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在 OpenClaw 中安装
/install engramai
功能描述
Neuroscience-grounded memory for AI agents. Add, recall, and manage memories with ACT-R activation, Hebbian learning, and cognitive consolidation.
使用说明 (SKILL.md)
engramai 🧠
Cognitive memory system implementing ACT-R activation, Memory Chain consolidation, Ebbinghaus forgetting, and Hebbian learning.
Installation
pip install engramai
Quick Start
from engram import Memory
mem = Memory("./agent.db")
mem.add("User prefers concise answers", type="relational", importance=0.8)
results = mem.recall("user preferences", limit=5)
mem.consolidate() # Daily maintenance
CLI Usage
# Add a memory
neuromem add "User prefers dark mode" --type preference --importance 0.8
# Recall memories
neuromem recall "user preferences"
# View statistics
neuromem stats
# Run consolidation (like sleep)
neuromem consolidate
# Prune weak memories
neuromem forget --threshold 0.01
# List memories
neuromem list --limit 20
# Show Hebbian links
neuromem hebbian "dark mode"
AI Agent Integration (Important!)
For AI agents to use engram correctly, follow these patterns:
When to Call What
| Trigger | Action | Example |
|---|---|---|
| Learn user preference | store(type="relational") |
"User prefers concise answers" |
| Learn important fact | store(type="factual") |
"Project uses Python 3.12" |
| Learn how to do something | store(type="procedural") |
"Deploy requires running tests first" |
| Question about history | recall() first, then answer |
"What did I say about X?" |
| User satisfied | reward("positive feedback") |
Strengthens recent memories |
| User unsatisfied | reward("negative feedback") |
Suppresses recent memories |
| Daily maintenance | consolidate() + forget() |
Run via cron or heartbeat |
What to Store
✅ Store:
- User preferences and habits
- Important facts and decisions
- Lessons learned
- Procedural knowledge
❌ Don't store:
- Every conversation message (too noisy)
- Temporary information
- Publicly available facts
- Sensitive data (unless requested)
Importance Guide
| Level | Use For |
|---|---|
| 0.9-1.0 | Critical info (API keys location, absolute preferences) |
| 0.7-0.8 | Important (code style, project structure) |
| 0.5-0.6 | Normal (general facts, experiences) |
| 0.3-0.4 | Low priority (casual chat, temp notes) |
Hybrid Mode (Recommended)
Use engram alongside file-based memory:
- engram: Active memory — retrieval, associations, dynamic weighting
- Files (memory/*.md): Logs — transparency, debugging, manual editing
Heartbeat Maintenance
Add to your heartbeat or cron:
## Memory Maintenance (Daily)
- [ ] engram.consolidate
- [ ] engram.forget --threshold 0.01
Memory Types
factual— Facts and knowledgeepisodic— Events and experiencesrelational— Relationships and preferencesemotional— Emotional momentsprocedural— How-to knowledgeopinion— Beliefs and opinions
MCP Server
For Claude/Cursor/Clawdbot integration:
python -m engram.mcp_server --db ./agent.db
MCP Config (Clawdbot):
mcp:
servers:
engram:
command: python3
args: ["-m", "engram.mcp_server"]
env:
ENGRAM_DB_PATH: ~/.clawdbot/agents/main/memory.db
Tools: engram.store, engram.recall, engram.consolidate, engram.forget, engram.reward, engram.stats, engram.export
Key Features
| Feature | Description |
|---|---|
| ACT-R Activation | Retrieval ranked by recency × frequency × context |
| Memory Chain | Dual-system consolidation (working → core) |
| Ebbinghaus Forgetting | Natural decay with spaced repetition |
| Hebbian Learning | "Neurons that fire together wire together" |
| Confidence Scoring | Metacognitive monitoring |
| Reward Learning | User feedback shapes memory |
| Zero Dependencies | Pure Python stdlib + SQLite |
Links
安全使用建议
This skill is internally coherent as a memory tool, but there are several things to check before installing or enabling it: 1) Verify the PyPI package and GitHub repository (author identity, recent commits, stars, open issues) and prefer pinned versions and checksums. 2) Inspect the package source (or ask for a code audit) before running pip install, because it will run code on your host and can read/write the agent DB. 3) Don’t store secrets in the memory DB; change the guidance that lists 'API keys location' as storable—keep secrets in a dedicated secret manager and never mark them as normal memories. 4) If running the MCP server, run it in a restricted environment (container or limited user) and ensure the DB file is encrypted or access-limited. 5) Ask the skill author for privacy/security details: whether the DB is encrypted, how rewards/exports handle sensitive fields, and whether the MCP server binds only to localhost. If you need, provide the package name and I can look up the PyPI/GitHub repository and summarize maintainer details and recent code activity.
功能分析
Type: OpenClaw Skill
Name: engramai
Version: 1.0.0
The engramai skill provides a cognitive memory system for AI agents, implementing features like ACT-R activation and Hebbian learning. The SKILL.md and _meta.json files contain standard documentation, installation instructions for the 'engramai' PyPI package, and configuration for an MCP server. No evidence of data exfiltration, malicious execution, or harmful prompt injection was found; the instructions are focused on guiding the agent's memory management logic.
能力评估
Purpose & Capability
Name and description (neuroscience-grounded memory) match the SKILL.md: it documents an installable Python package, a client API, CLI, and an MCP server. Requiring python3 and a pip package is expected. Minor inconsistencies: SKILL.md claims 'Zero Dependencies' while also instructing 'pip install engramai'; links include both PyPI and an unrelated-sounding npm package which is unusual but possibly indicating multi-language ports.
Instruction Scope
Instructions tell agents to store and recall arbitrary memories, run a local MCP server, and recommend storing high-importance items (example list includes 'API keys location' under critical info). Although SKILL.md also warns against storing sensitive data, the Importance Guide explicitly lists API key locations as 'critical info' — this is contradictory and risks agents storing secrets. Running the MCP server with a writable DB path under ~/.clawdbot is expected for a memory tool, but it does give the package direct read/write access to the agent's memory DB.
Install Mechanism
The skill is instruction-only in the registry (no install spec), but SKILL.md instructs users to 'pip install engramai' (PyPI). Installing a package from PyPI is common and traceable, but it executes third‑party code on the host—there is no packaged install spec or checksum in the registry to verify provenance. No direct red flags like IP download URLs were present.
Credentials
The skill requests no environment variables in metadata, which is reasonable. However, the documentation suggests storing extremely sensitive items (API keys location) at high importance and configuring ENGRAM_DB_PATH to point to the agent DB. That combination could enable the package to access secrets stored in the same DB. The skill does not justify why secrets would be stored or how they would be protected/encrypted.
Persistence & Privilege
always is false and autonomous invocation is allowed (platform default). The MCP server pattern implies the package could be run as a long-lived process that reads/writes the agent DB; this is consistent with a memory service but increases blast radius compared to an ephemeral library. No evidence the skill attempts to modify other skills or system-wide 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 engramai - 安装完成后，直接呼叫该 Skill 的名称或使用
/engramai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
v1.0.0
initial publish
元数据
常见问题
Engramai 是什么？
Neuroscience-grounded memory for AI agents. Add, recall, and manage memories with ACT-R activation, Hebbian learning, and cognitive consolidation. 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件，目前累计下载 92 次。
如何安装 Engramai？
在 OpenClaw 或 Claude Code 对话框中运行命令「/install engramai」即可一键安装，无需额外配置。
Engramai 是免费的吗？
是的，Engramai 完全免费，采用 MIT-0 许可证，可自由下载、安装和使用。
Engramai 支持哪些平台？
Engramai 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 Engramai？
由 shing19（@shing19）开发并维护，当前版本 v1.0.0。
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