ReMe/docs/en/integrations.md

title	description
Agent Integrations	Connect ReMe to agents through the CLI, HTTP, MCP, Skills, and host adapters.

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
DeepSeek Harness	@agentscope-ai/reme-dsh-plugin profile bundle
OpenClaw	@agentscope-ai/reme-openclaw-plugin
Claude Code	Shared HTTP 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_read.

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.

DeepSeek Harness

Install the self-contained DeepSeek Harness plugin:

dsh plugin --profile web add @agentscope-ai/reme-dsh-plugin

Release links: Awesome DSH Plugin and npm.

It injects long-term-memory usage guidance into new root-agent sessions and exposes the read-only reme_search tool; it does not preload the full memory history into the prompt. Completed user/assistant turns can be submitted to auto_memory in background batches, while a timezone-aware schedule runs auto_dream to consolidate daily notes.

DSH settings configure the endpoint, guidance language, search limits, capture interval, root-agent filtering, and consolidation schedule. The ReMe Status page exposes Overview, Auto Memory, Memory Consolidation, Components, Journal, and Personal Knowledge Base views. Runtime counters are diagnostic state; workspace Markdown remains the durable source of truth.

OpenClaw

Install the independently published OpenClaw plugin:

openclaw plugins install clawhub:@agentscope-ai/reme-openclaw-plugin

Release links: ClawHub and npm. The plugin provides its own host-specific ReMe HTTP boundary and release lifecycle.

Claude Code

The Claude Code plugin connects every Claude Code window to one ReMe HTTP process at http://127.0.0.1:2333/mcp by default. The reme-memory Skill selects among semantic search, topological traverse, and state-oriented daily_list / frontmatter_read, then reads and cites the relevant workspace paths.

On Stop, the hook passes only the Claude Code session_id to the server-side auto_memory_cc job. On POSIX systems it detaches the potentially long model call so Claude Code can stop immediately; unreachable-service and other best-effort failures are written to the plugin log instead of blocking the host. ReMe resolves the local transcript, and repeated Stop events with no new messages do not create duplicate memory.

Hermes Agent

integrations/hermes_agent/ provides a memory provider with HTTP and embedded modes. It recalls context before model calls and asynchronously invokes auto_memory after each turn. Its config_schema.py is rendered by Hermes' generic memory settings UI.

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.