AgentRecall v3.3.27 — Usage Guide
AgentRecall is a persistent memory system with 6 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 nameidentity — who the user is (1-2 lines)insights — top 5 awareness insights (title + confirmation count)active_rooms — top 3 palace rooms by saliencecross_project — insights from other projects matching current contextrecent — today/yesterday journal briefswatch_for — predictive warnings from past correction patterns
How to use the response:
- Read
identity to calibrate your tone and approach
- Read
insights — these are battle-tested lessons. Follow them.
- Read
watch_for — these are patterns where you've been wrong before on this project. Adjust your approach.
- Read
recent to understand where the last session left off
- 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.
check
When: Before executing a complex task where you might misunderstand the human's intent.
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
Two-call pattern:
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.
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
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
- Call
session_start at the beginning. Insights from past sessions prevent repeated mistakes.
- Call
session_end when done. If the session produced decisions, insights, or corrections, save them.
- Insights should be reusable. Write them for a future agent who has never seen this project.
- Match the human's language. If they write in Chinese, save in Chinese.
- Don't over-save. 1-3 insights per session. 1-2
remember calls during work. More is noise.
- Rate your recall results. Feedback makes future retrievals better.
- Use
check for ambiguous tasks. 5 seconds of verification beats 30 minutes of wrong work.
- Read
watch_for warnings. If session_start or check returns warnings, adjust your approach.
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/identity.md # Project intention + goals
palace/graph.json # Memory connection edges
alignment-log.json # Correction history for watch_for
Obsidian-compatible. Open palace/ as a vault to see the knowledge graph.
Platform Compatibility
| Platform | How to install |
|---|
| Claude Code | claude mcp add --scope user agent-recall -- npx -y agent-recall-mcp |
| Cursor | .cursor/mcp.json |
| VS Code / Copilot | .vscode/mcp.json |
| Windsurf | ~/.codeium/windsurf/mcp_config.json |
| Codex | codex mcp add agent-recall -- npx -y agent-recall-mcp |
| Hermes Agent | ~/.hermes/config.yaml under mcp_servers: |
| Roo Code | .roo/mcp.json |
| Claude Desktop | claude_desktop_config.json |
| Gemini CLI | MCP server config |
| OpenCode | MCP server config |
| Any MCP client | command: npx, args: ["-y", "agent-recall-mcp"], transport: stdio |
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.
