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toon-memory

The Continuity Layer for AI Agents — AI agents shouldn't have to relearn your project every session.

npm version License: MIT CI Docs MCP Badge

Table of Contents

Overview

Ever had that feeling where your AI agent forgets everything from yesterday's session? You explain the same architecture decision for the third time, and it still suggests the approach you already rejected?

toon-memory fixes this. It's the Continuity Layer for AI Agents — a lightweight system that preserves your project's knowledge, decisions, and conventions across sessions, so every session starts where the last one ended. Fully local and private, over MCP — no cloud, no server.

📖 Read the documentation

Real-world use cases

Scenario What toon-memory does
Design debates "We chose Redis over Memcached because of pub/sub support"
Framework choices "This project uses Zod for validation, not Joi"
Bug fixes "Redis pool exhaustion — fix was max_connections=20"
Architecture notes "Broker service uses RESP protocol, not HTTP"
Onboarding "The deploy script lives in scripts/deploy.sh"
Team context "PR #142 reverted the caching change — don't re-add it"

Blog Post

Read How toon-memory Makes Your AI Agent Smarter to see a real-world demo of persistent memory in action.

Features

  • A complete memory toolkit — Full memory management via Model Context Protocol, including memory_smart_recall (unified recall with session bias), memory_sessions for multi-session coordination, context_* tools for one-call context generation (briefing, diff, focus, health audit, export), memory_compress (LLM-powered compression), memory_consolidate (deterministic dedup/merge/cleanup), memory_primer (auto-injected context), memory_merge_sessions (cross-session merge), memory_pin/memory_unpin (pin important entries with priority 1-5), memory_checkpoint (session snapshot with 7d TTL), memory_search (unified search with tag filters + session bias), memory_tag (batch tag operations), memory_export_gist/memory_import_gist (GitHub Gist sync), memory_secret (encrypted secrets vault), memory_export_global/memory_import_global (cross-project conventions), memory_forget (soft/hard delete, restore, supersede), memory_reflect (staleness/quality reflection), and memory_promote (auto-promote low-confidence drafts)
  • MCP Resources — Read memory as context without tool invocations, including a System Primer (auto-generated knowledge map)
  • 22 agents supported — OpenCode, VS Code, Claude Code, Cursor, Windsurf, Cline, Continue, Codex CLI, Gemini CLI, Zed, Antigravity, Aider, KiloCode, OpenClaw, Kiro, Qwen, Kimi, Goose, Junie, Amp, Grok, Trae
  • Interactive installer — Select which agents to configure from a menu
  • SessionStart hooks — Auto-reminders for Claude Code, Codex CLI, Gemini CLI, Antigravity
  • TOON format — 22% fewer tokens than JSON (measured), better LLM comprehension
  • Per-project memory — Each project gets its own memory file
  • Zero config — Just install and use
  • Auto gitignore — Automatically adds .toon-memory/memory/ to .gitignore
  • Date filtering — Search memory by date range
  • Auto-archive — Old entries (>30 days), expired TTL entries, or 100+ entries moved to archive automatically
  • Encryption — AES-256-GCM encryption for sensitive data
  • Watch mode — Auto-backup every N minutes
  • Memory TTL — Configurable per-entry expiration (7d, 30d, or exact dates)
  • Tag inference — Auto-detect tags from content when tags are empty (built-in vocabulary + project dependencies)
  • Memory diff — See what changed since your last session
  • Related entries — Auto-suggest related memories when saving
  • Memory graph — Connect entries with links/[[key]] refs; memory_recall can expand a relationship-aware subgraph for more precise, lower-token recall (no embeddings, no LLM)
  • Token-efficient recall — memory_recall({ compact: true }) returns numeric-indexed entries, drops id/date/file, renders graph edges as ->2, and truncates graph neighbors to snippets
  • BM25 + centrality ranking — Recall re-ranks by BM25 relevance and graph centrality (hubs surface even without the query word); per-hop decay keeps distant nodes low
  • Auto-tag from dependencies — toon-memory init scans package.json/Cargo.toml/requirements.txt/go.mod and writes a project vocabulary so entries mentioning a dependency get auto-tagged with it
  • Smart Recall — memory_smart_recall combines BM25 + graph + decay + quality in one call; the LLM calls this at the start of every task
  • Quality scoring — Every entry gets a 0–1 quality score based on structure (tags, links, content specificity, recency, access count); high-quality entries surface first
  • Merge-dedup — Saving with the same key merges attributes (union of tags, max confidence, latest date, combined links) instead of overwriting
  • Near-duplicate detection — Consolidation detects near-duplicates via Jaccard similarity (threshold 0.7) and merges them
  • Confidence score — Each entry tracks reliability: user-asserted = 1.0, inferred = 0.65–0.75
  • LLM-powered compression — memory_compress uses AI to summarize long entries; memory_consolidate(mode: "low-quality") does batch cleanup deterministically
  • Cross-session merge — memory_merge_sessions merges observations across parallel sessions for a file
  • GitHub Gist sync — memory_export_gist and memory_import_gist sync memory entries via GitHub Gist (zero dependencies)
  • Verbatim mode — config.verbatim preserves original entries instead of overwriting on save
  • Context generation tools — context_generate (full briefing), context_diff (incremental), context_focus (targeted), context_health (audit), context_export (markdown) — each replaces 5-6 manual tool calls. Zero LLM, pure deterministic aggregation
  • System Primer — Auto-injected at session start via systemPrimer(), showing top 5 memories for instant context
  • Path Scoping — Entries can be scoped to file paths via glob patterns (path_scope); recall filters by scope automatically
  • Budget Control — Three output levels: budget: "tiny" (key+1 line, ~50 tokens), "normal" (compact with tags/edges), "deep" (all fields with origin/scope/status). Backward compatible with compact: true
  • Origin Tracking — Each entry tracks its origin (human, agent, inferred); human assertions get a quality boost
  • Soft Delete — memory_forget soft-deletes by default (sets status=obsolete). Restore with memory_forget(key, action: "restore"), hide with action: "soft", permanent removal via action: "hard"
  • Enhanced Health Audit — context_health now detects missing-evidence (path_scope without file) and stale-claims (overlapping content in same category)
  • Typed graph edges — Edges carry types (superseded_by, supersedes, relates), written as type:key in the graph. Explicit links become relates:key, so you can tell how entries are related, not just that they are
  • RRF ranking — Recall fuses BM25 (×3) and graph-centrality ranks with Reciprocal Rank Fusion and an adaptive k = clamp(3..60, round(sqrt(n))). Benchmark (8 gold queries): nDCG 0.776, MRR 0.917 — exact parity with the previous linear scoring. Pass rrf: false to fall back
  • Memory reflect — memory_reflect ranks entries by staleness, quality, and over-connection to surface what needs attention or cleanup. Deterministic, zero LLM
  • Memory supersede — memory_forget(key, action: "supersede", new_key) marks an entry as replaced by a newer one (superseded_by link + supersededOn date). memory_recall({ as_of }) re-includes old entries for point-in-time queries before their supersession
  • Auto-promote — memory_promote promotes low-confidence drafts to active entries deterministically (threshold 0.65, Jaccard dedup), with dryRun by default
  • Explain WHY — memory_recall/memory_smart_recall accept explain: true and append a deterministic reason line to every returned entry (↳ 100% relevance · used 14× · used today · importance HIGH) — why it was retrieved, no LLM
  • Token budgets — budget_tokens caps the recall output by estimated token count; entries accumulate greedily and the tail that would exceed the budget is dropped (0 = no limit)
  • Version supersession — memory_consolidate(mode: "versions") detects entries describing the same subject at different library versions (e.g. "Use React 18" vs "Use React 19") and retires the older ones in favor of the newest
  • Negative memories — a warning category for "do NOT do this" facts; warning entries get a recall boost so the agent sees the landmines before repeating them
  • Language + folder ranking — recall boosts entries written in the same script family (latin/CJK/cyrillic/…) and entries whose path_scope matches the current file
  • Explicit importance — memory_remember({ importance }) sets critical, high, medium, or low. Critical decisions surface first (+0.3), low notes stay out of the way (−0.1); empty = auto (recency + frequency). Re-saving keeps the higher level
  • Evidence layer — every memory_remember save is annotated with an evidence level: verified when its referenced file exists on disk, unverified when it doesn't, conflict when it overlaps a warning or critical/high decision. Conflicts get a +0.15 recall boost (verified +0.03, unverified −0.02) and a ⚠️ CONTRADICTION warning on save — but never block the write
  • Secrets vault — memory_secret stores credentials in an encrypted sidecar (secrets.toon, AES-256-GCM) so data.toon stays a readable open format while sensitive values never hit plaintext
  • Global memory import/export — memory_export_global writes project memory to ~/.toon-memory/memory/global.toon; memory_import_global pulls cross-project conventions back with a one-shot, deterministic, offline merge (never a live dual source)
  • ~1 MB install — three tiny prompt packages (@inquirer/checkbox/select/confirm); the MCP SDK, zod, and the TOON parser are bundled into the shipped binary — a single npm i -g downloads ~1 MB (was ~14 MB) and lands ~4.4 MB on disk (was ~33 MB)

Installation

1. Install

# macOS / Linux
curl -fsSL https://raw.githubusercontent.com/LuiggiVal08/toon-memory/main/install.sh | sh

# Windows (PowerShell)
irm https://raw.githubusercontent.com/LuiggiVal08/toon-memory/main/install.ps1 | iex

# Or with npm (any platform)
npm i -g toon-memory

Tip: The npm install is the most reliable method. The curl/irm scripts are convenience wrappers.

Size: A bare npm i -g toon-memory downloads ~1 MB and installs ~4.4 MB — three tiny prompt packages; everything else (MCP SDK, zod, TOON parser) ships bundled.

2. Configure your agent(s)

# Interactive installer — detects agents and configures MCP
npx toon-memory

The installer will:

  1. Detect which AI agents you have installed
  2. Ask which ones to configure
  3. Add the MCP server config automatically

3. Use it

That's it! In your next agent session, try:

memory_stats      # See what's in memory
memory_recall     # Search memory before reading files
memory_remember   # Save important decisions

Tip: Always run memory_recall at the start of a session. Your agent will have context from previous sessions instantly.

MCP Client Quick Setup

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "toon-memory": {
      "command": "npx",
      "args": ["-y", "toon-memory", "mcp"]
    }
  }
}

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "toon-memory": {
      "command": "npx",
      "args": ["-y", "toon-memory", "mcp"]
    }
  }
}

Windsurf

Add to ~/.codeium/windsurf/mcp_config.json:

{
  "mcpServers": {
    "toon-memory": {
      "command": "npx",
      "args": ["-y", "toon-memory", "mcp"]
    }
  }
}

Supported Agents

Agent Config Location Format Hooks Auto-Setup
OpenCode .opencode/opencode.json + .opencode/plugins/toon-memory.ts Plugin SessionStart (plugin, no top-level hooks) ✅
VS Code / Copilot .vscode/mcp.json JSON — ✅
Claude Code .mcp.json (MCP) + .claude/settings.json (hooks) JSON SessionStart + PostToolUse + Stop ✅
Cursor .cursor/mcp.json JSON — ✅
Windsurf ~/.codeium/windsurf/mcp_config.json JSON — ✅
Cline .cline/mcp.json JSON — ✅
Continue .continue/config.json JSON — ✅
Codex CLI .codex/config.toml TOML SessionStart + PostToolUse + Stop ([[hooks]] event=) ✅
Gemini CLI .gemini/settings.json JSON SessionStart + PostToolUse + Stop (hooks.*) ✅
Zed ~/.config/zed/settings.json JSONC — ✅
Antigravity .agents/mcp_config.json + .agents/hooks.json hooks.json PreInvocation + PostToolUse + Stop (no SessionStart event) ✅
Aider — — — 📝 Instructions
KiloCode ~/.kilocode/mcp_settings.json JSON — ✅
OpenClaw .openclaw.json JSON — ✅
Kiro .kiro/settings/mcp.json JSON — ✅

Tip: You can configure toon-memory for multiple agents at the same time. Each agent gets the same shared memory file at .toon-memory/memory/.

MCP Tools

Tool Description
memory_remember Save a decision, pattern, bug, knowledge, or warning (negative "do NOT do this" memory, recalled with a boost) — optional TTL, auto-tag inference, links to build the memory graph, merge-dedup on same key, auto quality score and confidence. Write-path intelligence: each save is annotated with an evidence level — verified when the referenced file exists on disk, unverified when it doesn't, conflict when it overlaps a warning or critical/high decision (recalled with a boost and surfaced with a ⚠️ CONTRADICTION warning, but never blocks the write)
memory_recall Search memory (use BEFORE reading files, filters expired TTL). mode: "graph" expands a relationship-aware subgraph for higher precision. `budget: "tiny"
memory_smart_recall Unified recall: BM25 + graph + decay + quality in one call. sessionBias boosts entries from the current git branch. explain: true appends per-entry reasons, budget_tokens caps output by estimated tokens. Use at the START of every task. Returns compact, token-efficient output
memory_forget Lifecycle ops by key or id: action: "soft" (default) marks obsolete, "hard" permanently removes, "restore" brings back to active, "supersede" retires it with a superseded_by link to new_key
memory_stats View memory state (including TTL stats, quality distribution, origin/status breakdown, cold memories below quality/access thresholds, and hit rate / duplicate / obsolete metrics)
memory_summary Save/retrieve file summaries
memory_archive Archive old entries (>30 days) and expired TTL entries
memory_diff Show changes since a date (24h, 7d, or exact date)
memory_suggest Find related entries for a given context
memory_encrypt Enable AES-256-GCM encryption
memory_decrypt Disable encryption
memory_backup Create timestamped backup of memory file (auto-prunes to 10 most recent)
memory_captured List activity auto-captured by hooks (opt-in) or clear the log
memory_checkpoint Session checkpoint: creates a snapshot of current memory state with 7d TTL. Useful for rollback reference during long sessions
memory_consolidate Cleanup ops, deterministic (no LLM): mode: "identical" (default) dedupes identical-content entries, "similar" merges near-duplicates (Jaccard >50%), "low-quality" batch-removes low-quality entries (minQuality, dryRun), "versions" retires older library-version entries in favor of the newest
memory_sessions Show active agent sessions (branch, files, last-seen) and soft conflicts for parallel work
memory_compress LLM-powered two-step compression: summarize + overwrite. Uses anthropic/openai CLI if available, otherwise returns prompt for manual compression
memory_primer One-call context primer: top memories + categories + session file changes. Auto-injected at session start
memory_merge_sessions Merge observations across parallel sessions for a file. Deduplicates and optionally auto-promotes to memory
memory_export_gist Export memory entries to a GitHub Gist (public or private). Uses GITHUB_TOKEN or gh CLI
memory_import_gist Import entries from a GitHub Gist. Merges with existing entries (union of tags, max confidence)
memory_secret Encrypted secrets vault (secrets.toon, AES-256-GCM): store/get/list/forget. Keeps data.toon readable while sensitive values stay encrypted at rest. Requires TOON_MEMORY_KEY
memory_export_global Write current project memory to the global file (~/.toon-memory/memory/global.toon). One-shot share of cross-project conventions
memory_import_global Merge cross-project conventions from the global file into this project (one-shot, deterministic, offline). merge: false replaces instead
memory_graph_path BFS shortest path between two entries in the knowledge graph. Shows how concepts are connected
context_brief One-call context briefing: memory + sessions + health in compact markdown. Use instead of 5-6 separate memory_* calls. Zero LLM, pure deterministic aggregation
context_generate Full project briefing: combines project structure, git state, memory entries, and active sessions in one call. Replaces 5-6 manual tool calls
context_diff Incremental briefing: git commits + modified files + new/updated memory + active sessions since last session
context_focus Hyper-focused briefing: only relevant memory + related source files + callers + test files for a query
context_health Memory health audit: orphan links, duplicates, broken file refs, expired TTL, stale sessions, score 0–100
context_export Export memory as markdown: injectable context for system prompts (full or compact)
memory_pin Pin an entry with priority 1-5: pinned entries always appear first in recall results sorted by priority, even without a keyword match
memory_unpin Unpin an entry: remove the priority flag
memory_search Unified search with filters: same as memory_recall plus category, tags, from_date, to_date filters. Tag filter uses AND logic — all specified tags must match. budget controls output verbosity. path_scope filters by glob pattern. sessionBias boosts entries from the current git branch
memory_tag Batch tag operations: add, remove, or set tags on one or more entries by key or id

MCP Resources

Memory is also exposed as MCP resources for direct context reading:

Resource URI Description
Memory Entries toon://memory/entries Full memory dump
Current Memory toon://memory/current Current memory state with recent entries
Memory Stats toon://memory/stats Category counts and TTL info
System Primer toon://memory/summaries Auto-generated knowledge map (top entries, categories, patterns)

MCP Prompts

Prompt Description
summarize_project_context Analyze current TOON memory and generate a compact project summary. Optional intent parameter to focus on a specific area

Examples

Remember a decision

memory_remember({
  category: "decision",
  key: "use-zod",
  content: "Use Zod for validation — simpler than Joi, better TS support",
  file: "src/types.ts",
  tags: "validation;types"
})
// 🧠 Guardado: decision/use-zod (a1b2c3d4)
// Quality score: 0.65 (2 tags, detailed content)
// 🔗 Entradas relacionadas:
//   [pattern] zod-schemas — Shared Zod schemas for API validation

Tip: Use descriptive keys like use-zod instead of vague ones like validation. Your agent searches by key and content, so specificity helps. Saving with the same key auto-merges (union of tags, max confidence).

Remember with TTL

memory_remember({
  category: "knowledge",
  key: "sprint-deadline",
  content: "Sprint ends July 18, feature freeze is July 16",
  ttl: "7d"
})
// 🧠 Guardado: knowledge/sprint-deadline (x1y2z3w4)
// ⏰ TTL: 2026-07-19
// Quality score is calculated automatically.

Tip: Use TTL for temporary context like deadlines, sprint info, or time-sensitive notes. Entries with expired TTL are automatically filtered from search results.

Set explicit importance

memory_remember({
  category: "decision",
  key: "db-choice",
  content: "We chose Postgres over MySQL — JSONB for flexible schemas, better extension ecosystem",
  importance: "critical"
})
// 🧠 Guardado: decision/db-choice (a1b2c3d4)
// 🎯 Importance: critical (+0.3 boost) — surfaces above routine entries

Tip: Mark foundational decisions critical so they always rank near the top of recall. importance accepts critical, high, medium, or low; leave it empty to let the system rank by recency and frequency automatically.

Auto-inferred tags

memory_remember({
  category: "bug",
  key: "redis-connection-timeout",
  content: "Redis connection timeout in production, increased pool size"
  // tags left empty — auto-inferred from content
})
// 🧠 Guardado: bug/redis-connection-timeout (a1b2c3d4)
// 🏷️ Tags inferidos: redis
// Quality score is calculated automatically based on inferred tags and content.

Tip: Leave tags empty and the system will infer them from your content using a built-in vocabulary of 20+ categories (redis, auth, api, db, security, etc.) plus a project vocabulary derived from your dependencies at init time. So if your project depends on redis, any entry mentioning "redis" gets auto-tagged redis.

Search memory

memory_recall({ query: "redis" })
// [bug] redis-pool-fix (i9j0k1l2)
//   Added max_connections=20
//   File: redis.ts | Tags: redis;fix | Date: 2026-07-10

Tip: Search before you read files. This saves tokens and gives your agent context it wouldn't get from code alone. Quality-weighted ranking ensures the most useful entries surface first. Or use memory_smart_recall for a more comprehensive result.

Search with date filter

memory_recall({
  query: "redis",
  from_date: "2026-07-01",
  to_date: "2026-07-31"
})

Tip: Use date filters when you remember roughly when something happened but not exactly what. Quality-weighted ranking still applies.

Archive old entries

memory_archive()
// 📦 Archivadas 5 entradas antiguas
// 📋 Quedan 42 entradas activas

Tip: Run this periodically to keep memory lean. Archived entries are still searchable via memory_recall with date filters. Entries with expired TTL are also archived automatically. Low-quality entries get lower recall priority. Low-quality entries get lower recall priority.

Show changes since last session

memory_diff({ since: "24h" })
// 📋 Cambios desde 2026-07-11:
//
// ➕ Nuevas (2):
//   [decision] use-zod (a1b2c3d4)
//     Use Zod for validation
//   [bug] redis-timeout (e5f6g7h8)
//     Redis connection timeout fix

Tip: Use memory_diff at the start of a session to see what your agent learned since you last worked on the project. New entries include quality scores. New entries include quality scores.

Find related entries

memory_suggest({ context: "redis cache configuration" })
// 🔍 Sugerencias para "redis cache configuration":
//
// [decision] redis-cache-config (a1b2c3d4)
//   Redis cache layer for session storage
//   File: src/cache.ts | Tags: redis;cache | Date: 2026-07-10
//
// [bug] redis-pool-fix (i9j0k1l2)
//   Added max_connections=20
//   File: redis.ts | Tags: redis;fix | Date: 2026-07-10

Tip: Use memory_suggest when you need context about a topic but aren't sure what to search for. Or use memory_smart_recall for a more comprehensive result.

Smart Recall (unified)

memory_smart_recall({ intent: "diseño de base de datos para backend" })
// [1] decision/use-postgres
//   Choose Postgres for ACID compliance and JSON support
//   tags: db;decision · edges: ->2
//
// [2] pattern/db-migrations
//   Use sequential migration files, never edit committed ones
//   tags: db;pattern · edges: ->1
//
// [3] bug/redis-timeout
//   Redis connection timeout — increased pool to 20
//   tags: redis;bug

Tip: Use memory_smart_recall at the START of every task. It combines BM25 + graph + decay + quality in one call — no need to guess what to search for.

Explain WHY a result was returned

memory_recall({ query: "redis", explain: true })
// [decision] redis-cache-config (a1b2c3d4)
//   Redis cache layer for session storage
//   File: src/cache.ts | Tags: redis;cache | Date: 2026-07-10
//   ↳ 92% relevance · used 14× · used today · importance HIGH

The ↳ reason line is deterministic (relevance %, access count, last-used, importance) — no LLM involved. Use explain: true when you want to know why the agent was shown those entries.

Cap output with budget_tokens

memory_recall({ query: "redis", budget_tokens: 300 })
// Entries accumulate greedily; the tail that would exceed the estimate is dropped.
// budget_tokens: 0 (default) = no limit.

Tip: Combine budget_tokens with budget: "deep" for a context window that stays inside a hard token ceiling regardless of memory size.

Full project briefing (one call)

context_generate({})
// # Project Briefing (full)
//
// ## Project
// - Name: my-app
// - Root: /path/to/project
// - Package Manager: npm
// - TypeScript: ✓ (v5.3)
//
// ## Git Status
// - Branch: main
// - 3 uncommitted, 0 untracked
//
// ## Memory (42 entries, 12 patterns, 8 bugs)
// [1] decision/use-postgres
//   Choose Postgres for ACID compliance
//   tags: db;decision
//
// ## Sessions
// - egraterol (main, 2m ago): 42 files touched

Tip: Use context_generate at the start of a session to get full context in one call. Replaces 5-6 separate tool calls.

Memory health audit

context_health({})
// # Memory Health (score: 87/100)
//
// ## Summary
// - 42 entries (12 patterns, 8 bugs, 15 decisions, 7 knowledge)
// - 65.3% average quality
//
// ## Issues (3)
// - Orphan link: pattern/db-migrations → pattern/db-seed (key not found)
// - Duplicate: [bug] redis-pool-fix has identical content
// - Expired TTL: [knowledge] sprint-deadline (expired 2026-07-20)
//
// ## Stale Files (1)
// - src/legacy.ts (deleted, 2 refs)

Tip: Run context_health when memory feels cluttered. Shows orphan links, duplicates, expired TTL entries, broken file references, missing-evidence entries (path_scope without file), and stale claims (overlapping content).

Merge-dedup (automatic)

When you save with the same key, attributes are merged instead of overwritten:

// First save
memory_remember({
  category: "decision",
  key: "use-zod",
  content: "Use Zod for validation",
  tags: "types"
})
// 🧠 Guardado: decision/use-zod (a1b2c3d4)

// Later save with same key — merges automatically
memory_remember({
  category: "decision",
  key: "use-zod",
  content: "Use Zod for validation — also handles API response parsing",
  tags: "types;api"
})
// 🧠 Actualizado: decision/use-zod (a1b2c3d4)
// 🔗 Merge: tags combinados, fecha y links actualizados
// Tags now: "types;api" (union of both)

Tip: Use descriptive, stable keys. The same key = merge, different key = new entry.

Quality scoring

Every entry gets an automatic quality score (0–1) based on structure:

Factor Weight What it measures
Tags 0.3 max More specific tags = higher quality
Links 0.2 max Connected entries = higher quality
Content length 0.3 max Detailed > vague
Recency 0.1 max Recent entries score higher
Specificity 0.1 max Unique words vs repeated words
Origin +0.1/−0.05 Human assertions boosted, inferred slightly penalized

High-quality entries surface first in recall. Check quality with memory_stats:

memory_stats()
// ...
// Calidad promedio: 0.58 (12 con score)

Confidence score

Each entry tracks how reliable the information is:

Source Confidence Meaning
User assertion 1.0 "We use Postgres" — direct statement
Inferred 0.65–0.75 Agent inferred from context
Uncertain 0.50 Agent is guessing

Confidence is preserved on merge (max of both entries).

System Primer

The System Primer is an auto-generated knowledge map exposed as an MCP resource. Agents load it at session start for instant context:

// Exposed as toon://memory/summaries
// Auto-regenerates on every read
// Contains: top entries, categories, patterns

Tip: Add toon://memory/summaries to your agent's system prompt for instant context at session start.

Enable encryption

// First, set TOON_MEMORY_KEY in your environment (or .env file):
// export TOON_MEMORY_KEY="your-secret-key-here"

memory_encrypt()
// 🔐 Encriptación habilitada

Warning: The encryption key must be set via TOON_MEMORY_KEY env var before encrypting. Save it somewhere safe — if you lose it, your memory data is gone forever. Quality scores and confidence are preserved through encryption.

Coordinación multi-sesión

When you run several AI agent sessions in parallel (e.g. three OpenCode sessions on the same repo at once), they can accidentally clobber each other's work. toon-memory ships with memory_sessions, a file-based coordination tool that lets every session see what its siblings are doing — with no server, no network, and no LLM calls.

How it works

  • On startup, a SessionStart hook writes a heartbeat file for the session at .toon-memory/memory/sessions/<id>.json. Each process writes only its own file, so there's no lock contention.
  • The heartbeat records the agent name, the git branch, the files touched, and a last-seen timestamp.
  • Reading across all those files gives every session a shared, eventually-consistent view of who else is active.
  • Dead sessions (process PID no longer alive and a stale heartbeat past the TTL window) are pruned lazily.

The memory_sessions tool

memory_sessions({ conflictsOnly: false })
// 🧭 Sesiones activas (2) — ventana 30 min:
//
// • opencode @ feature/auth (tú)
//   id: a1b2c3d4
//   hace 2 min
//   Archivos:
//     • src/auth.ts
//
// • claude @ feature/db
//   id: e5f6g7h8
//   hace 9 min
//     • src/db.ts
//
// 🔥 Conflictos suaves (1):
//   ⚠️ src/types.ts  ↔  opencode @ feature/auth, claude @ feature/db
  • Pass conflictsOnly: true to skip the session list and show only soft conflicts: 
    memory_sessions({ conflictsOnly: true })
// 🔥 Conflictos suaves (1):
//
// ⚠️ src/types.ts
//    ↔ opencode @ feature/auth (a1b2c3d4), claude @ feature/db (e5f6g7h8)
  • A soft conflict is any file touched by 2+ active sessions — a heads-up that you might be editing the same code. It's not a hard lock, just a warning to coordinate.

Recommended parallel-session habit

  1. At the start of every session, the SessionStart hook already prints the other active sessions and any soft conflicts.
  2. Run memory_smart_recall({ intent: "what I'm working on" }) to get full context (memory + graph + quality).
  3. Run memory_sessions() to see the full picture (branches, files, last-seen) and memory_sessions({ conflictsOnly: true }) if you only care about clashes.
  4. If you share a file with another session, sync up before editing so you don't overwrite each other's changes.

Tip: This is purely local and lock-free — safe to run as often as you like. Combine it with memory_smart_recall({ intent: "project context" }) at session start for both cross-session memory and cross-session presence. The system primer (MCP resource) also provides instant context.

Memory Graph (recall basado en grafo)

When your memory grows, a flat keyword search can return either too much (every match) or the wrong context (no relationships). toon-memory can treat memory as a lightweight knowledge graph so recall returns the right entries with fewer tokens. Combined with quality scoring, the most useful entries surface first.

It's fully deterministic and offline — no embeddings, no vector DB, no LLM, no server. Edges come from two sources:

  • Explicit links — keys you declare when saving an entry.
  • Implicit [[key]] refs — any [[some-key]] mention inside the content.

How it works

  1. memory_remember stores links on the entry (space- or ;-separated keys). Quality score is calculated automatically.
  2. memory_recall({ mode: "graph" }) finds keyword matches (seeds), then expands the ego-subgraph up to hops (1 or 2) along the edges.
  3. Relevance propagates from the seeds to their neighbors, so a related decision or spec surfaces even if it doesn't contain the query word. Quality-weighted ranking ensures the most useful entries appear first.
  4. The result set is capped (limit, default 6) → smaller, more precise context for the agent. Or use memory_smart_recall for a unified call.

Remember with links

memory_remember({
  category: "decision",
  key: "risk-engine-priority",
  content: "The engine prioritizes risk over speed (see [[risk-spec]]).",
  file: "spec.md:10",
  tags: "risk;spec",
  links: "engine-arch"          // explicit edge to another entry
})
// 🧠 Guardado: decision/risk-engine-priority (a1b2c3d4)
// Quality score is calculated automatically based on tags, links, and content detail.

Recall with graph mode

memory_recall({ query: "riesgo", mode: "graph", hops: 2 })
// [decision] risk-engine-priority (a1b2c3d4)
//   The engine prioritizes risk over speed (see [[risk-spec]]).
//   File: spec.md:10 | Tags: risk;spec | Date: 2026-07-01
//   links: engine-arch
//
// [knowledge] risk-spec (a2b3c4d5)
//   Risk specification for the engine.
//   links: risk-engine-priority;engine-arch
//
// [pattern] engine-arch (e6f7g8h9)
//   Engine architecture.
//   links: risk-spec

Tip: Use mode: "graph" when a decision ripples across several entries (architecture, specs, related bugs). For isolated facts, the default flat mode is enough. Or use memory_smart_recall which combines graph + BM25 + quality automatically.

Token-efficient recall (compact)

When every token counts, pass compact: true to get a denser output:

memory_recall({ query: "riesgo", mode: "graph", hops: 2, compact: true })
// [1] decision/risk-engine-priority
//   The engine prioritizes risk over speed (see [[risk-spec]]).
//   tags: risk;spec · edges: ->2, ->3
//
// [2] knowledge/risk-spec
//   Risk specification for the engine.
//   tags: risk · edges: ->1
//
// [3] pattern/engine-arch
//   Engine architecture.
//   tags: engine · edges: ->1

How compact changes the output:

  • Each entry gets a stable numeric index ([1], [2], …) in score order.
  • id, date, and file are dropped — only tags is kept.
  • In graph mode, edges render as ->2 (numeric, not key names).
  • Neighbors reached via the graph (non-seeds) are truncated to a short snippet with an ellipsis, while directly-matched seeds keep their full content.
  • Quality-weighted ranking ensures the most useful entries appear first.
  • The stored .toon file is never mutated — compact only reshapes the response.

Tip: Combine compact: true with mode: "graph" for the smallest possible context window when recalling from a large, interconnected memory. For proactive/background recall, use budget: "tiny" which returns just the key + one line (~50 tokens). Or just use memory_smart_recall which does this automatically.

How recall ranks results

Recall is deterministic and offline (no embeddings, no LLM). Each candidate entry gets a combined score:

  • BM25 relevance — classic probabilistic term-frequency score against the query, using id + category + key + content + file + tags + quality + confidence.
  • Graph centrality — degree-normalized (0..1); a hub connected to many entries scores near 1, so it surfaces even without the query word.
  • Importance — recency + access frequency (same signal used elsewhere).
  • Quality boost — entries with higher quality scores (more tags, links, detail) get a ranking boost.
  • Seed bonus — entries that directly match the query get a flat boost.
  • Per-hop decay — nodes d hops from a seed are multiplied by 0.5^d, so distant context ranks below nearby context.

In graph mode, recall seeds on keyword matches, expands the ego-subgraph up to hops, an