Cognee Memory

Cognee Memory System

AI knowledge engine — a memory system in 6 lines of code.

Provenance: contributed by ClawHub user @smseow001 (originally published as cognee-memory v1.0.0, MIT-0). Translated from Chinese and adapted for this repository; the verbatim original is preserved in this file's git history.

Website: https://cognee.ai GitHub: https://github.com/topoteretes/cognee Install: pip install cognee OpenClaw plugin: @cognee/cognee-openclaw

Core API

The four operations

Quick start

Python API

import cognee
import asyncio


async def main():
    # Store into the knowledge graph
    await cognee.remember("Cognee turns documents into AI memory.")

    # Store into the session cache (fast)
    await cognee.remember("User prefers detailed explanations.", session_id="chat_1")

    # Query (auto-routed)
    results = await cognee.recall("What does Cognee do?")
    for result in results:
        print(result)

    # Delete
    await cognee.forget(dataset="main_dataset")


asyncio.run(main())

CLI

cognee-cli remember "Cognee turns documents into AI memory."
cognee-cli recall "What does Cognee do?"
cognee-cli forget --all
cognee-cli -ui  # open the local UI

Configuration

Environment variables

# LLM API key (required)
export LLM_API_KEY="your-openai-key"

# Or use another LLM provider
# See: https://docs.cognee.ai/setup-configuration/llm-providers

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

Use cases

1. Customer-support agent

User: "My invoice issue still isn't resolved"
Cognee tracks: interaction history, failed operations, resolved cases, product history
Agent reply: "Found 2 similar billing cases resolved last month; the issue was
caused by a payment-system sync delay"

2. SQL copilot (knowledge distillation)

User: "How do I compute customer retention?"
Cognee tracks: expert SQL queries, workflow patterns, schema structure, successful implementations
Agent reply: "A senior analyst solved a similar retention query — here is their approach..."

3. Cross-session memory

# Session 1
await cognee.remember("User prefers detailed explanations", session_id="user_123")

# Session 2 (query across sessions)
results = await cognee.recall("What does the user prefer?", session_id="user_123")

OpenClaw plugin installation

# Install through OpenClaw's plugin manager (not plain npm):
openclaw plugins install @cognee/cognee-openclaw

# Then configure Cognee as the memory provider:
openclaw cognee setup

The plugin integrates automatically via OpenClaw's hook system:

• before_prompt_build → inject relevant memories into the prompt (auto-recall)
• after_tool_call → capture tool activity as trace entries
• llm_output → capture prompt/answer pairs into the session cache
• agent_end → sync changed memory files after each run
• session_end → bridge the session cache into the permanent knowledge graph

See the plugin README for required hook permissions (allowPromptInjection, allowConversationAccess) and configuration options.

vs. file-based memory

Deployment options

Example code

Full memory loop

import cognee
import asyncio


async def memory_loop():
    # 1. Learn new knowledge
    await cognee.remember("The user is learning Python programming")
    await cognee.remember("The user prefers learning by doing")

    # 2. Query related memories
    results = await cognee.recall("What are the user's learning preferences?")

    # 3. Improve based on feedback
    await cognee.improve("Correct the misunderstanding of the user's preferences")

    # 4. Forget incorrect memories
    await cognee.forget("The incorrect assumption")


asyncio.run(memory_loop())

Installation status

• Python package: install cognee
• OpenClaw plugin: install @cognee/cognee-openclaw separately

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