Eion

<div align="center"> <img src="assets/eion-navy.png#gh-light-mode-only" alt="Eion Logo" width="200" height="200"> <img src="assets/eion-cream.png#gh-dark-mode-only" alt="Eion Logo" width="200" height="200"> <h1 style="border-bottom: none; margin-bottom: 0;">Eion</h1>

Connecting AI agents through shared memory and collaborative intelligence.

</div> <div align="center"> <img src="assets/eion-demo.gif" alt="Eion Demo" width="90%" /> </div>

 

Eion is a shared memory storage that provides unified knowledge graph capabilities for multi-agent systems, adapting to different AI deployment scenarios from single LLM applications to complex multi-agency systems.

1. LLM Application

User ↔ LLM Application → Eion (context storage)

2. AI Agent Application

Business Logic ↔ AI Agent → Eion (memory + knowledge graph)

3. Agency (Multi-Agent) Systems

3a. Sequential Agency

Agent A → context → Agent B → context → Agent C
                ↓              ↓              ↓
              Eion ← shared memory & knowledge → Eion

3b. Concurrent Live Agency (WIP)

Agent A ──┐
          ├── shared live context ← Eion (live sync + notifications)
Agent B ──┤
          │
Agent C ──┘

4. External Guest Agent Access

Internal Agency: Agent A ↔ Agent B → Eion ← External Agent C (guest)
                                            ↑
                                    (controlled access)

Quick Start

Prerequisites

• Docker & Docker Compose: For PostgreSQL and Neo4j
• Go 1.21+: For the Eion server
• Python 3.13+: For knowledge extraction services

1. Clone and Setup

git clone <repo>
cd eion

2. Start Database Services

# Start all required databases (PostgreSQL + Neo4j)
docker-compose up -d

# Verify databases are ready
docker-compose ps

3. Setup Database Extensions and Tables

# Enable the pgvector extension (required for embeddings)
docker exec eion_postgres psql -U eion -d eion -c "CREATE EXTENSION IF NOT EXISTS vector;"

# Run main orchestrator migrations (includes sessions table)
docker exec -i eion_postgres psql -U eion -d eion < database_setup.sql

4. Install Python Dependencies

# Create virtual environment
python3 -m venv .venv
source .venv/bin/activate  # On Windows: .venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

5. Build and Run Eion Server

# Build the server
go build -o eion-server ./cmd/eion-server

# Run the server
./eion-server

6. Verify Setup

# Check server health
curl http://localhost:8080/health

# Expected response:
# {"status":"healthy","timestamp":"2024-12-19T10:30:00Z","services":{"database":"healthy","embedding":"healthy"}}

7. Access the Register Console

Navigate to the Register Console for easy cluster management:

http://localhost:8080/console/

⚠️ Important: Include the trailing slash / in the URL.

Console Features

Agent Registration Tab:

• Simple form-based agent registration with automatic ID generation
• Support for different permission levels (read, read-write, full CRUD)
• Real-time registration status feedback
• View all registered agents in a table

Monitoring Tab:

• Real-time agent status and system monitoring
• View active sessions and users (coming soon)
• Refresh functionality for live updates

Resources Tab:

• Copy-paste ready HTTP API examples with your current configuration
• MCP integration snippets (when MCP is enabled)
• Quick start guide for developers
• Copy-to-clipboard functionality for easy integration

Using the Console

1. Register an Agent:

   • Go to "Register Agents" tab
   • Fill in name, description, and permissions
   • Click "Register Agent"
   • Agent appears in the table below

2. Monitor Activity:

   • Switch to "Monitoring" tab
   • View agent status and system health
   • Use refresh button for updates

3. Get Integration Examples:

   • Visit "Resources" tab
   • Copy HTTP API examples with your server details
   • Use MCP configuration if MCP is enabled

Configuration Display

The console automatically shows:

• Cluster API key for authentication
• Server host and port
• MCP and Numa integration status
• Neo4j connection details (when enabled)

Styling

The console uses Eion's brand colors:

• Navy Blue background with Cream text
• Responsive design for desktop and mobile
• Consistent with the main Eion website styling

Architecture

Eion provides a unified API that combines:

• Memory Storage: PostgreSQL with pgvector for conversation history and semantic search
• Knowledge Graph: Neo4j with in-house extraction for temporal knowledge storage
• Real Embeddings: all-MiniLM-L6-v2 model (384 dimensions) using sentence-transformers - production-ready embeddings
• Knowledge Extraction: In-house extraction service for entity/relationship extraction

MCP Server Integration

Eion includes a built-in Model Context Protocol (MCP) Server that enables seamless agent integration through standardized tool interfaces.

What MCP Provides

The MCP server exposes Eion's Session-level API as MCP tools, allowing AI agents to:

• Store and retrieve conversation memories
• Perform semantic search across stored conversations
• Create and manage knowledge entries
• Search through extracted knowledge graphs

Available MCP Tools

Memory Tools (4):

• get_memory - Retrieve conversation history with filters
• add_memory - Store new conversation messages
• search_memory - Semantic search in conversation history
• delete_memory - Remove conversation data

Knowledge Tools (4):

• search_knowledge - Find relevant extracted knowledge
• create_knowledge - Add new knowledge entries
• update_knowledge - Modify existing knowledge
• delete_knowledge - Remove knowledge entries

Agent Integration

Direct MCP Client (Recommended):

from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

# Connect to Eion MCP server
server_params = StdioServerParameters(
    command="python",
    args=["-m", "internal.mcp.server"],
    env={"EION_BASE_URL": "http://localhost:8080"}
)

async with stdio_client(server_params) as (read, write):
    async with ClientSession(read, write) as session:
        await session.initialize()
        
        # Use tools with agent credentials
        result = await session.call_tool("add_memory", {
            "session_id": "agent_session_123",
            "agent_id": "my_registered_agent",
            "user_id": "end_user_456",
            "messages": [{"role": "user", "content": "Hello!"}]
        })

LangChain Integration:

from langchain_mcp_tools import convert_mcp_to_langchain_tools

mcp_servers = {
    "eion": {
        "command": "python", 
        "args": ["-m", "internal.mcp.server"],
        "env": {"EION_BASE_URL": "http://localhost:8080"}
    }
}

tools, cleanup = await convert_mcp_to_langchain_tools(mcp_servers)
# Use tools with LangChain agents

Claude Desktop Integration: Add to claude_desktop_config.json:

{
  "mcpServers": {
    "eion": {
      "command": "python",
      "args": ["-m", "internal.mcp.server"],
      "env": {"EION_BASE_URL": "http://localhost:8080"},
      "cwd": "/path/to/eion"
    }
  }
}

Authentication

All MCP tools require agent authentication:

• session_id - Unique session identifier for the conversation
• agent_id - Registered agent ID (obtain via Developer API)
• user_id - End user ID that the agent serves

Agents must be registered via the Developer API before using MCP tools:

curl -X POST http://localhost:8080/cluster/v1/agents \
  -H "Content-Type: application/json" \
  -d '{"name": "My Agent", "capabilities": ["memory", "knowledge"]}'

MCP Configuration

MCP server settings in eion.yaml:

mcp:
  enabled: true                     # Enable MCP server
  port: 8081                        # MCP server port  
  python_path: ".venv/bin/python"   # Python executable path
  log_level: "INFO"                 # Logging level
  timeout: 30                       # Request timeout (seconds)

The MCP server is embedded within the Eion server process and requires no separate deployment.

Configuration

Create eion.yaml (optional - defaults work out of the box):

common:
  http:
    host: "0.0.0.0"
    port: 8080
  
  postgres:
    user: "eion"
    password: "eion_pass" 
    host: "localhost"
    port: 5432
    database: "eion"
  
  # Neo4j Configuration (Required)
  numa:
    neo4j:
      uri: "bolt://localhost:7687"
      username: "neo4j"
      password: "password"
      database: "neo4j"

# MCP Server Configuration (Optional)
mcp:
  enabled: true                     # Enable MCP server
  port: 8081                        # MCP server port
  python_path: ".venv/bin/python"   # Path to Python executable
  log_level: "INFO"                 # MCP server log level
  timeout: 30                       # Request timeout in seconds

# Register Console Configuration (Optional)
console:
  enabled: true                     # Enable Register Console (default: true)

Additional Configuration

For production deployments, you may want to customize the database settings in docker-compose.yml or create your own configuration.

Or use the automated setup script:

# One-command setup (includes database startup, Python env, and server build)
./setup.sh

# Then start the server
./eion-server

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<div align="center"> <img src="assets/eion