EDDI

EDDI (Enhanced Dialog Driven Interface) is a Java/Quarkus multi-agent orchestration middleware for conversational AI. It routes conversations to one or more LLM providers, coordinates tool/API calls, and manages stateful, configurable conversation flows via a lifecycle pipeline and versioned JSON behavior rules. It can run as a Docker container (Kubernetes/OpenShift friendly) and includes built-in tools plus support for whitelisted HTTP-call tools.

Evaluated Mar 30, 2026 (0d ago)
Homepage ↗ Repo ↗ Ai Ml ai multi-agent orchestration conversation-memory conversational-ui java quarkus mcp-client mcp-server mongodb postgres prompt-management restful-api kubernetes security
⚙ Agent Friendliness
62
/ 100
Can an agent use this?
🔒 Security
56
/ 100
Is it safe for agents?
⚡ Reliability
39
/ 100
Does it work consistently?

Score Breakdown

⚙ Agent Friendliness

MCP Quality
0
Documentation
75
Error Messages
0
Auth Simplicity
70
Rate Limits
80

🔒 Security

TLS Enforcement
70
Auth Strength
75
Scope Granularity
35
Dep. Hygiene
45
Secret Handling
45

README/docs mention security controls such as SSRF protection, sandboxed calculator evaluation, tool hardening, and a tool execution pipeline with rate limiting, caching, cost tracking/budget enforcement, and whitelisted HTTP-call tools. However, the provided content does not detail TLS enforcement requirements, secret handling/logging behavior, or fine-grained authorization scopes, so scores are conservative.

⚡ Reliability

Uptime/SLA
20
Version Stability
60
Breaking Changes
40
Error Recovery
35
AF Security Reliability

Best When

You need a configurable middleware layer that manages agent routing, tool execution (including safe HTTP calls), and conversation state across multiple backend systems and LLMs.

Avoid When

You only need a lightweight chatbot UI or a single-model proxy with minimal security/tooling; the added orchestration/config complexity may be unnecessary.

Use Cases

  • Orchestrating multiple LLM providers based on conversation context/rules
  • Building stateful multi-step conversational experiences with tool/function calling
  • Integrating chat/agent flows with external REST APIs and business logic
  • Secure execution of tool calls with SSRF protection, rate limiting, caching, and budget enforcement
  • Deploying conversational middleware as a scalable, cloud-native service with metrics and health endpoints

Not For

  • Directly replacing an LLM API for simple single-call chat without orchestration needs
  • Uncontrolled agent tool execution where arbitrary outbound requests are acceptable (EDDI emphasizes whitelisting)
  • Environments that cannot run Java 21/Quarkus or require first-party managed SaaS

Interface

REST API
Yes
GraphQL
No
gRPC
No
MCP Server
No
SDK
No
Webhooks
No

Authentication

Methods: OAuth 2.0 (Keycloak) for authentication and user management (as stated)
OAuth: Yes Scopes: No

Authentication is described as OAuth2 via Keycloak, but the README does not specify token flows, scopes, or authorization model details.

Pricing

Free tier: No
Requires CC: No

No pricing information provided; project appears open-source and self-hosted via Docker/Kubernetes.

Agent Metadata

Pagination
none
Idempotent
False
Retry Guidance
Not documented

Known Gotchas

  • Tool execution is governed by configuration/rules; agents may fail to call tools if tool whitelisting/rate limits/budgets are not configured as expected.
  • Outbound web requests and SSRF-prone capabilities are protected; some URLs may be blocked due to SSRF protection rules.
  • HTTP-call tools rely on pre-configuration; agent behavior may be limited to allowed endpoints/parameters rather than free-form API calls.

Alternatives

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Scores are editorial opinions as of 2026-03-30.

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