Workflow Tool Connectors

Overview

Introduction to Workflow Connectors

Workflow Tool Connectors allow you to easily integrate Mem0's long-term memory layer into popular multi-agent frameworks, automation tools, and developer environments. By connecting Mem0 to tools like LangChain, n8n, OpenClaw, and Pi-Agent, your AI agents gain the ability to recall user preferences, store durable facts across sessions, and maintain context over time without starting from scratch.

Supported Frameworks and Connectors

Mem0 provides dedicated modules and plugins for various platforms and workflow engines.

Sources: LangChain integration (mem0-ts/src/community/src/integrations/langchain/mem0.ts), n8n node description (integrations/n8n-nodes-mem0/nodes/Mem0/Mem0.node.ts), OpenClaw plugin (integrations/openclaw/index.ts), Pi-Agent extension (integrations/pi-agent-plugin/src/memory/tools.ts).

Connecting Mem0 to LangChain

LangChain Memory Integration Overview

The Mem0Memory class integrates Mem0 directly into LangChain chat models and conversation chains, managing memory storage and retrieval automatically.

Configuration and Setup

1. Install the required Mem0 package in your TypeScript or JavaScript project.
2. Initialize the Mem0Memory class by providing your Mem0 API key and a session or user identifier.
3. Attach the memory instance to your LangChain conversation chain or chat model configuration.

Copyimport { Mem0Memory } from "@mem0/community";
import { ChatOpenAI } from "@langchain/openai";
import { ConversationChain } from "langchain/chains";
 
const memory = new Mem0Memory({
  sessionId: "user123",
  apiKey: "your-api-key",
  memoryOptions: {
    user_id: "user123",
    run_id: "run123"
  },
});
 
const model = new ChatOpenAI({
  modelName: "gpt-4o-mini",
  temperature: 0,
});
 
const chain = new ConversationChain({ llm: model, memory });

Automating Workflows with n8n

n8n Community Node Overview

The Mem0 community node for n8n (@mem0/n8n-nodes-mem0) allows you to build automated workflows that interact with your long-term memory layer, making it fully compatible with n8n's AI Agent (Tools Agent) node.

Setting Up the Mem0 Node

1. Add the Mem0 node to your n8n workflow canvas.
2. Create new Mem0 API credentials by providing your API key and base URL (defaults to https://api.mem0.ai).
3. Choose the Resource (Memory) and the desired Operation:
   • Add: Extract and store memories from messages.
   • Search: Search stored facts by meaning and semantic relevance.
   • Get All: List or page through stored memories.
   • Get / Update / Delete: Manage specific memories by their unique Memory ID.

Integrating with OpenClaw

OpenClaw Plugin Overview

The OpenClaw Mem0 plugin brings persistent memory to OpenClaw agents, supporting both the Mem0 cloud platform and self-hosted open-source backends.

Key Features and CLI Initialization

• Auto-Recall & Auto-Capture: Automatically injects relevant memories before each agent turn and captures key conversation facts afterward.
• Per-Agent Isolation: Multi-agent setups automatically read and write from separate user identifier namespaces using session routing.
• Dual Mode: Configure for either cloud platform usage or self-hosted open-source deployments.

You can initialize and log into the OpenClaw Mem0 plugin using the command-line interface:

Copyopenclaw mem0 init --api-key <your-api-key>

Supported authentication and provider configuration choices include:

• Mem0 Platform API Key: Authenticated via the MEM0_API_KEY environment variable or --mem0-api-key.
• Open Source with OpenAI: Self-hosted mode using OpenAI for language models and embeddings.
• OpenSource with Ollama: Fully local mode with no external API keys required.

Sources: OpenClaw plugin configuration (integrations/openclaw/openclaw.plugin.json).

Managing Scopes and Entities

Entity Scoping Overview

When working across multi-agent frameworks, proper scoping ensures that memories are retrieved by the correct user, agent, or project.

• User ID (user_id): Identifies the human user who owns the memory. Essential for cross-session recall.
• Agent ID (agent_id): Partitions memories by specific AI agents in multi-agent environments.
• Run ID / Session ID (run_id): Isolates memories to a specific workflow execution run or chat session.
• App ID (app_id): Groups memories by project, repository, or application namespace.

Related

• Agent IDE Plugins
• Building AI Companions