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# Mnemosyne 🧠
[](https://github.com/M4F-S/mnemosyne/actions/workflows/ci.yml)
[](https://pypi.org/project/mnemosyne-memory/)
[](https://www.python.org/downloads/)
[](https://modelcontextprotocol.io/)
[](https://opensource.org/licenses/Apache-2.0)
**Production-grade, local-first hierarchical memory engine for autonomous AI agents.**
Mnemosyne equips AI agents (Hermes, OpenClaw, OpenManus, Claude Desktop, Cursor, Windsurf, CrewAI, LangChain) with permanent, structured long-term memory. It bridges human-readable **Obsidian Markdown Vaults** with high-speed **PostgreSQL + pgvector (HNSW)** or zero-config **SQLite**, powering hybrid Reciprocal Rank Fusion (RRF) search, wikilink knowledge graphs, Ebbinghaus temporal decay, cross-agent fleet sharing, and asynchronous Google Drive cloud synchronization.
---
## 📑 Table of Contents
- [⚡ Complete Feature Matrix](#-complete-feature-matrix)
- [🏗️ System Architecture](#️-system-architecture)
- [🧠 Deep Dive into Key Capabilities](#-deep-dive-into-key-capabilities)
- [1. Hierarchical Wing & Room Taxonomy](#1-hierarchical-wing--room-taxonomy)
- [2. Hybrid Reciprocal Rank Fusion (RRF) Search](#2-hybrid-reciprocal-rank-fusion-rrf-search)
- [3. Cross-Agent Shared Memory Layer (`shared_db`)](#3-cross-agent-shared-memory-layer-shared_db)
- [4. Ebbinghaus Temporal Decay & Pinned Immunity](#4-ebbinghaus-temporal-decay--pinned-immunity)
- [5. Obsidian Markdown Vault & Bi-Directional Graph](#5-obsidian-markdown-vault--bi-directional-graph)
- [6. Turn-Aware Verbatim Session Ingestor](#6-turn-aware-verbatim-session-ingestor)
- [7. Asynchronous Google Drive Cloud Synchronization](#7-asynchronous-google-drive-cloud-synchronization)
- [8. Flexible Embedding Backends (FastEmbed / Microservice / Local)](#8-flexible-embedding-backends-fastembed--microservice--local)
- [9. Defense-in-Depth Security & Injection Armor](#9-defense-in-depth-security--injection-armor)
- [🛠️ MCP Tool Reference (8 Tools)](#️-mcp-tool-reference-8-tools)
- [🌐 Multi-Agent Fleet Production Architecture](#-multi-agent-fleet-production-architecture)
- [🚀 Quick Start & Installation](#-quick-start--installation)
- [🤖 Agent Framework Integration Recipes](#-agent-framework-integration-recipes)
- [💻 Complete CLI Command Reference](#-complete-cli-command-reference)
- [⚙️ Environment Variables Reference](#️-environment-variables-reference)
- [🧪 Testing & Benchmarks](#-testing--benchmarks)
- [📄 License](#-license)
---
## ⚡ Complete Feature Matrix
| Feature | Description | Benefit |
|---|---|---|
| **🤖 MCP Native (v2024-11-05)** | Standardized stdio JSON-RPC protocol server | Seamless drop-in for Claude, Cursor, Windsurf, Hermes, OpenClaw |
| **🔎 High-Recall HNSW Vector Search** | `pgvector` HNSW indexing with `vector_cosine_ops` (`m=16, ef_construction=64`) | Sub-millisecond vector recall without clustering retraining |
| **⚖️ Hybrid RRF Retrieval** | Reciprocal Rank Fusion of Dense Embeddings (1.0) + GIN FTS (0.8) + Graph (0.6) + Salience (0.2) | Captures exact technical keywords (CVEs, code tokens) & fuzzy semantics |
| **🏛️ Wing & Room Scoping** | 2-level taxonomy (`wing` = domain/project, `room` = channel/topic) | Eliminates context window bloating & cross-domain hallucination |
| **🌐 Cross-Agent Shared Memory** | Central `shared_db` queryable across multi-agent fleets with credential screening | Collective fleet intelligence without compromising private databases |
| **☁️ Async Google Drive Sync** | Local-first bidirectional sync engine with MD5 diffing and `.conflict.md` branch resolution | Sub-millisecond agent I/O locally + automatic cloud backup & team sharing |
| **⏳ Ebbinghaus Temporal Decay** | Exponential decay $Salience_t = Salience_0 \times (0.95)^{\Delta t}$ with 90-day auto-archive | Auto-prunes transient noise while keeping active memories sharp |
| **📌 Pinned Memory Immunity** | Permanent immunity to decay via `pinned=True` or `#pinned` tags | Guarantees foundational instructions and core rules never fade |
| **📖 Obsidian Zettelkasten** | Writes human-readable Markdown notes with YAML frontmatter & `[[Wiki Links]]` | Direct visual inspection, editing, and graph visualization in Obsidian |
| **📜 Turn-Aware Ingestor** | 1,500-char sliding-window chunking with 200-char overlap along turn boundaries | Preserves entire conversation history without breaking code blocks |
| **🛡️ Prompt Injection Armor** | Neutralizes control tokens (`<|im_start|>`, `[INST]`) in prose; escapes XML context tags | Prevents memory poisoning and context hijacking attacks |
| **⚡ FastEmbed ONNX Support** | Ultra-lightweight ONNX runtime embedding engine (~30MB RAM) | 90% memory reduction vs heavy PyTorch `sentence-transformers` |
| **🔄 Zero-Config SQLite Fallback** | Automatic fallback to local SQLite when PostgreSQL is offline | 100% feature parity for standalone developer workstations |
---
## 🏗️ System Architecture
```
┌──────────────────────────────────────────────┐
│ 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 into Key Capabilities
### 1. Hierarchical Wing & Room Taxonomy
Memory cross-contamination is a major failure mode in multi-agent fleets. Mnemosyne structures memory as a 2-level physical palace:
* **`wing` (Domain/Project):** Top-level domain boundary (e.g. `ecommerce`, `pentest`, `devops`, `shared`).
* **`room` (Topic/Channel):** Granular topic partition (e.g. `database`, `firewall`, `stripe_api`).
Queries can be scoped tightly to a specific wing or room, preventing marketing prompts from recalling penetration testing findings.
### 2. Hybrid Reciprocal Rank Fusion (RRF) Search
Standard vector search fails on exact technical strings (e.g. `CVE-2024-38077`, `0x7fff5fbff8c0`), while keyword search fails on semantic concepts. Mnemosyne executes multi-candidate retrieval and merges results using weighted RRF:
$$\text{RRF Score}(d) = \sum_{m \in \text{modes}} w_m \cdot \frac{1}{k + \text{rank}_m(d)} + 0.2 \cdot \text{Salience}(d)$$
* **Dense HNSW Vector Search:** Weight $1.0$ (Cosine distance over 384-dimensional embeddings).
* **PostgreSQL Full-Text Search:** Weight $0.8$ (`tsvector` weighted with title as `A` and content as `B`).
* **Recursive Graph Traversal:** Weight $0.6$ (Recursive CTE discovering 1-hop and 2-hop `[[Wiki Links]]`).
* **Memory Salience Engine:** Weight $0.2$ (Importance score from $0.0$ to $1.0$).
### 3. Cross-Agent Shared Memory Layer (`shared_db`)
In autonomous multi-agent environments, agents maintain isolated private databases (`toy_db`, `old_db`, `candy_db`, etc.) to prevent state corruption. However, collective intelligence requires sharing global policies and verified facts.
* **Publishing:** Using `memory_publish_shared`, vetted notes are published to `shared_db`.
* **Credential Screening:** Content is scanned against strict regex filters for Anthropic keys (`sk-ant-`), Google Gemini keys (`AIza...`), HuggingFace tokens (`hf_...`), OpenAI keys (`sk-proj-...`), AWS access keys (`AKIA...`), Slack tokens (`xox-`), and private keys.
* **Fail-Soft Recall:** When an agent queries memory, `memory_recall` queries both the private store and `shared_db`. If the shared database is temporarily unreachable, it degrades gracefully without interrupting the agent.
### 4. Ebbinghaus Temporal Decay & Pinned Immunity
Memories naturally lose relevance over time. Mnemosyne implements Herman Ebbinghaus's exponential forgetting curve:
$$\text{Salience}(t) = \text{Salience}_0 \times (0.95)^{\Delta t_{\text{days}}}$$
* **Touch Feedback:** Accessing a memory updates `last_accessed_at`, resetting its decay.
* **Nightly Auto-Archiving:** Consolidation automatically transitions notes with $\text{Salience} < 0.05$ and unaccessed for $>90\text{ days}$ to `status = 'archived'`.
* **Pinned Immunity:** System rules, core policies, or notes marked with `pinned=True` or tagged `#pinned` receive permanent immunity from temporal decay ($\text{Salience} = 1.0$).
### 5. Obsidian Markdown Vault & Bi-Directional Graph
Every memory created by an agent is simultaneously written as a human-readable `.md` file inside your Obsidian vault:
* **Zettelkasten Frontmatter:** Contains `title`, `date`, `tags`, `type`, `salience`, `wing`, and `room`.
* **Knowledge Graph:** Target notes mentioned as `[[Target Note]]` are automatically parsed into bi-directional edges in PostgreSQL.
* **Live Inspection:** Open Obsidian on your desktop or mobile device and explore your agent fleet's collective memory in Obsidian's interactive Graph View.
### 6. Turn-Aware Verbatim Session Ingestor
Conversational transcripts often contain crucial nuances lost in lossy summarization. `memory_ingest_session`:
* Splits raw transcripts along turn boundaries (`User:`, `Assistant:`, `### Turn`, `**Human**:`).
* For turns longer than 1,500 characters, applies a **linear sliding window** (1,500 chars with 200-char overlap).
* Chains sequential chunks using `[[Session ... Turn 01 Part 02]]` wikilinks, preserving code blocks, execution traces, and conversational flow.
### 7. Asynchronous Google Drive Cloud Synchronization
Keep your agent vaults securely backed up and synchronized across multiple machines or mobile devices:
* **Local-First Speed:** Agent tool calls execute at local SSD speeds (<1ms) without blocking on Google Drive network latency.
* **Background Daemon / Cron Sync:** Scans vault files, computes MD5 checksums, and synchronizes deltas bidirectionally with Google Drive.
* **Conflict Resolution:** If a file is modified on both Google Drive and the local agent vault simultaneously, Mnemosyne saves the incoming version as `NoteName.conflict-YYYYMMDD-HHMMSS.md`, preventing data loss.
* **Authentication:** Supports Google Cloud Service Account JSON (`GOOGLE_APPLICATION_CREDENTIALS`, `GDRIVE_SERVICE_ACCOUNT_JSON`) and OAuth2 user tokens (`GDRIVE_TOKEN_JSON`).
### 8. Flexible Embedding Backends (FastEmbed / Microservice / Local)
Mnemosyne adapts to any deployment resource budget:
1. **FastEmbed ONNX Runtime (Recommended for Standalone Nodes):** Uses ONNX Runtime C++ execution (~30MB RAM). Zero PyTorch overhead.
2. **Centralized Microservice (`mnemosyne.embed_service`):** Hosts sentence-transformers in a single dedicated container serving multiple agent containers over HTTP (`MEMORY_EMBED_URL`).
3. **Local SentenceTransformers:** Standalone PyTorch execution (`all-MiniLM-L6-v2`, 384-dimensional).
4. **Deterministic Hash Fallback:** Zero-RAM mathematical vector hash for ultra-constrained environments.
### 9. Defense-in-Depth Security & Injection Armor
* **Path Traversal Immunity:** Dual-resolved canonical path checks (`is_relative_to`) ensure file operations cannot escape the vault root.
* **Atomic Sibling Writes:** Files are written to sibling temporary files (`.note.pid.tmp`) and renamed atomically, with automatic fallback for `EXDEV` cross-device volume mounts.
* **Control Token Neutralization:** Neutralizes LLM injection tokens (`<|im_start|>`, `<|system|>`, `[INST]`, `<<SYS>>`) in prose while preserving code blocks verbatim.
* **Structured XML Context Enclosure:** Recalled memories are wrapped in `<recalled_memory_context id="..." title="..." source="...">` tags with internal tag escaping, ensuring host LLMs never confuse recalled memories with active system directives.
---
## 🛠️ MCP Tool Reference (8 Tools)
All 8 tools are natively exposed to agents over standard MCP JSON-RPC stdio:
### 1. `memory_remember`
Store a private memory note in the vault with semantic embedding, tags, and hierarchical scoping.
```json
{
"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`
Publish a sanitized, vetted finding or policy to the cross-agent shared fleet memory (`shared_db`).
```json
{
"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`
Search memories across private and shared fleet databases using hybrid RRF, HNSW vectors, keywords, or graph.
```json
{
"query": "HNSW index configuration parameters",
"mode": "hybrid",
"top_k": 5,
"scope": {
"wing": "engineering",
"room": "databases"
},
"include_shared": true
}
```
### 4. `memory_ingest_session`
Ingest and chunk a complete conversation transcript verbatim along turn boundaries.
```json
{
"transcript": "User: How do we configure pgvector?\nAssistant: Use CREATE EXTENSION vector; then create an HNSW index.",
"wing": "engineering",
"room": "sessions"
}
```
### 5. `memory_timeline`
Inspect recent memory operations (remember, recall, remind, consolidate) in chronological order.
```json
{
"limit": 20
}
```
### 6. `memory_history`
View version history and past edit snapshots of a specific memory note before updates.
```json
{
"title": "PostgreSQL HNSW Tuning",
"limit": 5
}
```
### 7. `memory_remind_me`
Schedule a future prospective reminder or recurring task.
```json
{
"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`
Get real-time memory health metrics, store backend status, request counts, and active wings.
```json
{}
```
---
## 🌐 Multi-Agent Fleet Production Architecture
In multi-agent production setups (such as the 5-agent Hermes fleet), Mnemosyne isolates agent databases on an internal Docker network while providing shared intelligence:
```
┌─────────────────────────────────────────┐
│ 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 │
└───────────────────────────────────┘
```
---
## 🚀 Quick Start & Installation
### 1. Installation
```bash
# 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. Run with Docker Compose
Start the PostgreSQL + `pgvector` container:
```bash
docker compose up -d
```
### 3. Launch MCP Server
```bash
# 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
```
---
## 🤖 Agent Framework Integration Recipes
### 1. Claude Desktop (`claude_desktop_config.json`)
```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`)
```json
{
"mcpServers": {
"mnemosyne": {
"command": "python3",
"args": ["-m", "mnemosyne", "server"],
"env": {
"MEMORY_VAULT_PATH": "./.vault",
"MEMORY_DEFAULT_WING": "codebase"
}
}
}
}
```
### 3. Hermes Agent (`~/.hermes/config.yaml`)
```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`)
```yaml
plugins:
mcp_servers:
mnemosyne:
command: "python3"
args: ["-m", "mnemosyne", "server"]
env:
MEMORY_VAULT_PATH: "~/.openclaw/vault"
MEMORY_DEFAULT_WING: "openclaw"
```
### 5. Python SDK & Autonomous Agent Scripts
```python
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"])
```
---
## 💻 Complete CLI Command Reference
Mnemosyne includes a full-featured management CLI:
```bash
# 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
```
---
## ⚙️ Environment Variables Reference
| Variable | Default | Description |
|---|---|---|
| `MEMORY_VAULT_PATH` | `~/.mnemosyne/vault` | Filesystem path to the local Obsidian Markdown vault directory |
| `MEMORY_DB_DSN` | *(none)* | PostgreSQL DSN (e.g. `postgresql://user:pass@host:5432/db`). If unset, uses SQLite |
| `MEMORY_SHARED_DSN` | *(none)* | PostgreSQL DSN for the optional cross-agent shared fleet database |
| `MEMORY_AGENT_NAME` | `local-agent` | Identifier for the origin agent in multi-agent fleet deployments |
| `MEMORY_EMBED_URL` | *(none)* | URL of remote centralized embedding microservice (e.g. `http://localhost:8000`) |
| `MEMORY_REQUIRE_POSTGRES` | `false` | Set `true` to raise an error instead of falling back to SQLite if PostgreSQL fails |
| `GOOGLE_APPLICATION_CREDENTIALS` | *(none)* | File path to Google Cloud Service Account JSON for Google Drive synchronization |
| `GDRIVE_SERVICE_ACCOUNT_JSON` | *(none)* | Stringified JSON content of Google Cloud Service Account credentials |
| `GDRIVE_TOKEN_JSON` | *(none)* | Stringified JSON content of authorized Google OAuth2 user token |
| `TOKENIZERS_PARALLELISM` | `false` | Disables HuggingFace tokenizer forks to preserve stdio JSON-RPC stream integrity |
| `HF_HUB_DISABLE_PROGRESS_BARS` | `1` | Disables progress bars in stdio to keep MCP streams pristine |
| `HF_HUB_OFFLINE` | `0` | Set `1` to run SentenceTransformers 100% offline using local cache |
| `TRANSFORMERS_OFFLINE` | `0` | Set `1` to prevent transformers from making external HuggingFace network requests |
---
## 🧪 Testing & Benchmarks
Mnemosyne maintains a comprehensive test suite (unit tests, security injection tests, SQLite tests, and live PostgreSQL pgvector integration tests):
```bash
# 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
```
---
## 📄 License
Apache-2.0 License. Built for the open autonomous agent ecosystem. See [LICENSE](LICENSE) for full details.
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