# 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** · License: Apache 2.0 · [GitHub](https://github.com/labsai/EDDI) · [Website](https://eddi.labs.ai/) *** ## 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. *** ## 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 | *** ## 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 *** ## 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) | *** ## 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) | *** ## 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 to access the Manager Dashboard. See [**Getting Started**](https://docs.labs.ai/getting-started/getting-started) for all setup options. *** ## 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. --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://docs.labs.ai/readme.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.