Universal Shared Memory for AI Agents

The Silo Problem: Why Every AI Tool Forgets

You spend 45 minutes teaching Claude Code your backend database conventions, your preferred API response format, and your deployment credentials. Then you switch over to ChatGPT to outline a marketing campaign, or command Hermes Agent on Telegram to run a midnight maintenance script.

Neither of them has any clue what Claude just learned. You end up copy-pasting system prompts, re-explaining project architecture, and watching background agents hallucinate stale parameters.

The root cause is structural: each AI platform treats memory as an internal proprietary silo.

The Dual Roles: Builders vs. Operators

A modern AI engineering workflow divides naturally into two archetypes:

• Builders (Claude Code, ChatGPT/Codex): High cognitive horsepower tools you sit with at your keyboard. They run in interactive bursts. When you close your terminal or laptop lid, their context window vanishes.
• Operators (Hermes Agent, OpenClaw): Autonomous agents living on cloud servers or background daemons. You control them via Telegram, Discord, or webhooks. They execute cron jobs, monitor uptime, scrape data, and dispatch alerts while you sleep.

When both tap into the exact same memory store, you build an architecture once with your builder at 2 PM, and at 3 AM your background operator executes tasks respecting the exact same rules without a single extra prompt.

Connecting All 5 Platforms (Step-by-Step)

1. Claude.ai & Claude Code

Claude can be integrated via the MCP (Model Context Protocol) server or the native Mem0 plugin:

# Add memory marketplace and connector plugin
/plugin marketplace add mem0ai/mem0
/plugin install mem0@mem0-plugins

# Or configure MCP in ~/.claude/settings.json
{
  "mcpServers": {
    "shared-memory": {
      "command": "npx",
      "args": ["-y", "@mem0/mcp-server"],
      "env": { "MEM0_API_KEY": "m0-xxxx", "USER_ID": "dev_master" }
    }
  }
}

2. ChatGPT (Custom GPT or Actions)

In ChatGPT, create a Custom GPT or use Developer Actions pointing to your Memory endpoint OpenAPI spec:

paths:
  /v1/memories/:
    post:
      summary: "Store memory node"
      operationId: "addMemory"
      parameters:
        - name: "user_id"
          in: query
          required: true
          schema: { type: string, default: "dev_master" }

3. Google Gemini (Vertex AI / Extensions)

Gemini utilizes Function Calling tools or Google Workspace Extensions configured to communicate with the shared vector memory bank via REST webhook.

4. Hermes Agent (Nous Research)

Hermes Agent connects natively via skill tools or MCP. You provide your shared API key and user identifier in your configuration profile:

# Enable shared memory skill in Hermes
hermes config set memory.provider "mem0"
hermes config set memory.user_id "dev_master"
hermes config set memory.api_key "env:SHARED_MEMORY_KEY"

5. OpenClaw (Browser Automation)

OpenClaw pulls session context from the shared memory bank before navigating web DOMs, injecting credentials and learned user preferences dynamically into web forms.

Which Memory Substrate Should You Choose?

The #1 Mistake That Breaks Sync Silently

95% of engineers who fail to get shared memory working hit the ID Mismatch Trap:

Always standardize on a single, global user token (e.g. edo_primary_cluster) across all 5 clients.