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Engram Evomap - Long Term AI Memory

作者 404-UNKNOW · GitHub ↗ · v0.1.0
cross-platform ⚠ suspicious
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在 OpenClaw 中安装
/install engram-evomap
功能描述
The AEIF-based long-term memory hub for AI Agents to prevent repeating bugs.
使用说明 (SKILL.md)

🧬 engram — Agent Long-Term Experience Memory Hub

🤖 System Mind

You are equipped with an external long-term memory store based on the AEIF v1.0 (Agent Experience Interchange Format) protocol.

  • Consult first: Before tackling a recurring error or a complex problem, search your memory for existing solutions.
  • Commit success: After successfully resolving a significant challenge, "geneticize" the experience and store it immediately.

🎯 Automatic Trigger Rules

  • Error Interception (Auto-Consult): When detecting signals such as error, failed, SSL, 404, EACCES, or timeout, the system automatically retrieves and injects [EvoMap Advice].
  • Task Distillation (Auto-Commit): After completing a multi-step complex task successfully, trigger !exp commit to preserve the logic for future sessions.

🛠️ Command Set

Command Action
!exp consult '\x3Cproblem>' Performs a semantic search for historical solutions. Returns Top-3 matches.
!exp commit Asynchronously distills current session history into a universal AEIF capsule.
!exp list Displays memory statistics and a list of recently stored capsules.
!exp score \x3Cid> --bad Provides negative feedback to a capsule, decreasing its TrustScore.

📦 Output Specification

  • Advice should be injected as a system observation wrapped in --- separators.
  • Focus on providing actionable [PATCH], [CONFIG], or [WORKAROUND] steps.
  • Do not modify user-validated paths unless explicitly requested.
安全使用建议
Key things to consider before installing: - This skill will send recent session history to an LLM (via llmClient) for distillation and verification. If your agent uses an external LLM provider, private or sensitive conversation content may be transmitted off‑host. - Distilled results and seed capsules are persisted to a local SQLite DB (default: data/engram.db) and model files are downloaded to ~/.engram_cache. Review and control these paths if you need to limit data exposure or storage location. - The included seed capsules contain potentially unsafe/workaround commands (e.g., disabling git SSL verification). The verification system may still surface such insecure advice if trust thresholds are met. Inspect seeds.json and consider removing or editing any risky capsules. - If you want to reduce risk: disable automatic commits/interception (use the skill only manually), run the skill in an isolated environment, change DB/cache paths to a controlled location, and review/limit the LLM client the skill is allowed to call. - If you need higher assurance, ask the author for explicit documentation about what is sent to the LLM, an option to disable remote LLM calls (pure local-only mode), and a configuration to opt out of auto-commit/auto-intercept.
功能分析
Type: OpenClaw Skill Name: engram-evomap Version: 0.1.0 The engram-evomap bundle is a local long-term memory system for AI agents that uses vector embeddings (@xenova/transformers) and SQLite (better-sqlite3) to store and retrieve solutions to recurring technical issues. The code follows a structured 'Agent Experience Interchange Format' (AEIF) and includes a privacy-preserving distillation process in 'src/core/gene-processor.js' that explicitly instructs the LLM to redact sensitive data like IPs, paths, and usernames. While 'data/seeds/seeds.json' contains a potentially risky suggestion to disable Git SSL verification, it is documented as a workaround with an accompanying security warning, and no evidence of intentional malice or unauthorized data exfiltration was found.
能力评估
Purpose & Capability
Name/description (AEIF long-term memory for agents) match the code and instructions: it vectorizes queries, stores AEIF capsules in SQLite, and provides consult/commit/list commands. Requiring node is appropriate; no unrelated credentials or binaries are requested.
Instruction Scope
SKILL.md tells the agent to auto-consult on error signals and to auto-commit distilled experiences. The actual implementation will send recent session history to an LLM (GeneProcessor.distill / VerificationEngine.selfReflect) and then store resulting capsules (rawPayload) in a local DB. That means potentially sensitive conversation content will be transmitted to whatever LLM client the agent provides and may be persisted locally. The skill does not explicitly declare or highlight this data flow in SKILL.md.
Install Mechanism
There is no install spec in the metadata (instruction-only), but the packaged code depends on @xenova/transformers and at runtime will download a transformer model (all‑MiniLM‑L6‑v2) into ~/.engram_cache. Runtime model downloads and writing a cache directory are notable (network activity and disk writes), but are expected for an offline semantic engine.
Credentials
The skill requests no explicit environment variables or credentials, which is consistent. However, it uses process.env and the agent's provided llmClient to call external LLM(s) for distillation and verification. That implies session content and derived capsules will be released to the agent's LLM provider and stored locally. The skill also writes a DB (data/engram.db) and cache (~/.engram_cache) by default — these file writes are reasonable for a memory store but may hold sensitive data. The seed capsules include advice that lowers security hygiene (e.g., 'git config --global http.sslVerify false').
Persistence & Privilege
always is false; the skill isn't force-enabled. It does persist data (SQLite DB) and cache model files under the user's directories and spawns worker threads. Autonomous invocation is allowed (platform default), which combined with the auto-commit/auto-intercept rules increases the chance that content will be sent to an LLM and stored without an extra explicit user action.
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 engram-evomap
  3. 安装完成后,直接呼叫该 Skill 的名称或使用 /engram-evomap 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
v0.1.0
- Removed the file: docs/index.html - No changes to the skill’s interface, description, or functionality.
v1.0.0
Initial release of engram-evomap: a long-term memory hub for AI agents. - Introduces AEIF-based memory to prevent repeating bugs. - Supports commands to consult, commit, and score prior experiences. - Implements auto-consult on error detection and auto-commit after complex task success. - Provides actionable advice formatted for easy integration into agent workflows.
元数据
Slug engram-evomap
版本 0.1.0
许可证
累计安装 1
当前安装数 1
历史版本数 2
常见问题

Engram Evomap - Long Term AI Memory 是什么?

The AEIF-based long-term memory hub for AI Agents to prevent repeating bugs. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 432 次。

如何安装 Engram Evomap - Long Term AI Memory?

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

Engram Evomap - Long Term AI Memory 是免费的吗?

是的,Engram Evomap - Long Term AI Memory 完全免费(开源免费),可自由下载、安装和使用。

Engram Evomap - Long Term AI Memory 支持哪些平台?

Engram Evomap - Long Term AI 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 Engram Evomap - Long Term AI Memory?

由 404-UNKNOW(@404-unknow)开发并维护,当前版本 v0.1.0。

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