/AgentRecall-X

name: agent-recall
description: >-
  Persistent compounding memory for AI agents. 5 default MCP tools: session_start,
  session_end, remember, recall, check. Full surface (18 tools) available with --full flag.
  Two-verb model: inhale (session_start) and exhale (session_end).
  Correction-first memory with decision trail tracking,
  watch_for warnings, palace rooms with salience scoring, cross-project insight
  matching, same-day journal merging, ambient recall hooks. Local markdown only.
  Zero cloud, zero telemetry, Obsidian-compatible.
  Optional Supabase backend: when configured via `ar setup supabase`, recall()
  uses pgvector cosine similarity on OpenAI/Voyage embeddings instead of keyword
  search — same API, semantic understanding. Gracefully degrades to local search
  if not configured.
origin: community
version: 3.4.30
author: Goldentrii
platform: clawhub
install:
  mcp:
    command: npx
    args: ["-y", "agent-recall-mcp"]
    transport: stdio
    env: {}
security:
  network: none
  credentials: none
  filesystem: read-write ~/.agent-recall/ only
  telemetry: none
  cloud: none
tags:
  - memory
  - persistence
  - multi-session
  - mcp
  - cross-project
  - feedback-loop
  - intelligent-distance
  - auto-naming
  - knowledge-graph
  - obsidian
trigger:
  - "save"
  - "save session"
  - "/arsave"
  - "/arstart"
  - "remember this"
  - "recall"
  - "what did we do last time"
  - "load context"
  - "start session"
  - "end session"
  - "checkpoint"
  - "保存"
  - "记住"
  - "上次做了什么"
  - "加载上下文"
skip:
  - "don't save"
  - "skip memory"
  - "no need"
  - "不用记"
  - "算了"

AgentRecall v3.4.30 — Usage Guide

AgentRecall is a persistent memory system. Default surface: **5 tools** (two verbs + three essentials). Full surface: 18 tools via `npx agent-recall-mcp --full`. This guide describes how and when to use them.

**Two-verb model:** `session_start` (inhale — load context) and `session_end` (exhale — save and compound). Everything else is available but secondary; most agents never need more than the default 5. See [Automaticity Law](#why-5-default-tools) below.

Setup

AgentRecall requires the MCP server to be running. If tool calls fail with "unknown tool", the human needs to install it first.

> **Visual setup guide** (all 13 clients, copy-paste prompts): open `warroom/install.html` from the repo, or the [GitHub raw link](https://raw.githubusercontent.com/Goldentrii/AgentRecall-X/main/warroom/install.html) in a browser.

Installation (human runs once)

**Claude Code:**

claude mcp add --scope user agent-recall -- npx -y agent-recall-mcp

**Cursor** (`.cursor/mcp.json`):

{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }

**VS Code / GitHub Copilot** (`.vscode/mcp.json`):

{ "servers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }

**Windsurf** (`~/.codeium/windsurf/mcp_config.json`):

{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }

**Codex:**

codex mcp add agent-recall -- npx -y agent-recall-mcp

**Hermes Agent** (`~/.hermes/config.yaml`):

mcp_servers:
  agent-recall:
    command: npx
    args: ["-y", "agent-recall-mcp"]

**Roo Code** (`.roo/mcp.json`):

{ "mcpServers": { "agent-recall": { "command": "npx", "args": ["-y", "agent-recall-mcp"] } } }

**Any MCP-compatible agent:**

command: npx
args: ["-y", "agent-recall-mcp"]
transport: stdio

---

Tools

AgentRecall's default surface provides **5 tools**. Start the server with `--full` to enable the complete 18-tool surface.

**Default tools (always available):** `session_start`, `session_end`, `remember`, `recall`, `check`

**Full-mode only (`--full`):** `memory_query`, `check_action`, `register_rule`, `pipeline_open`, `pipeline_close`, `pipeline_list`, `pipeline_current`, `pipeline_show`, `skill_write`, `skill_recall`, `skill_list`, `dashboard_export`, `session_end_reflect`, `project_board`, `project_status`, `digest`, `bootstrap_scan`, `bootstrap_import`

---

Default tools

`session_start`

**When:** Beginning of a session, to load prior context.

**What it returns:**

• `project` — detected project name
• `identity` — who the user is (1-2 lines)
• `insights` — top 5 awareness insights (title + confirmation count + severity)
• `active_rooms` — top 5 palace rooms by salience (with staleness flag + last_updated)

_(Palace = your project's long-term knowledge store, organized into topic rooms like "architecture", "goals", "blockers". Salience = relevance score 0-1 based on recency, access frequency, and connections. Rooms with stale=true haven't been updated in 7+ days.)_

• `cross_project` — insights from other projects matching current context
• `recent` — today/yesterday journal briefs
• `watch_for` — predictive warnings from past correction patterns + decision calibration
• `corrections` — P0 behavioral rules (max 10, always loaded, never expire)
• `resume` — structured re-entry briefing: `last_date`, `last_trajectory`, `sessions_count`

**How to use the response:** 1. Read `identity` to calibrate your tone and approach 2. Read `insights` — these are battle-tested lessons. Follow them. 3. Read `watch_for` — these are patterns where you've been wrong before on this project. Adjust your approach. 4. Read `recent` to understand where the last session left off 5. Present a brief to the human: project name, last session summary, relevant insights

**Example call:**

session_start({ project: "auto" })

`remember`

**When:** You learn something worth keeping. A decision, a bug fix, an insight, a session note.

**What it does:** Auto-classifies your content and routes it to the right store:

• Bug fix / lesson → knowledge store
• Architecture / decision → palace room
• Cross-project pattern → awareness system
• Session activity → journal

You do NOT need to decide where it goes. Just describe what