name: agent-recall description: >- Persistent compounding memory for AI agents. 10 MCP tools: session_start, remember, recall, session_end, check, digest, project_board, project_status, bootstrap_scan, bootstrap_import. 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: 3.4.10 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 v3.4.10 — Usage Guide

AgentRecall is a persistent memory system with 10 MCP tools. This guide describes how and when to use them.

Setup

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

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 provides these MCP 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 to remember.

How to use:

remember({
  content: "We decided to use GraphQL instead of REST because the frontend needs flexible queries",
  context: "architecture decision"    // optional hint, improves routing
})

Returns: routed_to (which store), classification (content type), auto_name (semantic slug generated)

recall

When: You need to find something from past sessions. A decision, a pattern, a lesson.

What it does: Searches ALL stores at once using Reciprocal Rank Fusion (RRF) — each source (palace, journal, insights) ranks internally, then positions merge so no single source dominates. Journal entries decay fast via Ebbinghaus curve (S=2 days); palace entries are near-permanent (S=9999). Returns ranked results with stable IDs.

How to use:

recall({ query: "authentication design", limit: 5 })

Feedback: After using results, rate them. Ratings use a Bayesian Beta model — the mathematically optimal estimate of true usefulness:

recall({
  query: "auth patterns",
  feedback: [
    { id: "abc123", useful: true },   // Beta(2,1) → ×1.33 next time
    { id: "def456", useful: false }   // Beta(1,2) → ×0.67 next time
  ]
})

Feedback is query-aware — rating something "useless" for one query doesn't penalize it for unrelated queries.

session_end

When: End of session, after work is done.

What it does in one call:

• Writes daily journal entry
• Updates awareness with new insights (merge or add)
• Consolidates decisions/goals into palace rooms
• Archives demoted insights (preserved, not deleted)

How to use:

session_end({
  summary: "Built auth module with JWT refresh rotation. Fixed CORS bug.",
  insights: [
    {
      title: "JWT refresh tokens need httpOnly cookies — localStorage is vulnerable",
      evidence: "XSS attack vector discovered during security review",
      applies_when: ["auth", "jwt", "security", "cookies"],
      severity: "critical"
    }
  ],
  trajectory: "Next: add rate limiting to API endpoints"
})

Rules for insights:

• 1-3 per session. Quality over quantity.
• Must be reusable. "Fixed a bug" is NOT an insight. "API returns null when session expires — always null-check auth responses" IS an insight.
• applies_when keywords determine when this insight surfaces in future sessions across ALL projects.

Return fields:

• journal_written — boolean, true if journal entry was saved
• awareness_updated — boolean, true if any insight was stored
• palace_consolidated — boolean, true if palace rooms were updated
• insights_processed — number of insights accepted
• quality_warnings — advisory warnings if insights are too short, lack evidence, or use event-verb phrasing (never blocks saves)
• card — formatted save summary (box-drawing card)
• merge_suggestions — array of similar recent entries (optional)

check

When: Before executing a complex task where you might misunderstand the human's intent. Also for tracking decision quality over time.

What it does:

• Records your understanding of the goal
• Returns watch_for — patterns from past corrections on this project
• Returns similar_past_deltas — times you misunderstood similar goals before
• After human responds, record the correction for future agents
• Optionally tracks decision trails with prior/posterior/evidence for calibrated judgment

Two-call pattern (correction tracking):

Call 1 — before work:

check({
  goal: "Build REST API for user management",
  confidence: "medium",
  assumptions: ["User wants REST, not GraphQL", "CRUD endpoints", "PostgreSQL backend"]
})

Read the watch_for response. If it says "You've been corrected on API style 3 times", ASK the human before proceeding.

Call 2 — after human corrects (if they do):

check({
  goal: "Build REST API for user management",
  confidence: "high",
  human_correction: "Actually wants GraphQL, not REST",
  delta: "API style preference — assumed REST, human prefers GraphQL"
})

This feeds the predictive system. Future agents on this project will get warnings.

Decision trail (Bayesian-inspired calibration):

For major decisions, track confidence and outcome to calibrate judgment over time:

check({
  goal: "Use GraphQL instead of REST",
  confidence: "medium",
  prior: 0.7,                    // initial confidence (0-1)
  evidence: [
    { factor: "Frontend needs flexible queries", direction: "supports", weight: 0.2 },
    { factor: "No GraphQL experience on team", direction: "weakens", weight: 0.3 }
  ],
  posterior: 0.55,               // updated confidence after evidence
  outcome: "rejected"            // final result: "confirmed", "rejected", "partial", or free text
})

When outcome is provided, the decision trail is persisted to the palace decisions room. After 3+ closed decisions, session_start surfaces calibration warnings: "Your priors average 0.8 but outcomes average 0.5 — you're overconfident."

Returns: recorded, watch_for, similar_past_deltas, decision_id (when outcome provided), decision_trail_saved, calibration_note

project_board

When: Start of a new session when you don't know which project to work on.

What it does: Scans all projects and returns a status board — last activity date, pending work, active blockers. Use this before session_start to pick which project to load.

project_board()

project_status

When: Quick check on a specific project's health without loading full context.

What it returns: Last trajectory, active blockers, palace room freshness (stale flag), next steps, summary line. Lighter than session_start — no awareness or cross-project loading.

project_status({ project: "auto" })

bootstrap_scan

When: First time using AgentRecall, or when /arstatus shows an empty board.

What it does: Scans your machine for existing projects — git repos, Claude AutoMemory (~/.claude/projects/), and CLAUDE.md files. Returns a structured report of what CAN be imported. Read-only, no writes.

What it scans:

• ~/Projects/, ~/work/, ~/code/, ~/dev/, ~/src/, ~/repos/, ~/github/ for git repos
• ~/.claude/projects/ for Claude AutoMemory (user profile, project memories, feedback)
• CLAUDE.md files in project roots

How to use:

bootstrap_scan()

Returns: projects (array of discovered projects with importable items), global_items (user profile), stats (totals + scan duration)

bootstrap_import

When: After reviewing bootstrap_scan results, to import selected projects.

What it does: Creates AgentRecall entries for discovered projects — palace rooms, identity.md, knowledge entries from Claude memory, initial journal from git history.

How to use:

bootstrap_import({
  scan_result: "<JSON from bootstrap_scan>",
  project_slugs: ["my-app", "api-server"],    // optional: import only these
  item_types: ["identity", "architecture"]     // optional: import only these types
})

CLI equivalent:

ar bootstrap                    # scan and show what's available
ar bootstrap --dry-run          # preview what would be imported
ar bootstrap --import           # import all new projects
ar bootstrap --import --project my-app  # import one project

What gets imported per project:

• identity — palace identity.md from project name + description + language
• memory — Claude AutoMemory .md files → palace knowledge room
• architecture — CLAUDE.md content → palace architecture room
• trajectory — git log → initial journal entry with recent activity

Safety:

• Scan is read-only — never writes to your machine or to AgentRecall
• Import only writes to ~/.agent-recall/, never modifies source files
• Skips .env, credentials, .pem, .key files — never reads secrets
• Projects already in AgentRecall are skipped (no double-import)

Session Flow

Start of session

1. session_start()           → load context, read insights and warnings
2. Present brief to human    → "Last session: X. Insights: Y. Ready."
3. check() if task is complex → verify understanding before work

During work

4. remember() when you learn something   → auto-routes to right store
                                           (stores: journal for daily activity, palace rooms for persistent decisions, awareness for cross-project insights)
5. recall() when you need past context   → searches everything
6. check() before major decisions        → verify understanding

End of session

7. check() with corrections if any       → record what human corrected
8. session_end()                          → save journal + insights + consolidation
9. Done — all data saved locally (only push to git if user explicitly asks)

How Memory Compounds

Each layer feeds the next. The system gets better the more you use it.

SAVE: remember("JWT needs httpOnly cookies")
  → Auto-named: "lesson-jwt-httponly-cookies-security"
  → Indexed in palace + insights
  → Auto-linked to "architecture" room (keyword overlap)
  → Salience scored: recency(0.30) + access(0.25) + connections(0.20) + ...

RECALL: recall("cookie security") — 3 sessions later, different project
  → Finds the JWT insight via keyword match + graph edge traversal
  → Agent rates it useful → feedback boosts future ranking
  → Next recall on similar query → this result surfaces higher

COMPOUND: After 10 sessions
  → 200-line awareness contains cross-validated insights
  → watch_for warns about past mistakes before they repeat
  → Corrections auto-promote to awareness at 3+ occurrences
  → Graph connects related memories across rooms automatically

Best Practices

1. Call session_start at the beginning. Insights from past sessions prevent repeated mistakes.
2. Call session_end when done. If the session produced decisions, insights, or corrections, save them.
3. Insights should be reusable. Write them for a future agent who has never seen this project.
4. Match the human's language. If they write in Chinese, save in Chinese.
5. Don't over-save. 1-3 insights per session. 1-2 remember calls during work. More is noise.
6. Rate your recall results. Feedback makes future retrievals better.
7. Use check for ambiguous tasks. 5 seconds of verification beats 30 minutes of wrong work.
8. Read watch_for warnings. If session_start or check returns warnings, adjust your approach.
9. Run bootstrap on first install. If /arstatus shows no projects, bootstrap_scan discovers what's already on your machine and imports it in seconds.
10. Check active_rooms in session_start. Palace rooms with high salience contain your project's most important decisions and patterns. Rooms marked stale may need updating.

Storage

All data is local markdown + JSON at ~/.agent-recall/. No cloud, no telemetry, no API keys.

~/.agent-recall/
  awareness.md                              # 200-line compounding document (global)
  awareness-state.json                      # Structured awareness data
  awareness-archive.json                    # Demoted insights (preserved, not deleted)
  insights-index.json                       # Cross-project insight matching
  feedback-log.json                         # Retrieval quality ratings
  projects/<name>/
    journal/YYYY-MM-DD.md                   # Daily journals (legacy)
    journal/YYYY-MM-DD--arsave--NL--slug.md # Smart-named journals (auto-save)
    palace/rooms/<room>/                    # Persistent knowledge rooms
    palace/rooms/decisions/                 # Decision trail records (prior/posterior/outcome)
    palace/identity.md                      # Project intention + goals
    palace/graph.json                       # Memory connection edges
    alignment-log.json                      # Correction history for watch_for
    digest/                                 # Pre-digested context summaries

Obsidian-compatible. Open palace/ as a vault to see the knowledge graph.

Platform Compatibility

All platforms use the same tools. No platform-specific behavior.

Security & Privacy

• Zero network: No outbound HTTP requests, no telemetry, no analytics, no cloud sync. All operations are local filesystem reads/writes.
• Zero credentials: No API keys, tokens, or environment variables required.
• Scoped filesystem access: Reads/writes only to ~/.agent-recall/ (configurable via --root flag). Does not access files outside this directory unless the agent explicitly passes project-specific paths.
• No code execution: The MCP server does not execute arbitrary code, run shell commands, or spawn child processes.
• Transparent storage: All data is human-readable markdown and JSON. Inspect it anytime: ls ~/.agent-recall/ or open it as an Obsidian vault.
• Open source: Full source at github.com/Goldentrii/AgentRecall. MIT license.

What & Why · 什么和为什么

AgentRecall is not a memory tool. It's a learning loop.

Memory is the mechanism. Understanding is the goal. Every time you correct your agent — "no, not that version", "put this section first", "ask me before you assume" — that correction is stored, weighted, and recalled next time.

After 10 sessions, your agent doesn't just remember your project. It understands how you think: your priorities, your communication style, your non-negotiables.

AgentRecall 不是记忆工具，是学习闭环。

记忆是机制，理解才是目标。每一次纠正——"不是那个版本"、"先放这一段"、"假设之前先问我"——都会被存储、加权、并在下次召回。

跑 10 次会话之后，agent 不只是记得项目，它理解你的思考方式：优先级、沟通风格、不可妥协的底线。

Five things that make it different:

• Correction-first. When you say "no, that's wrong", we log a CorrectionRecord with severity, holder, and evidence. After N confirmations across sessions, it auto-promotes to a cross-project insight.
• Measurable learning loop. Every correction tracks retrieved_count, heeded_count, recurrence_count, precision. The KPI that matters: did the same bug recur after we warned about it?
• Five memory types. Episodic, semantic, procedural, narrative, correction — mapped to canonical cognitive-psychology taxonomy (Squire 2004, Tulving 1972).
• Local markdown only. Everything lives in ~/.agent-recall/. Open it in Obsidian. Grep it in the terminal. Version it in git. No cloud, no API keys, no lock-in.
• Backed by published math. FSRS-lite decay (Ebbinghaus → SuperMemo → FSRS-6), Modern Hopfield retrieval (Ramsauer 2020), RRF fusion (Cormack 2009).

让它不同的五件事：

• 以纠正为先。 你说"不对"时，我们记下 CorrectionRecord（严重度、归属、证据）。跨会话被确认 N 次后，自动晋升为跨项目的 insight。
• 可量化的学习闭环。 每条纠正都跟踪 retrieved_count（被召回多少次）、heeded_count（被遵守多少次）、recurrence_count（同样的 bug 是否复发）、precision。唯一重要的 KPI：警告之后同样的 bug 还复发吗？
• 五种记忆类型。 Episodic、semantic、procedural、narrative、correction —— 对应认知心理学经典分类（Squire 2004、Tulving 1972）。
• 只用本地 markdown。 一切都在 ~/.agent-recall/。用 Obsidian 打开、用终端 grep、用 git 版本管理。零云、零 API key、零锁定。
• 基于已发表数学。 FSRS-lite 衰减（Ebbinghaus → SuperMemo → FSRS-6）、Modern Hopfield 检索（Ramsauer 2020）、RRF 融合（Cormack 2009）。

5 Memory Layers · 五层记忆模型

The canonical cognitive-psychology taxonomy mapped to your agent's filesystem · 把认知心理学的经典记忆分类映射到你的文件系统：

All five layers share one canonical naming grammar (<scope>/<type>/[<topic>/]<temporal>--<slug>.md) so any agent — Claude, Codex, future LLM — can compose retrieval paths from intent instead of grepping five conventions. Existing files keep working via a legacy_path virtual-key view. No migration needed.

所有五层共享一个 规范命名语法（<scope>/<type>/[<topic>/]<temporal>--<slug>.md），任何 agent —— Claude、Codex、未来的 LLM —— 都能用意图组合检索路径，不用 grep 五套命名约定。旧文件通过 legacy_path 虚拟键视图继续可用。无需迁移。

The Session Loop · 会话循环

Without /arstatus, a fresh agent has zero orientation. Without /arsave, nothing compounds. These two are the entire loop. 没有 /arstatus，新 agent 完全失去方向。没有 /arsave，什么都不会复合。这两个就是整个闭环。

Already Using Another Memory System? · 已经用过别的？

/arbootstrap scans your machine and imports everything: git repos, Claude AutoMemory (~/.claude/projects/), CLAUDE.md files. Read-only scan, secrets never touched.

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