安装
14
版本数
在 OpenClaw 中安装
/install ourmem
功能描述
Shared memory that never forgets. Cloud hosted or self-deployed. Collective intelligence for AI agents with Space-based sharing across agents and teams. Use...
安全使用建议
What to consider before installing: - Metadata omission: The skill's registry entry claims no required credentials, but its docs and verify.sh require an API key (OMEM_API_KEY) and API URL. Treat the skill as one that needs a secret — the metadata should have declared this. Ask the publisher to correct the metadata. - Never share API keys: The docs' recommended sharing mechanism (passing another user's API key as target_user) is insecure: an API key is equivalent to full access to that tenant/space. Do not share API keys between users; prefer explicit, audited access controls or invitation flows. If asked to enter someone else's API key, decline. - Verify source & packages before running install steps: the docs reference npm packages and GHCR images (@ourmem/*, ghcr.io/ourmem). Confirm the publisher's identity, check the npm/ghcr package owners, inspect package content and Docker image layers, and prefer pinned releases (not 'latest') and checksums. - Be cautious when writing credentials to config files: the setup commands modify ~/.claude/settings.json, openclaw.json, opencode.json, and other client configs. Ensure those files are backed up and that you understand where you are storing secrets on disk (use least-privileged keys, not long-lived org-wide keys). - If self-hosting, isolate the service: run on a dedicated VM/container, review Docker images and binaries from the GitHub releases page, and avoid enabling cloud embedding/storage (Bedrock / S3 / OSS) unless you understand which cloud credentials are needed. - Audit code before trusting the hosted endpoint: there is no homepage or canonical source listed in the registry metadata. Request the public repo / homepage, inspect the repo and server code (or use self-hosted builds you built yourself) and confirm privacy/security policies before sending production or sensitive data to api.ourmem.ai. - Practical mitigations: use per-plugin/test API keys you can rotate, restrict the keys' scope if the service supports it, do not put secrets into shared or world-readable config files, and run the provided verify.sh only after ensuring the OMEM_API_KEY/URL point to a trusted server.
功能分析
Type: OpenClaw Skill Name: ourmem Version: 1.1.1 The 'ourmem' skill bundle provides persistent memory by exfiltrating conversation data and extracted facts to an external service (api.ourmem.ai). It instructs the AI agent to perform high-risk operations, including executing shell commands to modify sensitive configuration files (e.g., ~/.claude/settings.json, openclaw.json) and installing external plugins via npx/npm. While these actions align with the stated purpose of cross-session memory and the bundle includes self-hosting options, the requirement for broad file system access, automated environment modification, and the transmission of user data to a third-party API represents a significant security risk and attack surface.
能力评估
Purpose & Capability
The SKILL.md and reference docs clearly require an API key (OMEM_API_KEY / api_key) and describe integration with many client plugins/platforms, yet the registry metadata lists no required environment variables or primary credential. That mismatch is incoherent: a persistent/shared memory mesh legitimately needs an API key, so the metadata omission is misleading and reduces transparency for users evaluating permissions.
Instruction Scope
Runtime instructions instruct the agent (and the user) to write credentials into many different config files (~/.claude/settings.json, opencode.json, openclaw.json, MCP configs), run curl to create tenants, install plugins from npm/marketplace, and—critically—recommend sharing by passing another user's API key as target_user. The SKILL.md also references server installation steps that accept AWS credentials for Bedrock embedding. These instructions go beyond simple 'how to talk to ourmem' and include actions that persist secrets in multiple places and encourage sharing of raw API keys (insecure).
Install Mechanism
The skill is instruction-only (no install spec), minimizing automatic disk writes. The docs reference downloads from reasonable hosts (ghcr.io and github.com) and standard npm packages (@ourmem/*). No obscure URLs or shorteners are used. However, the skill prescribes executing platform-specific install commands that will change user config files and fetch packages — so while the install sources look normal, the absence of a declared install spec in registry metadata reduces transparency about what will actually be run when following instructions.
Credentials
Although metadata declares no required env vars or primary credential, the docs and verify.sh clearly require OMEM_API_KEY and OMEM_API_URL; self-hosting/docs additionally require optional cloud credentials (AWS keys or OSS/S3 credentials) for embedding or object storage. Requesting broad cloud credentials is explainable for optional embedding/storage features, but the absence of any declared required secrets in metadata plus the advice to share API keys across users (pass target_user = other tenant id / API key) is disproportionate and insecure.
Persistence & Privilege
always:false (normal). The skill instructs edits to multiple agent/client configuration files to enable a persistent plugin — this is expected for a memory plugin, but the instructions explicitly place API keys into those files and into environment variables. That creates persistent secrets on disk across multiple tools and increases blast radius if the hosted endpoint or packages are untrusted. This is a legitimate functionality but a sensitive operation that should be clearly declared and audited.
如何使用
  1. 确保已安装 OpenClaw(本地或 Docker 部署)
  2. 在对话框中输入安装命令:/install ourmem
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /ourmem 触发
  4. 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
ourmem 1.1.1 - Updated platform setup instructions in SKILL.md to reflect revised config file locations and plugin/package names - Clarified smart ingest pipeline: highlighted async LLM extraction and expected delay for fact extraction/search - Expanded onboarding and setup documentation for both hosted and self-hosted modes - Improved accuracy of platform-specific configuration guidance - Minor terminology clarifications in user-facing docs
v1.1.0
Restructure skill architecture
v1.0.0
ourmem 1.0.0 - Added new trigger phrases to support memory sharing with other users (e.g. "share with user", "share my memories with another user"). - Updated trigger and usage guidance in SKILL.md for more precise activation around sharing scenarios. - Expanded onboarding and usage documentation. - Reference docs updated for API quick access and web integration.
v0.7.3
ourmem v0.7.3 - Added alternate trigger and reference support for "omem" alongside "ourmem" - Expanded onboarding and usage instructions to clarify "omem" as a recognized alias - Updated trigger lists and keyword metadata for Omem/Ourmem equivalency - Clarified platform-specific lifecycle hooks for memory ingestion and storage - Improved instructions for when and how to use or skip this skill
v0.7.2
ourmem v0.7.2 changelog - Major documentation rewrite: SKILL.md has been refactored for clarity, conciseness, and improved intent detection guidelines. - Trigger phrase and intent instructions are now more explicit and easier to follow. - Onboarding, verification, and API key setup steps have been clarified and reorganized. - Role distinctions between user-facing and internal terms (like API key, tenant, secret) are better defined. - All references/setup guides updated for consistency with the new onboarding process. - Key requirement: Agents must send the final handoff message after setup, not just complete the technical steps.
v0.7.1
ourmem 0.7.1 - Onboarding instructions for hosted setup have been expanded and improved in SKILL-web.md and references/hosted-setup.md. - Updated guided setup flow to better explain each step and options for hosted and self-hosted modes. - Clarified platform-specific installation and initial verification steps. - No changes to user-facing commands or API behavior.
v0.7.0
ourmem 0.7.0 - Documentation updated across SKILL.md, SKILL-web.md, and references/api-quick-ref.md - No functional or API changes; updates focus on improved onboarding and clarity - Onboarding steps, usage guidelines, and memory management instructions were revised and expanded - File consistency and help references improved for better user setup experience
v0.6.0
ourmem 0.6.0 - Documentation updates in onboarding and API quick reference files. - Improved clarity and instructions for setting up both hosted and self-hosted modes. - No breaking changes; usage and API unchanged.
v0.5.0
- Changelog documentation standardized and moved to SKILL-web.md. - No changes to functionality, onboarding, API, or user-facing workflow. - SKILL.md content remains unchanged in this release.
v0.4.0
## ourmem v0.4.0 Changelog - Documentation migrated to `SKILL-web.md` (previously in `SKILL.md`) - No functional or logic changes to the skill; content remains unchanged - Prepares for improved web-based or platform-specific documentation delivery
v0.3.1
ourmem 0.3.1 - Documentation changes (SKILL-web.md): No source or logic modifications. - No code, configuration, or behavioral changes in this release.
v0.3.0
ourmem v0.3.0 - Added new quick API reference for easier integration (`references/api-quick-ref.md`) - Improved and expanded onboarding and deployment instructions in documentation - Updated web and main skill docs for clarity and user guidance
v0.2.0
**Summary: Major expansion — now with shared, analytics-enabled memory spaces and collective intelligence features.** - Adds support for memory analytics, memory decay, and collective intelligence concepts. - New onboarding: three-tier space architecture, provenance tracking, and quality-gated knowledge sharing. - Expanded trigger list and platform setup instructions — supports MCP, plugin marketplace, and new per-platform docs. - Includes new tool definitions, automatic hooks, and an overview of the Smart Ingest pipeline. - Improved, structured handoff with API key, recovery, and next steps. - Documentation split: added SKILL-web.md and clarified references for hosted and self-hosted setup.
v0.1.0
ourmem 0.1.0 initial release - Introduces a persistent memory system for AI agents with both cloud-hosted and self-hosted deployment options. - Detects explicit and implicit user requests to save, recall, import, or share long-term memory across sessions, devices, or agents. - Provides guided onboarding with step-by-step setup, API key management, and platform-specific plugin installation instructions. - Supports team "Spaces" for shared memory, memory import/export, and verification steps to confirm proper installation. - Includes clear guidelines on what information to store versus skip and safety warnings about secrets and key management.
元数据
Slug ourmem
版本 1.1.1
许可证 MIT-0
累计安装 0
当前安装数 0
历史版本数 14
常见问题

Ourmem 是什么?

Shared memory that never forgets. Cloud hosted or self-deployed. Collective intelligence for AI agents with Space-based sharing across agents and teams. Use... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 214 次。

如何安装 Ourmem?

在 OpenClaw 或 Claude Code 对话框中运行命令「/install ourmem」即可一键安装,无需额外配置。

Ourmem 是免费的吗?

是的,Ourmem 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。

Ourmem 支持哪些平台?

Ourmem 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 Ourmem?

由 yhyyz(@yhyyz)开发并维护,当前版本 v1.1.1。

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