Mnemosyne
Mnemosyne 🧠
Produktionsreife, lokal-first hierarchische Gedächtnis-Engine für autonome KI-Agenten.
Mnemosyne stattet KI-Agenten (Hermes, OpenClaw, OpenManus, Claude Desktop, Cursor, Windsurf, CrewAI, LangChain) mit permanentem, strukturiertem Langzeitgedächtnis aus. Es verbindet menschenlesbare Obsidian Markdown Vaults mit Hochgeschwindigkeits-PostgreSQL + pgvector (HNSW) oder SQLite ohne Konfiguration und ermöglicht hybride Reciprocal Rank Fusion (RRF)-Suche, Wikilink-Wissensgraphen, Ebbinghaus-Zeitabklingung, agentenübergreifendes Fleet-Sharing und asynchrone Google-Drive-Cloud-Synchronisierung.
📑 Inhaltsverzeichnis
Related MCP server: Synapto
⚡ Vollständige Feature-Matrix
Feature | Beschreibung | Nutzen | ||
🤖 MCP-Nativ (v2024-11-05) | Standardisierter stdio-JSON-RPC-Protokollserver | Nahtlose Integration für Claude, Cursor, Windsurf, Hermes, OpenClaw | ||
🔎 HNSW-Vektorsuche mit hoher Trefferquote |
| Sub-Millisekunden-Vektorabruf ohne Clustering-Neutraining | ||
⚖️ Hybrider RRF-Abruf | Reciprocal Rank Fusion aus Dense Embeddings (1.0) + GIN FTS (0.8) + Graph (0.6) + Salience (0.2) | Erfasst exakte technische Schlüsselwörter (CVEs, Code-Tokens) & Fuzzy-Semantik | ||
🏛️ Wing- & Room-Scoping | 2-stufige Taxonomie ( | Eliminiert Kontextfenster-Aufblähung & domänenübergreifende Halluzination | ||
🌐 Agentenübergreifendes gemeinsames Gedächtnis | Zentrale | Kollektive Fleet-Intelligenz ohne Kompromittierung privater Datenbanken | ||
☁️ Asynchrone Google-Drive-Synchronisierung | Lokal-first bidirektionale Synchronisierungs-Engine mit MD5-Diffing und | Sub-Millisekunden-Agent-I/O lokal + automatische Cloud-Backups & Team-Sharing | ||
⏳ Ebbinghaus-Zeitabklingung | Exponentieller Abfall $Salience_t = Salience_0 \times (0.95)^{\Delta t}$ mit 90-Tage-Auto-Archivierung | Entfernt automatisch transientes Rauschen, während aktive Erinnerungen scharf bleiben | ||
📌 Pinned-Memory-Immunität | Permanente Immunität gegen Abklingung durch | Garantiert, dass grundlegende Anweisungen und Kernregeln nie verblassen | ||
📖 Obsidian Zettelkasten | Schreibt menschenlesbare Markdown-Notizen mit YAML-Frontmatter & | Direkte visuelle Inspektion, Bearbeitung und Graph-Visualisierung in Obsidian | ||
📜 Turn-bewusster Ingestor | 1.500-Zeichen-Sliding-Window-Chunking mit 200-Zeichen-Überlappung entlang von Turn-Grenzen | Bewahrt die gesamte Konversationshistorie, ohne Codeblöcke zu brechen | ||
🛡️ Prompt-Injection-Schutz | Neutralisiert Kontroll-Tokens (`< | im_start | > | Verhindert Memory-Poisoning und Context-Hijacking-Angriffe |
⚡ FastEmbed-ONNX-Unterstützung | Ultra-leichte ONNX-Runtime-Embedding-Engine (~30MB RAM) | 90% Speicherreduktion gegenüber schwerem PyTorch | ||
🔄 SQLite-Fallback ohne Konfiguration | Automatischer Fallback auf lokales SQLite, wenn PostgreSQL offline ist | 100% Feature-Parität für eigenständige Entwickler-Workstations |
🏗️ Systemarchitektur
┌──────────────────────────────────────────────┐
│ AI Agents & LLM Frameworks │
│ Hermes • OpenClaw • Claude • Cursor • Manus │
└──────────────────────┬───────────────────────┘
│ JSON-RPC (stdio) / Python SDK
▼
┌─────────────────────────────────────────────────────────────────────────────────────────────────────────────┐
│ Mnemosyne Core (v3.4.0) │
│ │
│ ┌─────────────────────────┐ ┌─────────────────────────┐ ┌─────────────────────────────────────────────┐ │
│ │ Admission & Security │ │ Multi-Backend Embedder │ │ Turn-Aware Session Ingestor │ │
│ │ - Secret Regex Guard │ │ - FastEmbed (ONNX 30MB) │ │ - Turn boundary splitting │ │
│ │ - Injection Neutralizer │ │ - Remote Microservice │ │ - 1500-char linear sliding window │ │
│ │ - Path Traversal Guard │ │ - SentenceTransformers │ │ - Sequential [[Wiki Link]] chaining │ │
│ └─────────────────────────┘ └─────────────────────────┘ └─────────────────────────────────────────────┘ │
│ │
│ ┌───────────────────────────────────────────────────────────────────────────────────────────────────────┐ │
│ │ Hybrid RRF Retrieval & Graph Ranker │ │
│ │ RRF Score = 1.0 * Dense(HNSW) + 0.8 * Keyword(FTS) + 0.6 * Graph + 0.2 * Salience │ │
│ └───────────────────────────────────────────────────────────────────────────────────────────────────────┘ │
└───────────────────────────────────────┬───────────────────────────────────────┬─────────────────────────────┘
│ │
┌───────────────────────┴───────────────────────┐ │
▼ ▼ ▼
┌─────────────────────────────┐ ┌─────────────────────────────────────────────┐
│ PostgreSQL + pgvector │ │ Obsidian Markdown Vault (Local) │
│ - HNSW vector_cosine_ops │ │ - Human-Readable Markdown + YAML Frontmatter │
│ - GIN tsvector English FTS │ │ - [[Wikilink]] Knowledge Graph Visualizer │
│ - Self-Healing DB Pool │ │ - Atomic Writes with EXDEV Fallback │
│ - Private DB + shared_db │ └──────────────────────┬──────────────────────┘
└─────────────────────────────┘ │ Async Background Sync
▼
┌─────────────────────────────────────────────┐
│ Google Drive Cloud │
│ - Service Account / OAuth2 Authentication │
│ - MD5 Checksum Verification │
│ - Sibling .conflict-TIMESTAMP.md Resolution │
└─────────────────────────────────────────────┘🧠 Deep Dive in die Kernfunktionen
1. Hierarchische Wing- & Room-Taxonomie
Gedächtnis-Kreuzkontamination ist ein häufiges Fehlermuster in Multi-Agent-Fleets. Mnemosyne strukturiert das Gedächtnis als 2-stufigen physischen Palast:
wing(Domäne/Projekt): Obergrenze der Domäne (z. B.ecommerce,pentest,devops,shared).room(Thema/Kanal): Granulare Themenpartition (z. B.database,firewall,stripe_api).
Abfragen können eng auf einen bestimmten Wing oder Room eingegrenzt werden, sodass Marketing-Prompts keine Penetrationstest-Ergebnisse abrufen.
2. Hybride Reciprocal Rank Fusion (RRF)-Suche
Standard-Vektorsuche versagt bei exakten technischen Zeichenketten (z. B. CVE-2024-38077, 0x7fff5fbff8c0), während Stichwortsuche bei semantischen Konzepten versagt. Mnemosyne führt einen Multi-Kandidaten-Abruf durch und führt die Ergebnisse mittels gewichteter RRF zusammen:
$$\text{RRF Score}(d) = \sum_{m \in \text{modes}} w_m \cdot \frac{1}{k + \text{rank}_m(d)} + 0.2 \cdot \text{Salience}(d)$$
Dichte HNSW-Vektorsuche: Gewicht $1.0$ (Kosinus-Distanz über 384-dimensionale Embeddings).
PostgreSQL-Volltextsuche: Gewicht $0.8$ (
tsvectorgewichtet mit Titel alsAund Inhalt alsB).Rekursive Graph-Traversierung: Gewicht $0.6$ (Rekursiver CTE, der 1-Hop- und 2-Hop-
[[Wiki Links]]entdeckt).Memory-Salience-Engine: Gewicht $0.2$ (Wichtigkeits-Score von $0.0$ bis $1.0$).
3. Agentenübergreifende gemeinsame Gedächtnisschicht (shared_db)
In autonomen Multi-Agent-Umgebungen halten Agenten isolierte private Datenbanken (toy_db, old_db, candy_db usw.) vor, um Zustandskorruption zu verhindern. Kollektive Intelligenz erfordert jedoch das Teilen globaler Richtlinien und verifizierter Fakten.
Veröffentlichung: Mit
memory_publish_sharedwerden geprüfte Notizen inshared_dbveröffentlicht.Credential-Screening: Inhalte werden mit strengen Regex-Filtern auf Anthropic-Schlüssel (
sk-ant-), Google-Gemini-Schlüssel (AIza...), HuggingFace-Tokens (hf_...), OpenAI-Schlüssel (sk-proj-...), AWS-Zugriffsschlüssel (AKIA...), Slack-Tokens (xox-) und private Schlüssel geprüft.Fail-Soft-Abruf: Wenn ein Agent das Gedächtnis abfragt, fragt
memory_recallsowohl den privaten Speicher als auchshared_dbab. Ist die gemeinsame Datenbank vorübergehend nicht erreichbar, degradiert das System elegant, ohne den Agenten zu unterbrechen.
4. Ebbinghaus-Zeitabklingung & Pinned-Immunität
Erinnerungen verlieren mit der Zeit natürlich an Relevanz. Mnemosyne implementiert Herman Ebbinghaus' exponentielle Vergessenskurve:
$$\text{Salience}(t) = \text{Salience}0 \times (0.95)^{\Delta t{\text{days}}}$$
Touch-Feedback: Der Zugriff auf eine Erinnerung aktualisiert
last_accessed_atund setzt deren Abklingung zurück.Nächtliche Auto-Archivierung: Die Konsolidierung überführt automatisch Notizen mit $\text{Salience} < 0.05$ und ohne Zugriff seit $>90\text{ Tagen}$ in
status = 'archived'.Pinned-Immunität: Systemregeln, Kernrichtlinien oder Notizen, die mit
pinned=Truemarkiert oder mit#pinnedgetaggt sind, erhalten permanente Immunität gegen Zeitabklingung ($\text{Salience} = 1.0$).
5. Obsidian Markdown Vault & bidirektionaler Graph
Jede Erinnerung, die ein Agent erstellt, wird gleichzeitig als menschenlesbare .md-Datei in Ihrem Obsidian-Vault geschrieben:
Zettelkasten-Frontmatter: Enthält
title,date,tags,type,salience,wingundroom.Wissensgraph: Zielnotizen, die als
[[Zielnotiz]]erwähnt werden, werden automatisch in bidirektionale Kanten in PostgreSQL geparst.Live-Inspektion: Öffnen Sie Obsidian auf Ihrem Desktop oder Mobilgerät und erkunden Sie das kollektive Gedächtnis Ihres Agent-Fleets in Obsidians interaktiver Graph-Ansicht.
6. Turn-bewusster Verbatim-Sitzungs-Ingestor
Konversationstranskripte enthalten oft entscheidende Nuancen, die bei verlustbehafteter Zusammenfassung verloren gehen. memory_ingest_session:
Teilt rohe Transkripte entlang von Turn-Grenzen (
User:,Assistant:,### Turn,**Human**:).Wendet für Turns über 1.500 Zeichen ein lineares Sliding Window an (1.500 Zeichen mit 200 Zeichen Überlappung).
Verkettet sequenzielle Chunks mit
[[Session ... Turn 01 Part 02]]-Wikilinks und bewahrt so Codeblöcke, Ausführungsabläufe und den Konversationsfluss.
7. Asynchrone Google-Drive-Cloud-Synchronisierung
Halten Sie Ihre Agent-Vaults sicher gesichert und über mehrere Maschinen oder Mobilgeräte hinweg synchronisiert:
Lokale Geschwindigkeit: Agent-Tool-Aufrufe werden mit lokaler SSD-Geschwindigkeit (<1ms) ausgeführt, ohne auf die Netzwerklatenz von Google Drive zu warten.
Hintergrund-Daemon / Cron-Synchronisierung: Scannt Vault-Dateien, berechnet MD5-Prüfsummen und synchronisiert Deltas bidirektional mit Google Drive.
Konfliktlösung: Wenn eine Datei gleichzeitig auf Google Drive und im lokalen Agent-Vault geändert wird, speichert Mnemosyne die eingehende Version als
NoteName.conflict-YYYYMMDD-HHMMSS.md, um Datenverlust zu verhindern.Authentifizierung: Unterstützt Google Cloud Service Account JSON (
GOOGLE_APPLICATION_CREDENTIALS,GDRIVE_SERVICE_ACCOUNT_JSON) und OAuth2-Benutzertokens (GDRIVE_TOKEN_JSON).
8. Flexible Embedding-Backends (FastEmbed / Microservice / Lokal)
Mnemosyne passt sich jedem Bereitstellungs-Ressourcenbudget an:
FastEmbed ONNX Runtime (Empfohlen für eigenständige Knoten): Verwendet ONNX Runtime C++-Ausführung (~30MB RAM). Kein PyTorch-Overhead.
Zentralisierter Microservice (
mnemosyne.embed_service): Hostet sentence-transformers in einem einzigen dedizierten Container, der mehrere Agent-Container über HTTP bedient (MEMORY_EMBED_URL).Lokale SentenceTransformers: Eigenständige PyTorch-Ausführung (
all-MiniLM-L6-v2, 384-dimensional).Deterministischer Hash-Fallback: Mathematischer Vektor-Hash mit null RAM für extrem ressourcenbeschränkte Umgebungen.
9. Defense-in-Depth-Sicherheit & Injektionsschutz
Pfad-Traversal-Immunität: Doppelt aufgelöste kanonische Pfadprüfungen (
is_relative_to) stellen sicher, dass Dateioperationen die Vault-Root nicht verlassen können.Atomare Sibling-Schreibvorgänge: Dateien werden in temporäre Sibling-Dateien (
.note.pid.tmp) geschrieben und atomar umbenannt, mit automatischem Fallback fürEXDEV-geräteübergreifende Volume-Mounts.Neutralisierung von Steuerungstokens: Neutralisiert LLM-Injektionstokens (
<|im_start|>,<|system|>,[INST],<<SYS>>) in Prosa, während Codeblöcke unverändert erhalten bleiben.Strukturierte XML-Kontextumhüllung: Abgerufene Erinnerungen werden in
<recalled_memory_context id="..." title="..." source="...">-Tags mit interner Tag-Escaping eingebettet, sodass Host-LLMs abgerufene Erinnerungen niemals mit aktiven Systemanweisungen verwechseln.
🛠️ MCP-Tool-Referenz (8 Tools)
Alle 8 Tools werden Agenten nativ über standardmäßiges MCP JSON-RPC stdio bereitgestellt:
1. memory_remember
Speichert eine private Erinnerungsnotiz im Vault mit semantischem Embedding, Tags und hierarchischer Bereichsdefinition.
{
"title": "PostgreSQL HNSW Tuning",
"content": "For datasets >10,000 vectors, use HNSW with m=16 and ef_construction=64 for optimal recall.",
"tags": ["database", "pgvector", "performance"],
"wing": "engineering",
"room": "databases",
"salience": 0.8,
"pinned": true
}2. memory_publish_shared
Veröffentlicht einen bereinigten, geprüften Befund oder eine Richtlinie im gemeinsamen agentenübergreifenden Flotten-Speicher (shared_db).
{
"title": "Fleet Security Policy: SSL Verification",
"content": "All internal agent HTTP requests must enforce SSL certificate validation.",
"tags": ["security", "policy"],
"wing": "shared",
"room": "general"
}3. memory_recall
Durchsucht Erinnerungen in privaten und gemeinsamen Flotten-Datenbanken mithilfe von hybridem RRF, HNSW-Vektoren, Schlüsselwörtern oder Graphen.
{
"query": "HNSW index configuration parameters",
"mode": "hybrid",
"top_k": 5,
"scope": {
"wing": "engineering",
"room": "databases"
},
"include_shared": true
}4. memory_ingest_session
Erfasst und chunked ein vollständiges Gesprächstranskript wörtlich entlang der Wendepunkt-Grenzen.
{
"transcript": "User: How do we configure pgvector?\nAssistant: Use CREATE EXTENSION vector; then create an HNSW index.",
"wing": "engineering",
"room": "sessions"
}5. memory_timeline
Untersucht kürzliche Speicheroperationen (remember, recall, remind, consolidate) in chronologischer Reihenfolge.
{
"limit": 20
}6. memory_history
Zeigt Versionsverlauf und vergangene Bearbeitungsschnappschüsse einer bestimmten Erinnerungsnotiz vor Aktualisierungen an.
{
"title": "PostgreSQL HNSW Tuning",
"limit": 5
}7. memory_remind_me
Plant eine zukünftige prospektive Erinnerung oder wiederkehrende Aufgabe.
{
"title": "Rotate Database Credentials",
"content": "Verify that all 5 agent connection pools are refreshed with new passwords.",
"trigger_at": "2026-09-01T00:00:00Z",
"recurring": "monthly"
}8. memory_audit
Liefert Echtzeit-Metriken zur Speichergesundheit, Store-Backend-Status, Anforderungszahlen und aktive Wings.
{}🌐 Produktionsarchitektur für Multi-Agenten-Flotten
In Multi-Agenten-Produktionsumgebungen (wie der 5-Agenten-Hermes-Flotte) isoliert Mnemosyne Agent-Datenbanken in einem internen Docker-Netzwerk und bietet gleichzeitig gemeinsame Intelligenz:
┌─────────────────────────────────────────┐
│ Production VPS (${VPS_HOST}) │
└────────────────────┬────────────────────┘
│
┌───────────────────┬──────────────────┼───────────────────┬──────────────────┐
▼ ▼ ▼ ▼ ▼
┌──────────────────┐┌──────────────────┐┌──────────────────┐┌──────────────────┐┌──────────────────┐
│ hermes-agent ││ hermes-assistant ││ hermes-marketing ││ hermes-pentest ││ hermes-trader │
│ (Toy) ││ (Old) ││ (Candy) ││ (Pencil) ││ (Coin) │
│ Database: ││ Database: ││ Database: ││ Database: ││ Database: │
│ toy_db ││ old_db ││ candy_db ││ pencil_db ││ trader_db │
└────────┬─────────┘└────────┬─────────┘└────────┬─────────┘└────────┬─────────┘└────────┬─────────┘
│ │ │ │ │
└───────────────────┴──────────────────┼───────────────────┴──────────────────┘
│
▼
┌───────────────────────────────────┐
│ PostgreSQL + pgvector (HNSW) │
│ - Private DBs: toy_db, old_db.. │
│ - Shared DB: shared_db │
└───────────────────────────────────┘🚀 Schnellstart & Installation
1. Installation
# Standard installation
pip install mnemosyne-memory
# With Google Drive Cloud Synchronization support
pip install "mnemosyne-memory[gdrive]"
# With lightweight FastEmbed ONNX support (~30MB RAM)
pip install "mnemosyne-memory[fastembed]"
# Full installation (All extras + Dev dependencies)
pip install "mnemosyne-memory[dev,gdrive,fastembed,embed-service]"2. Ausführen mit Docker Compose
Starten Sie den PostgreSQL- + pgvector-Container:
docker compose up -d3. MCP-Server starten
# Standalone with local SQLite (Zero configuration)
python -m mnemosyne server
# With PostgreSQL + pgvector
MEMORY_DB_DSN="postgresql://mnemosyne:***@localhost:5432/my_agent_db" python -m mnemosyne server🤖 Integrationsrezepte für Agent-Frameworks
1. Claude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"mnemosyne": {
"command": "python3",
"args": ["-m", "mnemosyne", "server"],
"env": {
"MEMORY_VAULT_PATH": "/Users/username/Documents/Obsidian/AgentVault",
"MEMORY_DEFAULT_WING": "claude"
}
}
}
}2. Cursor IDE (.cursor/mcp.json)
{
"mcpServers": {
"mnemosyne": {
"command": "python3",
"args": ["-m", "mnemosyne", "server"],
"env": {
"MEMORY_VAULT_PATH": "./.vault",
"MEMORY_DEFAULT_WING": "codebase"
}
}
}
}3. Hermes-Agent (~/.hermes/config.yaml)
mcp_servers:
obsidian_memory:
command: python3
args: ["-m", "mnemosyne", "server"]
env:
MEMORY_DB_DSN: "postgresql://mnemosyne:***@${DB_HOST}:5432/toy_db"
MEMORY_SHARED_DSN: "postgresql://mnemosyne:***@${DB_HOST}:5432/shared_db"
MEMORY_VAULT_PATH: "/opt/data/vault"4. OpenClaw (openclaw-config.yaml)
plugins:
mcp_servers:
mnemosyne:
command: "python3"
args: ["-m", "mnemosyne", "server"]
env:
MEMORY_VAULT_PATH: "~/.openclaw/vault"
MEMORY_DEFAULT_WING: "openclaw"5. Python-SDK & autonome Agent-Skripte
from mnemosyne import UnifiedMemorySystem
mem = UnifiedMemorySystem(
vault_path="~/.agent/vault",
dsn="postgresql://mnemosyne:***@localhost:5432/agent_db",
shared_dsn="postgresql://mnemosyne:***@localhost:5432/shared_db"
)
# Remember fact
mem.remember(
title="Kubernetes Cluster Policy",
content="Deployments in staging must specify resource memory limits.",
wing="infrastructure",
room="k8s",
tags=["kubernetes", "policy"],
pinned=True
)
# Hybrid recall
results = mem.recall(
query="staging memory limits",
mode="hybrid",
scope={"wing": "infrastructure"}
)
for r in results:
print(r["title"], "->", r["formatted_context"])💻 Vollständige CLI-Befehlsreferenz
Mnemosyne enthält eine voll ausgestattete Verwaltungs-CLI:
# 1. Store a memory note
python -m mnemosyne remember "API Architecture" "Uses Bearer JWT auth." --tags security auth --wing backend --room api --salience 0.8 --pinned
# 2. Publish shared fleet memory
python -m mnemosyne publish-shared "Global Production Policy" "Always check SSL certs." --wing devops
# 3. Search memories
python -m mnemosyne recall "JWT authentication" --mode hybrid --top-k 5 --wing backend
# 4. View activity timeline
python -m mnemosyne timeline --limit 20
# 5. Trigger Ebbinghaus decay & link reconciliation
python -m mnemosyne consolidate --decay-rate 0.95 --archive-threshold 0.05
# 6. Check system statistics & health
python -m mnemosyne stats
# 7. Synchronize with Google Drive (One-off pass)
python -m mnemosyne sync-gdrive --folder "My-Agent-Vault" --credentials service-account.json
# 8. Run Google Drive Sync as a background daemon
python -m mnemosyne sync-gdrive --daemon --interval 60
# 9. Run standalone Centralized Embedding Microservice
python -m mnemosyne embed-service --host 0.0.0.0 --port 8000 --model all-MiniLM-L6-v2⚙️ Referenz der Umgebungsvariablen
Variable | Standard | Beschreibung |
|
| Dateisystempfad zum lokalen Obsidian-Markdown-Vault-Verzeichnis |
| (keine) | PostgreSQL-DSN (z. B. |
| (keine) | PostgreSQL-DSN für die optionale agentenübergreifende gemeinsame Flotten-Datenbank |
|
| Kennung für den Ursprungsagenten in Multi-Agenten-Flottenbereitstellungen |
| (keine) | URL des entfernten zentralisierten Embedding-Microservice (z. B. |
|
| Setzen Sie |
| (keine) | Dateipfad zur Google Cloud Service Account JSON für die Google-Drive-Synchronisierung |
| (keine) | Stringifizierter JSON-Inhalt der Google Cloud Service Account-Anmeldedaten |
| (keine) | Stringifizierter JSON-Inhalt des autorisierten Google-OAuth2-Benutzertokens |
|
| Deaktiviert HuggingFace-Tokenizer-Forks, um die Integrität des stdio-JSON-RPC-Streams zu bewahren |
|
| Deaktiviert Fortschrittsbalken in stdio, um MCP-Streams makellos zu halten |
|
| Setzen Sie |
|
| Setzen Sie |
🧪 Tests & Benchmarks
Mnemosyne pflegt eine umfassende Testsuite (Unit-Tests, Sicherheits-Injektionstests, SQLite-Tests und Live-PostgreSQL-pgvector-Integrationstests):
# Run all unit tests
pytest tests/ -v -m "not integration"
# Run full test suite including live PostgreSQL + pgvector tests
MEMORY_DB_DSN="postgresql://mnemosyne:***@localhost:5432/test_db" pytest tests/ -v📄 Lizenz
Apache-2.0-Lizenz. Entwickelt für das offene Ökosystem autonomer Agenten. Siehe LICENSE für vollständige Details.
Available Tools
9 toolsmemory_assemble_contextA
Retrieve and format high-salience memories into a strict token-budgeted XML prompt block ready for direct agent context injection.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | hybrid | |
| query | Yes | Search query to retrieve context for | |
| scope | No | Optional wing/room filter scope | |
| max_tokens | No | Maximum token budget for the assembled context | |
| include_shared | No | Whether to include shared fleet knowledge |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses that the tool retrieves rather than mutates, filters for 'high-salience', enforces a 'strict token-budget', and formats into XML. This is useful, though it doesn't explain how salience is determined or how failures/token-truncation are handled.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single dense, front-loaded sentence with no filler. It names the primary action, the input type, the processing behavior, and the output format in an efficient structure.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The description covers the key output (XML prompt block) but leaves important gaps for a tool with five parameters and no annotations: the meaning of each `mode`, how `scope` is applied, the effect of `include_shared`, and expected behavior when the token budget is exceeded. The schema fills some gaps, but the description alone isn't fully complete for correct invocation.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 80%, so the schema already documents most parameter meanings. The description adds some context around token budgeting and salience, but it doesn't elaborate on the `mode` enum, `scope` object, or `include_shared` flag beyond what the schema fields already say. This is adequate but not additive.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('Retrieve and format') and resource ('high-salience memories'), and specifies the unique output format: 'strict token-budgeted XML prompt block ready for direct agent context injection.' It doesn't explicitly name sibling alternatives, but the XML formatting and context-injection purpose distinguish it from tools like memory_recall or memory_timeline.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The intended use is implied: assemble memory context for direct agent injection. It does not name when to use this over alternatives, nor provide any when-not-to-use guidance. The phrase 'ready for direct agent context injection' gives some context, but an agent would need to infer the comparison with memory_recall.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_auditA
Get memory system statistics, health check, active wings, and version info
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden of behavioral disclosure. The verb 'Get' strongly implies a read-only operation, and the listed outputs suggest no mutation. However, it does not explicitly state that no state changes occur, whether permissions are required, or whether the health check performs any active diagnostics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, compact sentence with no filler. The verb is front-loaded and each listed item contributes meaning. Every word earns its place, and the structure is easy for an agent to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With zero parameters and no output schema, the description needs to convey what the tool returns. It does so by listing four output categories: statistics, health check, active wings, and version info. This is adequate for simple invocation, though the term 'active wings' is domain-specific and not expanded.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters, so the schema provides no parameter documentation. Per the baseline for a zero-parameter tool, the description does not need to compensate for parameter gaps; it simply describes the operation's outputs, which is sufficient.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description opens with the specific verb 'Get' and names distinct resources: memory system statistics, health check, active wings, and version info. This makes the tool's focus clear and differentiates it from content-oriented siblings like memory_recall or memory_history, though it does not explicitly contrast itself with them.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no guidance on when to choose memory_audit over sibling tools. While the system-level subject matter implies a diagnostic use case, the description does not explicitly state when to run an audit versus when to use memory_history, memory_timeline, or memory_assemble_context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_historyB
View version history of a specific memory note (previous edits).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| title | Yes | The title of the note to check history for |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
There are no annotations, so the description carries the full burden of behavioral disclosure. It only indicates a read-style operation ('View') and says nothing about the shape of the returned history, ordering, pagination, or how 'previous edits' are represented.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. It is easy to parse and economical, though it may be too terse to convey all necessary behavioral and parameter guidance.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read tool, the description names the resource and indicates the action, and the required title parameter is sufficient to invoke it. However, with no output schema and no annotations, the lack of return-format and limit semantics leaves clear gaps for an agent trying to use the results correctly.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The title parameter is already documented in the schema, but the limit parameter has no description and the tool description adds no meaning for it. With 50% schema coverage, the description needed to compensate for the undocumented parameter and did not.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('View') and a precise resource ('version history of a specific memory note'), with the clarifying parenthetical 'previous edits'. It is immediately clear what the tool does, but it does not explicitly contrast with sibling tools such as memory_timeline or memory_audit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description conveys a clear use case: checking the previous edits of one specific note. It gives enough context for when an agent should invoke it, though it provides no explicit exclusions or alternative routing to sibling tools.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_ingest_sessionA
Ingest a full conversation transcript verbatim into memory, split along conversational turn boundaries.
| Name | Required | Description | Default |
|---|---|---|---|
| room | No | sessions | |
| wing | No | general | |
| transcript | Yes | The full conversation text to ingest |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full behavioral disclosure burden. It usefully disloses that content is stored verbatim and split by turn boundaries, but it does not explain side effects, storage semantics, deduplication, limits, or whether repeated ingestion creates duplicates. These would be valuable for a memory-mutating tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, no filler, and the most decision-relevant information is front-loaded. The phrase about splitting along turn boundaries is efficiently included without bloating the description.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple ingestion tool, the core invocation path is clear: provide the full transcript. However, no output schema or annotations exist, and the optional room/wing parameters are unexplained, so an agent would need to infer their purpose from names or defaults. This leaves some contextual ambiguity around storage targeting.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is only 33%, with only 'transcript' having a schema description. The description adds meaning to the requied parameter by specifying 'verbatim' and 'split along conversational turn boundaries', but it does not explain the optional 'room' and 'wing' parameters, which remain defined only by their names and defaults.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly identifies the action ('Ingest'), the resource ('full conversation transcript'), the destination ('into memory'), and the precise behavior ('verbatim, split along conversational turn boundaries'). This distinguishes it from sibling tools like memory_remember or memory_recall, which likely handle individual memories rather than bulk ingestion.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies this is for full multi-turn conversation transcripts, which suggests a bulk ingest use case distinct from memory_remember or memory_recall. However, it does not explicitly state when not to use it or name alternative tools, leaving any routing decision to inference.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_recallB
Search memory by semantic meaning, keywords, or graph. Retrieves from both private and shared stores.
| Name | Required | Description | Default |
|---|---|---|---|
| mode | No | ||
| query | Yes | ||
| scope | No | Restrict search to a wing and/or room | |
| top_k | No | ||
| include_shared | No | Whether to include cross-agent shared fleet memory |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral burden. It discloses that the tool retrieves from both private and shared stores and that search can be semantic, keyword-based, or graph-based. However, it does not mention return format, whether any filtering or ranking applies, or any access caveats.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single concise sentence with no filler. The primary action and retrieval scope are front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given 5 parameters, a nested scope object, no output schema, no annotations, and several related sibling tools, this description is too sparse. It leaves important invocation details such as mode defaults, query semantics, shared-memory handling, and result expectations unexplained.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is only 40%, and the description compensates poorly. It loosely maps to the mode enum with 'semantic meaning, keywords, or graph' and mentions shared/private stores, but it does not clarify the required query parameter, the meaning of hybrid mode, top_k behavior, or the nested scope object.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb and resource: 'Search memory' and lists search methods (semantic, keyword, graph). It is clear about what the tool does, though it does not explicitly differentiate itself from sibling tools like memory_timeline or memory_history.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies the tool is for searching/recalling stored memories, but it gives no explicit guidance on when to prefer memory_recall over alternatives like memory_assemble_context, memory_timeline, or memory_remind_me. Usage context is inferred rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_rememberA
Store a private memory note in the vault with semantic embedding, tags, and hierarchical wing/room scope.
| Name | Required | Description | Default |
|---|---|---|---|
| room | No | Topic within the wing (e.g. woocommerce, firewall, docker) | general |
| tags | No | Categorical tags | |
| wing | No | Project/domain grouping (e.g. ecommerce, security, devops) | general |
| title | Yes | Short, unique title for the memory | |
| pinned | No | Set true to make permanently immune to Ebbinghaus temporal decay | |
| content | Yes | The memory content in markdown | |
| salience | No | Importance score 0.0-1.0 (default 0.5) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden and it does add useful behavioral context: 'private,' 'vault,' 'semantic embedding,' and hierarchy. It does not disclose behaviors such as duplicate-title handling, temporal decay, or return value, which leaves some opacity for a state-changing tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
One sentence, front-loaded with the verb, and every detail—privacy, vault, embedding, tags, scope—earns its place. There is no filler or redundancy.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Schema plus description covers what to pass, but the absence of annotations and output schema, combined with seven sibling tools, leaves gaps: no routing guidance versus ingestion/sharing tools and no statement of what happens on success or when a title already exists. Complete enough for basic invocation but not fully self-sufficient.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% and each parameter already has meaningful descriptions (e.g., room is 'Topic within the wing'; pinned explains Ebbinghaus decay). The description adds only the notion of hierarchy, not substantive parameter semantics, so the baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific action ('Store') and a specific resource ('private memory note in the vault') while naming key processing details: semantic embedding, tags, and hierarchical wing/room scope. 'Private' distinguishes it from shared publication, and the write verb separates it from retrieval siblings like memory_recall.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Usage is implied by the store verb: use this when saving a private memory note. However, the description provides no explicit when-to-use or when-not-to-use guidance and names no alternatives, so an agent must infer the boundary against siblings like memory_ingest_session and memory_publish_shared.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_remind_meB
Schedule a future reminder or recurring task
| Name | Required | Description | Default |
|---|---|---|---|
| title | Yes | ||
| content | No | ||
| recurring | No | ||
| trigger_at | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Because no annotations are provided, the description carries the full burden of behavioral disclosure. It only states the basic scheduling action and omits important traits such as trigger_at format, how recurrence is applied, side effects when the reminder fires, and whether a memory entry is also created.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single efficient, front-loaded sentence with no wasted words. However, its brevity comes at the cost of necessary parameter and behavior details, so it is concise but not fully appropriately sized.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with four parameters, no annotations, schema description coverage of 0%, and no output schema, this description is too thin. An agent cannot confidently determine how to provide trigger_at or how recurring should behave, so the definition is materially incomplete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate, but it names no parameters directly. It only hints at 'future' and 'recurring', leaving title, content, trigger_at format, and the relationship between recurring and trigger_at undocumented.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Schedule') and identifies the resource ('future reminder or recurring task'). This clearly distinguishes it from retrospective siblings like memory_recall, memory_timeline, and memory_history, and from memory_remember which implies storage rather than scheduling.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The phrase 'Schedule a future reminder or recurring task' implies the core use case, but no explicit guidance is given about when to choose this over alternatives. There are no prerequisites, no exclusions, and no mention of sibling tools that might be more appropriate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
memory_timelineB
View recent memory operations (remember, recall, remind, consolidate) as a chronological timeline.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description bears the burden of behavioral disclosure. 'View' and 'chronological timeline' imply a read-only listing, which is helpful, but no details are given about ordering direction, result format, pagination, or whether any side effects occur.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, well-structured sentence that front-loads the main action and resource. Every word contributes value, and it includes useful examples of the operation types shown.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple read-only timeline with one optional parameter, the description is mostly adequate. However, the lack of sibling differentiation, no mention of the `limit` parameter, and no output format details leave clear gaps for an agent deciding how to invoke it.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The single parameter `limit` has zero schema description coverage and the tool description does not mention it at all. While the parameter is somewhat self-explanatory from its name and default value, the description adds no meaning beyond the schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb ('View') and a clear resource ('recent memory operations') with examples of operation types. It is sufficiently clear that this is a timeline of memory operations, though it does not explicitly distinguish itself from the similarly named sibling 'memory_history'.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus siblings like `memory_recall` or `memory_history`, nor does it mention any alternative conditions. Usage context is only implied by the word 'View'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
9 tool updates
v3.5.0- First observed
memory_assemble_context - First observed
memory_audit - First observed
memory_history - First observed
memory_ingest_session - First observed
memory_publish_shared - First observed
memory_recall - First observed
memory_remember - First observed
memory_remind_me - First observed
memory_timeline
TDQS
Scored across 9 tools
Most tools target distinct memory operations: storing, retrieving, scheduling, auditing, and publishing. Some overlap exists between memory_recall (search) and memory_assemble_context (curated retrieval), but their descriptions make the intended use clear.
All tool names follow a consistent memory_verb_noun pattern using snake_case (e.g., memory_ingest_session, memory_publish_shared). The one slight deviation is memory_remind_me, but it still fits the overall verb-first convention.
Nine tools cover the core memory management lifecycle without feeling bloated. The set is well-scoped for a memory server, with each tool serving a clear and necessary function.
The server covers storing, retrieving, scheduling, sharing, and diagnostics, but lacks explicit update or delete/forget tools. Memory_history implies prior edits exist, yet no edit tool is exposed, which is a notable gap for a memory management system.
Maintenance
Related MCP Connectors
Persistent memory, knowledge graph, task management and more for AI agents via MCP.
Persistent, portable memory for AI assistants — your private memory graph, from any MCP client.
Persistent memory for AI agents across Claude, ChatGPT and any MCP client.
- memnodeOAuthdev.memnode
Persistent, inspectable memory for AI agents with lineage, correction, and a hosted MCP endpoint.
Related MCP Servers
- AlicenseNot gradedqualityDmaintenanceGives AI agents persistent memory with semantic search, automatic extraction, and memory decay, accessible via MCP protocol.5 npmMIT
- AlicenseNot gradedqualityAmaintenanceProvides persistent, searchable memory for MCP-compatible agents, enabling recall by meaning, automatic decay, trust scoring, and cross-agent handoffs.21 PyPI5MIT
- AlicenseAqualityDmaintenanceProvides persistent memory with semantic search for MCP-based AI agents, enabling them to store and recall information across sessions using vector embeddings.41MIT
- AlicenseNot gradedqualityAmaintenanceProvides persistent, graph-based memory for AI agents using Neo4j and vector embeddings, enabling semantic recall across sessions via MCP.1Apache 2.0