E.D.D.I Documentation

Multi-Agent Orchestration Middleware for Conversational AI — coordinate multiple AI agents, business systems, and conversation flows through configuration, not code.

Welcome to the official documentation for E.D.D.I (Enhanced Dialog Driven Interface) — a production-grade multi-agent orchestration middleware for conversational AI.

Latest version: 6.0.0 · License: Apache 2.0 · GitHub · Website

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What Is EDDI?

EDDI coordinates between users, AI agents (LLMs), and business systems. It provides intelligent routing, conversation management, and API orchestration — all through versioned JSON configurations, not code.

Built with Java 25 and Quarkus. Ships as a Red Hat-certified Docker image. Supports MongoDB or PostgreSQL. Deploy on Docker, Kubernetes, or OpenShift.

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Start Here

Guide	Time	Description
🚀 Getting Started	5 min	Install EDDI and run your first agent
⚡ Developer Quickstart	10 min	Build a complete agent step-by-step via REST API
🏗️ Architecture Overview	15 min	Understand the lifecycle pipeline and config model
🧩 Putting It All Together	20 min	Real-world hotel booking agent walkthrough

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Key Capabilities

🤖 Multi-Agent Orchestration

• 12 LLM Providers — OpenAI, Anthropic, Google Gemini, Mistral AI, Azure OpenAI, Amazon Bedrock, Oracle GenAI, Vertex AI, Ollama, Jlama, Hugging Face, plus OpenAI-compatible endpoints

• Group Conversations — Multi-agent debates (Round Table, Peer Review, Devil's Advocate, Delphi, Debate)

• Managed Agents — Intent-based auto-routing with one conversation per user per intent

• Model Cascading — Cost-optimized multi-model routing with confidence-based escalation

🔗 Protocols & Interoperability

• MCP Server (48+ tools) — Full EDDI control from Claude Desktop, IDE plugins, or any MCP client

• A2A Protocol — Agent-to-Agent peer communication with skill discovery

🧠 Intelligence & Memory

• LLM Integration — Connect any of 12 providers with agent mode and tool calling

• RAG — 7 embedding providers, 5 vector stores, plus zero-infrastructure httpCall RAG

• Persistent User Memory — Agents remember facts across conversations

• Properties — Config-driven slot-filling and importance extraction

🔐 Enterprise Security

• Secrets Vault — Envelope encryption (AES-256-GCM + PBKDF2) for API keys

• Security — SSRF protection, sandboxed evaluation, Keycloak auth

• Audit Ledger — Write-once trail with HMAC integrity for EU AI Act compliance

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Agent Configuration

Build agent behavior by composing these extensions:

Extension	Purpose	Guide
Behavior Rules	Decision-making logic — IF conditions THEN actions	→ Guide
HTTP Calls	Call external REST APIs with templated requests	→ Guide
LLM Integration	Chat, agent mode, tool calling with any provider	→ Guide
Output	Define what the agent says, with alternatives	→ Guide
Output Templating	Dynamic responses using Qute templates	→ Guide
Properties	Extract and store structured data from conversations	→ Guide
Semantic Parser	Map user input to expressions via dictionaries	→ Guide
Context	Inject external data from your application	→ Guide

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Deployment & Operations

Topic	Guide
🐳 Docker	→ Guide
☸️ Kubernetes & Helm	→ Guide
🔴 Red Hat OpenShift	→ Guide
☁️ AWS + MongoDB Atlas	→ Guide
📊 Metrics & Monitoring	→ Guide
📋 Log Administration	→ Guide
🔖 Release & Versioning	→ Guide

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Quick Start

# One-command install (interactive wizard)
curl -fsSL https://raw.githubusercontent.com/labsai/EDDI/main/install.sh | bash

# Or pull and run directly
docker pull labsai/eddi:latest
docker compose up

Then open http://localhost:7070 to access the Manager Dashboard.

See Getting Started for all setup options.

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Browse All Documentation

See the full Table of Contents for the complete documentation index.

Have a question? Check the FAQs for common setup and configuration answers.