ryumem 0.6.0

Bi-temporal knowledge graph memory system using ryugraph

Ryumem

Bi-temporal Knowledge Graph Memory System

Ryumem is an open-source memory system for building intelligent agents with persistent, queryable memory using a bi-temporal knowledge graph architecture.

Features

• Episode-first ingestion - Every piece of information starts as an episode
• Automatic entity & relationship extraction - Powered by LLM (OpenAI, Gemini, Ollama, or LiteLLM)
• Bi-temporal data model - Track when facts were valid and when they were recorded
• Advanced hybrid retrieval - Combines semantic search, BM25 keyword search, and graph traversal
• Temporal decay scoring - Recent facts automatically score higher with configurable decay
• Full multi-tenancy - Support for user_id, agent_id, session_id, group_id
• Automatic contradiction handling - Detects and invalidates outdated facts
• Incremental updates - No batch reprocessing required
• Automatic tool tracking - Track all tool executions and query patterns
• Query augmentation - Enrich queries with historical context from similar past queries
• Dynamic configuration - Hot-reload settings without server restart
• Web dashboard - Modern Next.js UI with graph visualization
• MCP Server - Model Context Protocol integration for Claude Desktop and coding agents

Architecture

                    +------------------+
                    |   Dashboard      |
                    |   (Next.js)      |
                    +--------+---------+
                             |
                             v
+-------------+     +------------------+     +------------------+
|  MCP Server | --> |   API Server     | --> |   Graph DB       |
| (TypeScript)|     |   (FastAPI)      |     |   (ryugraph)     |
+-------------+     +--------+---------+     +------------------+
                             |
                             v
                    +------------------+
                    |   Python SDK     |
                    |   (ryumem)       |
                    +------------------+

Components:

• API Server (/server) - FastAPI backend with REST API
• Dashboard (/dashboard) - Next.js web UI for visualization
• MCP Server (/mcp-server-ts) - Model Context Protocol server for AI agents
• Python SDK (/src/ryumem) - Client library for Python applications

Quick Start

Option 1: Docker Compose (Recommended)

# Clone the repository
git clone https://github.com/predictable-labs/ryumem.git
cd ryumem

# Configure environment
cp server/.env.example server/.env
# Edit server/.env and add your LLM API key (GOOGLE_API_KEY or OPENAI_API_KEY)

cp dashboard/env.template dashboard/.env
# Edit dashboard/.env if needed

# Start all services
docker-compose up -d

# Dashboard: http://localhost:3000
# API: http://localhost:8000
# API Docs: http://localhost:8000/docs

Option 2: Local Development

Prerequisites:

• Python 3.10+
• Node.js 18+
• An LLM API key (Google Gemini, OpenAI, or local Ollama)

1. Start the API Server:

# Install SDK
pip install -e .

# Install server dependencies
cd server
pip install -r requirements.txt

# Configure environment
cp .env.example .env
# Edit .env and set your LLM API key

# Start server
uvicorn main:app --reload --host 0.0.0.0 --port 8000

2. Start the Dashboard:

cd dashboard
npm install
cp env.template .env
npm run dev

3. Register and Get API Key:

# Register a customer
curl -X POST "http://localhost:8000/register" \
  -H "Content-Type: application/json" \
  -d '{"customer_id": "my_company"}'

# Response includes your API key (ryu_...)

Python SDK

Installation

pip install ryumem

Or install from source:

git clone https://github.com/predictable-labs/ryumem.git
cd ryumem
pip install -e .

Basic Usage

from ryumem import Ryumem

# Initialize client
ryumem = Ryumem(
    api_url="http://localhost:8000",
    api_key="ryu_your_api_key_here"
)

# Add an episode
ryumem.add_episode(
    content="Alice joined Google as a software engineer in 2023",
    user_id="user_123"
)

# Search memories
results = ryumem.search(
    query="Where does Alice work?",
    user_id="user_123",
    strategy="hybrid"
)

Google ADK Integration

from google.adk.agents import Agent
from ryumem import Ryumem
from ryumem.integrations import add_memory_to_agent, wrap_runner_with_tracking

# Initialize Ryumem
ryumem = Ryumem(
    api_url="http://localhost:8000",
    api_key="ryu_your_api_key_here",
    augment_queries=True,
    similarity_threshold=0.3,
)

# Create your agent
agent = Agent(
    model="gemini-2.0-flash-exp",
    name="my_agent",
    instruction="You are a helpful assistant with memory.",
    tools=[...]
)

# Add memory to agent - creates search_memory() and save_memory() tools
agent = add_memory_to_agent(agent, ryumem)

# Wrap runner for automatic tool tracking
runner = wrap_runner_with_tracking(runner, agent)

MCP Server for Coding Agents

Ryumem includes an MCP (Model Context Protocol) server for integration with Claude Desktop and other AI coding agents.

Installation

# From npm (when published)
npm install -g @predictable/ryumem-mcp-server

# Or from source
cd mcp-server-ts
npm install
npm run build

Configure for Claude Desktop

Add to ~/Library/Application Support/Claude/claude_desktop_config.json (macOS):

{
  "mcpServers": {
    "ryumem": {
      "command": "node",
      "args": ["/path/to/ryumem/mcp-server-ts/build/index.js"],
      "env": {
        "RYUMEM_API_URL": "http://localhost:8000",
        "RYUMEM_API_KEY": "ryu_your_api_key_here"
      }
    }
  }
}

Available MCP Tools:

• search_memory - Multi-strategy semantic search
• add_episode - Save new memories
• get_entity_context - Explore entity relationships
• batch_add_episodes - Bulk memory operations
• list_episodes, get_episode, update_episode_metadata - Episode management

See MCP Server Documentation for details.

Environment Variables

Server (server/.env)

Variable	Required	Default	Description
GOOGLE_API_KEY	Yes*	-	Google Gemini API key
OPENAI_API_KEY	Yes*	-	OpenAI API key
RYUMEM_DB_FOLDER	Yes	./data	Database storage path
ADMIN_API_KEY	Yes	-	Admin key for registration
LLM_PROVIDER	No	gemini	LLM provider (gemini, openai, ollama, litellm)
LLM_MODEL	No	gemini-2.0-flash-exp	LLM model name
EMBEDDING_PROVIDER	No	gemini	Embedding provider
EMBEDDING_MODEL	No	text-embedding-004	Embedding model
GITHUB_CLIENT_ID	No	-	GitHub OAuth (optional)
GITHUB_CLIENT_SECRET	No	-	GitHub OAuth (optional)
CORS_ORIGINS	No	http://localhost:3000	Allowed CORS origins

*At least one LLM API key is required

Dashboard (dashboard/.env)

Variable	Required	Default	Description
NEXT_PUBLIC_API_URL	Yes	http://localhost:8000	Ryumem API server URL
NEXT_PUBLIC_GITHUB_REDIRECT_URI	No	-	GitHub OAuth redirect

MCP Server

Variable	Required	Default	Description
RYUMEM_API_URL	Yes	http://localhost:8000	API server URL
RYUMEM_API_KEY	Yes	-	Your API key

Examples

See the examples/ directory for complete working examples:

• Getting Started - Basic SDK usage
• Google ADK Integration - Memory for ADK agents
• LiteLLM Integration - Multiple LLM providers
• Ollama Integration - Local LLM usage

Documentation

• Server Documentation - API server setup and endpoints
• Dashboard Documentation - Web UI setup
• MCP Server Documentation - Claude integration
• Examples - Usage examples

Development

# Install dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Format code
black src/
ruff check src/

# Type checking
mypy src/

License

GNU Affero General Public License v3.0 (AGPL-3.0) - See LICENSE for details.

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Built by Predictable Labs