# Agenticx Memory Architect

Guide for setting up and using the AgenticX memory system including Mem0 integration...

- Repository: https://github.com/DemonDamon/AgenticX (866 stars)
- Category: AI and agents
- License: Apache-2.0
- Last updated: 2026-11-01
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add DemonDamon/AgenticX --skill agenticx-memory-architect -g -a claude-code -y`
Codex: `npx skills add DemonDamon/AgenticX --skill agenticx-memory-architect -g -a codex -y`
Cursor: `npx skills add DemonDamon/AgenticX --skill agenticx-memory-architect -g -a cursor -y`
Gemini CLI: `npx skills add DemonDamon/AgenticX --skill agenticx-memory-architect -g -a gemini-cli -y`

## Description

Guide for setting up and using the AgenticX memory system including Mem0 integration, long-term memory, context management, and memory-enhanced agents. Use when the user wants to add memory to agents, persist conversation history, build memory-aware workflows, or integrate with Mem0 for long-term recall.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# AgenticX Memory Architect

Guide for building agents with persistent memory capabilities.

## Overview

AgenticX integrates with Mem0 for long-term memory, providing agents with the ability to remember past interactions, learn from experience, and maintain context across sessions.

## Installation

```bash
pip install "agenticx[memory]"
# Includes: mem0, chromadb, qdrant-client, redis, milvus
```

## Memory System Components

| Component | Purpose |
|-----------|---------|
| `MemoryManager` | Core memory management interface |
| `Mem0Integration` | Bridge to Mem0's memory engine |
| `ContextMemory` | Short-term, session-scoped memory |
| `LongTermMemory` | Persistent, cross-session memory |

## Basic Memory Usage

### Initialize Memory

```python
from agenticx.memory import MemoryManager

memory = MemoryManager(
    provider="mem0",
    config={
        "llm": {"provider": "openai", "config": {"model": "gpt-4"}},
        "vector_store": {"provider": "chroma"}
    }
)
```

### Store and Retrieve

```python
# Add a memory
memory.add(
    content="User prefers concise reports with bullet points",
    user_id="user-123",
    agent_id="analyst"
)

# Search memories
results = memory.search(
    query="What format does the user prefer?",
    user_id="user-123"
)
for r in results:
    print(f"[{r.score:.2f}] {r.content}")

# Get all memories for a user
all_memories = memory.get_all(user_id="user-123")
```

## Memory-Enhanced Agents

### Attach Memory to an Agent

```python
from agenticx import Agent, AgentExecutor
from agenticx.memory import MemoryManager
from agenticx.llms import OpenAIProvider

memory = MemoryManager(provider="mem0")
agent = Agent(
    id="assistant",
    name="Personal Assistant",
    role="Assistant with memory",
    goal="Help users while remembering their preferences",
    organization_id="default"
)

executor = AgentExecutor(
    agent=agent,
    llm=OpenAIProvider(model="gpt-4"),
    memory=memory
)

The rest: https://github.com/DemonDamon/AgenticX/blob/HEAD/agenticx/skills/agenticx-memory-architect/SKILL.md