What makes Cognee Memory System notable?

1K downloads. Trust evidence available

How should Cognee Memory System be evaluated before use?

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What kind of evidence is visible on this page?

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Cognee Memory System

AI知识引擎 - 6行代码实现记忆系统。remember/recall/forget/improve循环，向量+图搜索，支持OpenClaw插件。 Skill: Cognee Memory System Owner: smseow001 Summary: AI知识引擎 - 6行代码实现记忆系统。remember/recall/forget/improve循环，向量+图搜索，支持OpenClaw插件。 Tags: ai:1.0.0, cognee:1.0.0, graph:1.0.0, knowledge:1.0.0, latest:1.0.0, memory:1.0.0, rag:1.0.0, search:1.0.0, vector:1.0.0 Version history: v1.0.0 | 2026-04-17T10:17:01.660Z | auto Cognee Memory 1.0.0 - Initial release of the Cognee AI knowledge engine. - Provides a 4-operation memory API

OpenClaw

View on ClawHub Homepage

Rank

Safety

Downloads

1.0k

Updated

Oct 11, 2026

Version

1.0.0

Source

CLAWHUB

About

What it does, and when to use it.

Capability contract not published. No trust telemetry is available yet. 1K downloads reported by the source. Last updated 10/11/2026.

Avoid when

Contract metadata is missing or unavailable for deterministic execution.

Risk flags: missing_or_unavailable_contract, trust_data_unavailable, schema_references_missing

Public facts

Every fact links back to the source it came from.

Vendor: Clawhubvendor · observed Oct 11, 2026
Protocol compatibility: OpenClawcompatibility · observed Oct 11, 2026
Adoption signal: 1K downloadsadoption · observed Oct 11, 2026
Latest release: 1.0.0release · observed Apr 17, 2026
Handshake status: UNKNOWNsecurity

Install and run

Setup complexity: low.

clawhub skill install s17a72fh5mgpvvs3dm2ax0yq8984pnqc:cognee-memory

Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Contract: missing

curl -s "https://www.xpersona.co/api/v1/agents/clawhub-smseow001-cognee-memory/snapshot"

Run-check

$0.02 USD

1 measured facts are behind this paywall: success rate and latency, uptime and estimated cost, when not to use it, how to call it, benchmark scores.

Agents pay $0.02 in USDC. A card payment is $0.50, the smallest a card allows.

Documentation

CLAWHUB

6,226 characters of source documentation, loaded on request.

Extracted files

3 files captured from the source.

SKILL.md

---
name: cognee-memory
version: 1.0.0
description: AI知识引擎 - 6行代码实现记忆系统。remember/recall/forget/improve循环，向量+图搜索，支持OpenClaw插件。
keywords: [memory,knowledge,graph,vector,search,cognee,ai,rag]
---

# Cognee Memory System
AI知识引擎 - 6行代码实现记忆系统

**官网：** https://cognee.ai  
**GitHub：** https://github.com/topoteretes/cognee  
**安装：** `pip install cognee`  
**OpenClaw插件：** `@cognee/cognee-openclaw`

---

## 核心API

### 四大操作

| 操作 | 功能 | 说明 |
|------|------|------|
| `remember` | 存储记忆 | 永久存储到知识图谱 |
| `recall` | 查询记忆 | 自动路由最优搜索策略 |
| `forget` | 删除记忆 | 删除过时/错误记忆 |
| `improve` | 优化学习 | 持续学习提升准确性 |

---

## 快速开始

### Python API

```python
import cognee
import asyncio

async def main():
    # 存储到知识图谱
    await cognee.remember("Cognee turns documents into AI memory.")
    
    # 存储到会话缓存（快速）
    await cognee.remember("User prefers detailed explanations.", session_id="chat_1")
    
    # 查询（自动路由）
    results = await cognee.recall("What does Cognee do?")
    for result in results:
        print(result)
    
    # 删除
    await cognee.forget(dataset="main_dataset")

asyncio.run(main())
```

### CLI

```bash
cognee-cli remember "Cognee turns documents into AI memory."
cognee-cli recall "What does Cognee do?"
cognee-cli forget --all
cognee-cli -ui  # 打开本地UI
```

---

## 配置

### 环境变量

```bash
# OpenAI API（必需）
export LLM_API_KEY="your-openai-key"

# 或使用其他LLM提供商
# 见: https://docs.cognee.ai/setup-configuration/llm-providers

# Cognee Cloud（可选）
export COGNEE_SERVICE_URL="https://your-instance.cognee.ai"
export COGNEE_API_KEY="ck_..."
```

---

## 使用场景

### 1. 客服Agent
```
用户："我的发票有问题还没解决"
Cognee追踪：历史交互、失败操作、已解决案例、产品历史
Agent回复："找到2个上月类似计费案例已解决，问题由支付系统同步延迟导致"
```

### 2. SQL Copilot（知识蒸馏）
```
用户："如何计算客户留存率？"
Cognee追踪：专家SQL查询、工作流模式、schema结构、成功实现
Agent回复："高级分析师解决了类似留存查询，这是他们的方案..."
```

### 3. 跨会话记忆
```python
# Session 1
await cognee.remember("用户喜欢详细的解释", session_id="user_123")

# Session 2（跨会话查询）
results = await cognee.recall("用户偏好什么？", session_id="user_123")
```

---

## OpenClaw插件安装

```bash
npm install @cognee/cognee-openclaw
```

插件自动集成：
- `SessionStart` → 初始化记忆
- `PostToolUse` → 捕获行动
- `UserPromptSubmit` → 注入相关上下文
- `PreCompact` → 跨上下文保留记忆
- `SessionEnd` → 桥接到永久知识图谱

---

## vs 其他记忆系统

| 功能 | 我们现有 | Cognee |
|------|---------|--------|
| 存储方式 | 文件 | 向量+图双存储 |
| 搜索方式 | 关键词 | 语义+关系 |
| 学习能力 | 无 | forget+improve |
| 跨Agent | 不支持 | 共享知识图谱 |
| 可视化 | 无 | CLI UI |

---

## 部署选项

| 平台 | 说明 |
|------|------|
| Cognee Cloud | 托管服务 |
| Modal | 无服务器，GPU自动扩展 |
| Railway | 简化PaaS |
| Fly.io | 边缘部署 |
| Render | 简单PaaS |

---

## 示例代码

### 完整记忆循环

```python
import cognee
import asyncio

async def memory_loop():
    # 1. 学习新知识
    await cognee.remember("用户正在学习Python编程")
    await cognee.remember("用户偏好边做边学的教学方式")
    
    # 2. 查询相关记忆
    results = await cognee.recall("用户的学习偏好是什么？")
    
    # 3. 根据反馈改进
    await cognee.improve("纠正对用户偏好的错误理解")
    
    # 4. 忘记错误记忆
    await cognee.forget("错误的假设")

asyncio.run(memory_loop())
```

---

## 安装状态

- Python包：✅ 已安装 `cognee`
- OpenClaw插件：需额外安装 `@cognee

_meta.json

{
  "ownerId": "kn7expjc3a3skyesxdg25d55n184p52s",
  "slug": "cognee-memory",
  "version": "1.0.0",
  "publishedAt": 1776421021660
}

skill-card.md

## Description:

Cognee Memory System helps agents remember, recall, forget, and improve knowledge using Cognee's vector and graph memory tools.

This skill is ready for commercial/non-commercial use.

## Publisher:

[smseow001](https://clawhub.ai/user/smseow001)

### License/Terms of Use:

MIT-0

## Use Case:

Developers and agent builders use this skill to add persistent memory, semantic recall, and knowledge-graph-backed context retrieval to assistants, copilots, and multi-agent workflows.

### Deployment Geography for Use:

Global

## Known Risks and Mitigations:

Risk: The skill instructs users to install external Python and npm packages.

Mitigation: Review package sources and versions in an isolated workspace before using the skill with sensitive projects.

Risk: The described OpenClaw plugin can persist prompts, tool actions, and session context.

Mitigation: Avoid storing secrets or regulated data, confirm where memory is stored, and treat recalled content as untrusted context.

Risk: Bulk forget and persistent-memory behavior may not match user expectations.

Mitigation: Confirm deletion behavior and test forget commands before relying on them in production workflows.

## Reference(s):

- [ClawHub Skill Page](https://clawhub.ai/smseow001/skills/cognee-memory)
- [Cognee Website](https://cognee.ai)
- [Cognee GitHub Repository](https://github.com/topoteretes/cognee)
- [Cognee LLM Provider Configuration](https://docs.cognee.ai/setup-configuration/llm-providers)

## Skill Output:

**Output Type(s):** [guidance, markdown, code, shell commands, configuration]

**Output Format:** [Markdown with Python and bash code blocks]

**Output Parameters:** [1D]

**Other Properties Related to Output:** [May include external package installation steps and memory operation examples.]

## Skill Version(s):

1.0.0 (source: frontmatter and server release metadata)

## Ethical Considerations:

Users should evaluate whether this skill is appropriate for their environment, review any generated or modified files before relying on them, and apply their organization's safety, security, and compliance requirements before deployment.

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