Agent Integrations

ReMe keeps memory in an independent service and a user-owned workspace. Multiple agents can call the same memory system without binding storage to one model or host.

Choose an interface

Scenario	Recommended interface
Local script or hook	ReMe CLI
Application backend	HTTP Client
Tool-protocol host	MCP
TypeScript agent	@agentscope-ai/reme
Claude Code	MCP + Skill + Stop Hook
Hermes Agent	Memory provider adapter
Codex or another coding agent	reme_memory Skill or MCP

General memory loop

1. Before answering, call search for relevant memory.
2. Use read on high-value results and traverse when relationships matter.
3. Retain workspace-relative source paths in the answer.
4. At session end, pass source messages to auto_memory.
5. Let background or scheduled workflows consolidate daily notes into digest memory.

An empty search result must remain empty; do not present model inference as recalled history.

MCP

The default HTTP service exposes streamable HTTP MCP at http://127.0.0.1:2333/mcp. Common tools include search, read, traverse, list, auto_memory, and proactive.

Use service.jobs to expose a read-only subset or keep write tools in a separate configuration.

CLI and Skill

skills/reme_memory/SKILL.md defines a general workflow for agents that can run local commands: installation checks, service discovery, retrieval, reading, and persistence boundaries.

It deliberately avoids silently modifying Python environments, stopping unknown processes on port conflicts, writing recalled tool output back as conversation source, or persisting credentials.

TypeScript, OpenClaw, and DeepSeek Harness

The @agentscope-ai/reme TypeScript package provides the shared HTTP client and host adapters. See the dedicated guides for DeepSeek Harness and OpenClaw.

Claude Code

integrations/claude_code/ provides streamable HTTP MCP configuration, a reme-memory Skill, and a Stop hook that calls auto_memory_cc. Follow that directory's README for installation.

Hermes Agent

integrations/hermes_agent/ provides a memory provider that recalls context before model calls and asynchronously invokes auto_memory after each turn.

Production guidance

• choose a stable absolute workspace_dir;
• reuse a service discovered by reme find_reme;
• treat reme help as the active Job contract;
• apply timeouts and failure logging to writes;
• do not block the host's core response path when memory is temporarily unavailable;
• use authentication, TLS, and a minimal Job allowlist for remote access.