Which integrations are available for this server?

Provides integration with n8n workflow automation through the Hermes bridge, enabling memory access from various platforms like Slack. Enables two-way synchronization of memories with an Obsidian vault, allowing management of memories as Markdown notes. Allows agents or users to store and retrieve memories via Slack through the Hermes bridge and n8n workflow. Allows agents or users to store and retrieve memories via a Telegram bot using the Hermes bridge.

How do I use agent-memory?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@agent-memory remember that the API rate limit is 1000 requests per minute" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

agent-memory

by khaled1174

Overview Schema Related Servers Score Discussions

Python

Local

Agent Memory System

Python License: MIT Tests Zero Dependencies MCP Tools

Persistent memory for AI agents using SQLite + FTS5.
Single-file core · Zero dependencies · Optional MCP server and Obsidian sync.

✨ Features

Feature	Description
🗄️ Persistent	SQLite database survives process restarts
🔍 Full-text search	FTS5 engine with ranked results
🔒 Agent isolation	Each agent gets its own namespace automatically
🛠️ MCP Server	12 tools for Claude Code / Claude Desktop
📝 Obsidian sync	Optional two-way Markdown bridge
🤖 CLAUDE.md generator	Auto-inject context into every session
🌍 Cross-platform	macOS, Linux, WSL, Windows
⚡ Zero dependencies	Core uses only Python standard library

🏗️ Architecture

graph TB
    subgraph Clients["🖥️ Clients"]
        CLI["💻 CLI\n(memory.py)"]
        MCP["🔌 MCP Server\n(memory_mcp.py)"]
        HERMES["🌉 Hermes Bridge\n(hermes/bridge.py)"]
    end

    subgraph Core["⚙️ Core Engine"]
        STORE["📥 Store\nmemory_store()"]
        SEARCH["🔍 Search\nFTS5 Engine"]
        ISO["🔒 Isolation\nNamespace Handler"]
        CTX["📋 Context\nCLAUDE.md Gen"]
    end

    subgraph Storage["💾 Storage"]
        DB[("🗄️ SQLite DB\nmemory.db")]
        FTS[("📑 FTS5 Index")]
    end

    subgraph External["🔗 Integrations"]
        OBS["📓 Obsidian Vault\n(Markdown)"]
        N8N["⚙️ n8n Workflow"]
        TG["📱 Telegram Bot"]
    end

    CLI --> STORE
    CLI --> SEARCH
    MCP --> STORE
    MCP --> SEARCH
    HERMES --> STORE
    HERMES --> SEARCH
    STORE --> ISO
    SEARCH --> ISO
    ISO --> DB
    DB --> FTS
    FTS --> SEARCH
    STORE --> OBS
    OBS --> STORE
    N8N --> HERMES
    TG --> HERMES
    CTX --> DB

    style Clients fill:#1f3558,stroke:#58a6ff,color:#cdd9e5
    style Core fill:#2d1f58,stroke:#bc8cff,color:#cdd9e5
    style Storage fill:#1f3a2d,stroke:#3fb950,color:#cdd9e5
    style External fill:#3a2d1f,stroke:#d29922,color:#cdd9e5

🔄 Memory Lifecycle

sequenceDiagram
    actor Agent as 🤖 AI Agent
    participant CLI as 💻 CLI / MCP
    participant ISO as 🔒 Namespace
    participant DB as 🗄️ SQLite
    participant FTS as 🔍 FTS5 Index
    participant OBS as 📓 Obsidian

    Agent->>CLI: memory store --type semantic --content "..."
    CLI->>ISO: Resolve agent namespace (AGENT_NAME)
    ISO-->>CLI: agent_id = "health" (or global)
    CLI->>DB: INSERT INTO memories (agent_id, type, content, ...)
    DB->>FTS: Auto-index content
    DB-->>CLI: memory_id = 42
    CLI-->>Agent: ✅ Stored (id=42)

    Agent->>CLI: memory search --query "rate limit"
    CLI->>ISO: Resolve agent namespace
    ISO-->>CLI: agent_id
    CLI->>FTS: SELECT * FROM memories_fts WHERE content MATCH "rate limit"
    FTS-->>CLI: Ranked results
    CLI-->>Agent: 📋 Results (ranked by relevance)

    Note over DB,OBS: Optional Obsidian Sync
    DB->>OBS: Sync to Markdown vault
    OBS->>DB: Sync from Markdown vault

🧠 Memory Types

mindmap
  root((🧠 Memory Types))
    📅 episodic
      Events and decisions
      Switched to Postgres
      Deployed v2 on Monday
    📚 semantic
      Facts and knowledge
      Rate limit 1000 req/min
      Max upload 50 MB
    ⚙️ procedural
      Workflows and how-tos
      Steps build test deploy
      Auth flow OAuth2 JWT

🚀 Quick Start

Option 1: CLI (zero dependencies)

cp memory.py /usr/local/bin/memory
chmod +x /usr/local/bin/memory

# Store a memory
memory store --type semantic --content "Max upload: 50 MB" --project api --importance 5

# Search memories
memory search --query "upload"

# List recent
memory list --project api --recent 10

# View stats
memory stats

Option 2: MCP Server (Claude Code / Claude Desktop)

pip install mcp

Add to ~/.claude/settings.json (Claude Code):

{
  "mcpServers": {
    "agent-memory": {
      "command": "python",
      "args": ["/path/to/memory_mcp.py"]
    }
  }
}

Claude Desktop paths:
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json

Restart and you'll see 12 tools available.

🛠️ MCP Tools

graph LR
    subgraph Session["📋 Session"]
        T1["memory_brief\nLoad context"]
        T2["memory_who\nAgent identity"]
        T3["memory_stats\nDB statistics"]
    end

    subgraph CRUD["✏️ CRUD"]
        T4["memory_store\nSave memory"]
        T5["memory_update\nPatch memory"]
        T6["memory_delete\nRemove by ID"]
    end

    subgraph Query["🔍 Query & Export"]
        T7["memory_search\nFTS5 full-text"]
        T8["memory_list\nFilter and sort"]
        T9["memory_export\nExport to JSON"]
        T10["memory_generate_context\nBuild CLAUDE.md"]
    end

    subgraph Sync["🔄 Sync"]
        T11["memory_sync_to_vault\nSQLite to Obsidian"]
        T12["memory_sync_from_vault\nObsidian to SQLite"]
    end

    style Session fill:#1f3558,stroke:#58a6ff,color:#cdd9e5
    style CRUD fill:#2d1f58,stroke:#bc8cff,color:#cdd9e5
    style Query fill:#1f3a2d,stroke:#3fb950,color:#cdd9e5
    style Sync fill:#3a2d1f,stroke:#d29922,color:#cdd9e5

🔒 Agent Isolation

graph TD
    ENV["🌍 AGENT_NAME env var"]

    ENV -->|"= 'health'"| HEALTH["🏥 health namespace\nWrite: own only\nRead: own + global"]
    ENV -->|"= 'orchestrator'"| ORCH["🎯 orchestrator\nWrite: anywhere\nRead: everything"]
    ENV -->|"not set"| GLOBAL["🌐 global\nWrite: anywhere\nRead: everything"]

    HEALTH --> DB1[("health::memories")]
    HEALTH -.->|read only| DB2[("global::memories")]
    ORCH --> DB1
    ORCH --> DB2
    ORCH --> DB3[("other::memories")]
    GLOBAL --> DB2

    style ENV fill:#21262d,stroke:#58a6ff,color:#cdd9e5
    style HEALTH fill:#1f3558,stroke:#58a6ff,color:#cdd9e5
    style ORCH fill:#2d1f58,stroke:#bc8cff,color:#cdd9e5
    style GLOBAL fill:#1f3a2d,stroke:#3fb950,color:#cdd9e5

export AGENT_NAME=health   # Linux / macOS / WSL
$env:AGENT_NAME = "health" # Windows PowerShell

Role	Write Access	Read Access
Regular agent	Own namespace only	Own + global
`orchestrator`	Anywhere	Everything
Not set	Anywhere	Everything

🌉 Hermes — Cross-Platform Memory

Two agents collaborate on Slack. You ask on Telegram. The memory carries.

flowchart LR
    SLACK["💬 Slack Agent"] -->|POST| N8N["⚙️ n8n"]
    N8N -->|HTTP| BRIDGE["🌉 Hermes Bridge\nbridge.py :8765"]
    BRIDGE <-->|SQLite| DB[("🗄️ memory.db")]
    TG["📱 Telegram Bot"] <-->|HTTP| BRIDGE
    MCP["🔌 MCP Server"] <-->|SQLite| DB
    CLI["💻 CLI"] <-->|SQLite| DB

    style SLACK fill:#1f3a2d,stroke:#3fb950,color:#cdd9e5
    style N8N fill:#3a2d1f,stroke:#d29922,color:#cdd9e5
    style BRIDGE fill:#2d1f58,stroke:#bc8cff,color:#cdd9e5
    style DB fill:#1f3558,stroke:#58a6ff,color:#cdd9e5
    style TG fill:#1f3558,stroke:#58a6ff,color:#cdd9e5
    style MCP fill:#2d1f58,stroke:#bc8cff,color:#cdd9e5
    style CLI fill:#1f3a2d,stroke:#3fb950,color:#cdd9e5

# 1. Start the bridge
python hermes/bridge.py

# 2. Import hermes/n8n_workflow.json into your n8n instance

# 3. Start the Telegram bot
TELEGRAM_TOKEN=xxx HERMES_URL=http://your-server:8765 python hermes/telegram_bot.py

See hermes/README.md for full setup, API reference, and VPS deployment.

📦 Obsidian Integration (Optional)

export OBSIDIAN_VAULT=/path/to/your/vault

# Auto-mirrors every store/update/delete to Markdown
memory store --type semantic --content "API uses JWT" --project api

# Bulk sync
memory sync-to-vault    # SQLite → Markdown
memory sync-from-vault  # Markdown → SQLite

📋 CLAUDE.md Generator

memory generate-context --output CLAUDE.md

Creates a Markdown file from your most important memories. Place it in your project root and Claude Code reads it automatically every session.

📁 File Structure

agent-memory/
├── memory.py              # Core: CLI + library (1282 lines)
├── memory_mcp.py          # MCP server (645 lines, requires: pip install mcp)
├── assets/
│   └── banner.svg         # Repository banner image
├── hermes/                # Cross-platform memory layer
│   ├── bridge.py          # HTTP gateway (zero new deps)
│   ├── telegram_bot.py    # Telegram recall bot (requires: requests)
│   ├── n8n_workflow.json  # Ready-to-import Slack → memory workflow
│   └── README.md          # Hermes setup guide
├── README.md
├── LICENSE
├── .gitignore
├── requirements.txt       # Runtime: empty (stdlib only)
├── requirements-dev.txt   # Dev: pytest, black, mypy, ruff
└── tests/
    ├── __init__.py
    └── test_memory.py     # 58 tests across 8 classes

🗃️ Database Location

Platform	Default Path
macOS / Linux	`~/.claude/memory/memory.db`
Windows	`%APPDATA%\\claude\\memory\\memory.db`

Override: export AGENT_MEMORY_DIR=/custom/path

🧪 Testing

pip install -r requirements-dev.txt
python -m pytest tests/test_memory.py -v          # 58 tests
python -m pytest tests/test_memory.py -v --cov=memory

📄 License

This server cannot be installed

license - permissive license

quality - not tested

maintenance

How are these scores calculated?

Resources

GitHub Repository

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