Eion | Connecting AI Agents Through Shared Memory And Collaborative Intelligence

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Eion Logo

Eion

Connecting AI agents through shared memory and collaborative intelligence.

Version License: AGPL v3

Eion Demo

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"}}

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

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"

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

GitHub:

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