by labsai
Config-driven engine that turns JSON into production-grade AI agents. Multi-agent orchestration, 12+ LLM providers, MCP/A2A protocols, RAG, persistent memory, and enterprise compliance (EU AI Act, GDPR, HIPAA). Built on Quarkus.
# Add to your Claude Code skills
git clone https://github.com/labsai/EDDILast scanned: 5/27/2026
{
"issues": [],
"status": "PASSED",
"scannedAt": "2026-05-27T08:04:59.422Z",
"semgrepRan": false,
"npmAuditRan": true,
"pipAuditRan": true
}EDDI is an open-source ai agents skill for AI coding assistants such as Claude Code, Codex CLI, and ChatGPT, built by labsai. Config-driven engine that turns JSON into production-grade AI agents. Multi-agent orchestration, 12+ LLM providers, MCP/A2A protocols, RAG, persistent memory, and enterprise compliance (EU AI Act, GDPR, HIPAA). Built on Quarkus. It has 361 GitHub stars.
Yes. EDDI passed SkillsLLM's automated security scan — a dependency vulnerability audit plus prompt-injection heuristics — with no high-severity issues. You can read the full report in the Security Report section on this page.
Clone the repository with "git clone https://github.com/labsai/EDDI" and add it to your Claude Code skills directory (see the Installation section above).
EDDI is primarily written in Java. It is open-source under labsai on GitHub, so you can review or fork the full source.
Yes. SkillsLLM lists many other AI Agents skills you can browse and compare side by side. Open the AI Agents category from the badge at the top of this page, or use the Related Skills and comparison links further down to weigh EDDI against similar tools.
No comments yet. Be the first to share your thoughts!

E.D.D.I (Enhanced Dialog Driven Interface) is a production-grade, config-driven multi-agent orchestration middleware for conversational AI. It coordinates users, AI agents, and business systems through intelligent routing, persistent memory, and API orchestration — without writing code.
Built with Java 25 and Quarkus. Ships as a Red Hat-certified Docker image. Native support for MCP (Model Context Protocol), A2A (Agent-to-Agent), Slack, OpenAPI, and OAuth 2.0.
Latest version: 6.1.0 · Website · Documentation · License: Apache 2.0
The fastest way to get EDDI running is the one-command installer. It sets up EDDI + your choice of database via Docker Compose, deploys the Agent Father starter agent, and walks you through creating your first AI agent.
Linux / macOS / WSL2:
curl -fsSL https://raw.githubusercontent.com/labsai/EDDI/main/install.sh | bash
Windows (PowerShell):
Invoke-WebRequest -UseBasicParsing -Uri "https://raw.githubusercontent.com/labsai/EDDI/main/install.ps1" -OutFile "install.ps1"
Unblock-File .\install.ps1
.\install.ps1
Requires Docker. The wizard auto-generates a unique vault encryption key for secret management.
bash install.sh --defaults # All defaults, no prompts
bash install.sh --db=postgres --with-auth # PostgreSQL + Keycloak
bash install.sh --full # Everything enabled (DB + auth + monitoring)
bash install.sh --local # Build Docker image from local source
The --local flag is for contributors testing pre-release builds:
./mvnw package -DskipTests # Build the Java app
bash install.sh --local # Build Docker image + start containers
The installer creates an eddi CLI wrapper that makes updating easy:
eddi update
This pulls the latest Docker image from the registry and restarts the containers. It works even when the same tag (e.g. latest) was re-published — Docker always checks the remote digest for changes.
eddicommand not found? The CLI lives at~/.eddi/eddi(Linux/macOS) or~/.eddi/eddi.cmd(Windows). Either restart your terminal so the PATH takes effect, or use the full path:# Linux / macOS ~/.eddi/eddi update # Windows (PowerShell) & "$HOME\.eddi\eddi.cmd" update
If the eddi CLI isn't available, run the equivalent docker commands from your install directory (~/.eddi by default):
cd ~/.eddi
docker compose --env-file .env -f docker-compose.yml pull
docker compose --env-file .env -f docker-compose.yml up -d
Adjust the -f flags to match your setup (e.g. add -f docker-compose.auth.yml if using Keycloak).
If you prefer manual control over Docker Compose:
# Default (EDDI + MongoDB)
docker compose up
# PostgreSQL instead of MongoDB
EDDI_DATASTORE_TYPE=postgres docker compose -f docker-compose.yml -f docker-compose.postgres.yml up
# With Keycloak authentication
docker compose -f docker-compose.yml -f docker-compose.auth.yml up
# With Prometheus + Grafana monitoring
docker compose -f docker-compose.yml -f docker-compose.monitoring.yml up
# Full stack (all overlays)
docker compose -f docker-compose.yml -f docker-compose.auth.yml \
-f docker-compose.monitoring.yml -f docker-compose.nats.yml up
Available compose overlays: docker-compose.auth.yml (Keycloak), docker-compose.monitoring.yml (Prometheus+Grafana), docker-compose.nats.yml (NATS JetStream), docker-compose.postgres.yml / docker-compose.postgres-only.yml, docker-compose.local.yml (build from source).
docker pull labsai/eddi # Pull latest from Docker Hub
→ hub.docker.com/r/labsai/eddi
Most multi-agent frameworks (LangGraph, CrewAI, AutoGen) are Python/Node libraries — great for prototyping, hard to govern in production. EDDI approaches from the opposite direction: a deterministic engine built to safely govern non-deterministic AI.
| Dimension | Typical Python/Node Frameworks | EDDI |
|---|---|---|
| Concurrency | GIL or single-threaded event loop | Java 25 Virtual Threads — true OS-level parallelism |
| Agent Logic | Embedded in application code | Versioned JSON configurations — update behavior without redeployment |
| Security Model | Often relies on sandboxed code execution | No dynamic code execution at all; envelope-encrypted vault, SSRF protection |
| Compliance | Requires custom implementation | GDPR, HIPAA, EU AI Act infrastructure built-in |
| Audit Trail | Application-level logging | HMAC-SHA256 immutable ledger with cryptographic agent signing |
| Deployment | pip/npm + manual infrastructure | One-command Docker install, Kubernetes/OpenShift-ready |
"The engine is strict so the AI can be creative." — Project Philosophy