# E.D.D.I Documentation

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-RC1** · License: Apache 2.0 · [GitHub](https://github.com/labsai/EDDI) · [Website](https://eddi.labs.ai/)

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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**](https://docs.labs.ai/getting-started/getting-started)                 | 5 min  | Install EDDI and run your first agent              |
| ⚡ [**Developer Quickstart**](https://docs.labs.ai/getting-started/developer-quickstart)        | 10 min | Build a complete agent step-by-step via REST API   |
| 🏗️ [**Architecture Overview**](https://docs.labs.ai/architecture-and-concepts/architecture)   | 15 min | Understand the lifecycle pipeline and config model |
| 🧩 [**Putting It All Together**](https://docs.labs.ai/getting-started/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**](https://docs.labs.ai/conversations-and-orchestration/group-conversations) — Multi-agent debates (Round Table, Peer Review, Devil's Advocate, Delphi, Debate)
* [**Managed Agents**](https://docs.labs.ai/conversations-and-orchestration/managed-agents) — Intent-based auto-routing with one conversation per user per intent
* [**Model Cascading**](https://docs.labs.ai/agent-configuration/model-cascade) — Cost-optimized multi-model routing with confidence-based escalation

### 🔗 Protocols & Interoperability

* [**MCP Server**](https://docs.labs.ai/protocols-and-integration/mcp-server) (48+ tools) — Full EDDI control from Claude Desktop, IDE plugins, or any MCP client
* [**A2A Protocol**](https://docs.labs.ai/protocols-and-integration/a2a-protocol) — Agent-to-Agent peer communication with skill discovery

### 🧠 Intelligence & Memory

* [**LLM Integration**](https://docs.labs.ai/agent-configuration/langchain) — Connect any of 12 providers with agent mode and tool calling
* [**RAG**](https://docs.labs.ai/agent-configuration/rag) — 7 embedding providers, 5 vector stores, plus zero-infrastructure httpCall RAG
* [**Persistent User Memory**](https://docs.labs.ai/architecture-and-concepts/user-memory) — Agents remember facts across conversations
* [**Properties**](https://docs.labs.ai/architecture-and-concepts/properties) — Config-driven slot-filling and importance extraction

### 🔐 Enterprise Security

* [**Secrets Vault**](https://docs.labs.ai/security-and-compliance/secrets-vault) — Envelope encryption (AES-256-GCM + PBKDF2) for API keys
* [**Security**](https://docs.labs.ai/security-and-compliance/security) — SSRF protection, sandboxed evaluation, Keycloak auth
* [**Audit Ledger**](https://docs.labs.ai/security-and-compliance/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](https://docs.labs.ai/agent-configuration/behavior-rules)              |
| **HTTP Calls**        | Call external REST APIs with templated requests      | [→ Guide](https://docs.labs.ai/agent-configuration/httpcalls)                   |
| **LLM Integration**   | Chat, agent mode, tool calling with any provider     | [→ Guide](https://docs.labs.ai/agent-configuration/langchain)                   |
| **Output**            | Define what the agent says, with alternatives        | [→ Guide](https://docs.labs.ai/agent-configuration/output-configuration)        |
| **Output Templating** | Dynamic responses using Qute templates               | [→ Guide](https://docs.labs.ai/agent-configuration/output-templating)           |
| **Properties**        | Extract and store structured data from conversations | [→ Guide](https://docs.labs.ai/architecture-and-concepts/properties)            |
| **Semantic Parser**   | Map user input to expressions via dictionaries       | [→ Guide](https://docs.labs.ai/agent-configuration/semantic-parser)             |
| **Context**           | Inject external data from your application           | [→ Guide](https://docs.labs.ai/agent-configuration/passing-context-information) |

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## Deployment & Operations

| Topic                   | Guide                                                                                              |
| ----------------------- | -------------------------------------------------------------------------------------------------- |
| 🐳 Docker               | [→ Guide](https://docs.labs.ai/deployment-and-infrastructure/docker)                               |
| ☸️ Kubernetes & Helm    | [→ Guide](https://docs.labs.ai/deployment-and-infrastructure/kubernetes)                           |
| 🔴 Red Hat OpenShift    | [→ Guide](https://docs.labs.ai/deployment-and-infrastructure/redhat-openshift)                     |
| ☁️ AWS + MongoDB Atlas  | [→ Guide](https://docs.labs.ai/deployment-and-infrastructure/setup-eddi-on-aws-with-mongodb-atlas) |
| 📊 Metrics & Monitoring | [→ Guide](https://docs.labs.ai/deployment-and-infrastructure/metrics)                              |
| 📋 Log Administration   | [→ Guide](https://docs.labs.ai/deployment-and-infrastructure/log-administration)                   |
| 🔖 Release & Versioning | [→ Guide](https://docs.labs.ai/deployment-and-infrastructure/release-versioning)                   |

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## Quick Start

```bash
# 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**](https://docs.labs.ai/getting-started/getting-started) for all setup options.

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## Browse All Documentation

See the full [**Table of Contents**](https://github.com/labsai/EDDI/blob/main/docs/SUMMARY.md) for the complete documentation index.

**Have a question?** Check the [**FAQs**](https://docs.labs.ai/reference/how-to...) for common setup and configuration answers.