The Gap in Enterprise AI
Enterprise AI orchestration has no middle ground. Teams either prototype with fragile low-code tools and rewrite for production, or build everything from scratch using AI libraries and frameworks.
EDDI Fills This Void
EDDI is a deployable middleware platform, not a library. It provides everything teams need out of the box:
- Visual Management UI: The EDDI Manager for building and monitoring agents
- Configuration-as-Code: Agent logic is JSON, not compiled code
- 65 MCP Tools: Full AI-native control via Model Context Protocol
- Enterprise Security: OIDC, vault, audit trails, no eval()
- Production Infrastructure: REST APIs, conversation state management, Prometheus metrics
- Horizontal Scaling: NATS JetStream for distributed architectures
Who Should Use EDDI?
- Enterprise teams who need a production-ready AI orchestration platform
- Prompt engineers who want to iterate without redeployment
- Regulated industries that require audit trails and EU AI Act compliance
- Platform teams: building internal AI services for multiple departments
EDDI vs. Typical Agent Frameworks
| Dimension | 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 configs, update behavior without redeployment |
| Security Model | Relies on sandboxed code execution | No dynamic code execution; 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 |
12 LLM Providers Supported
Connect to any major LLM provider, or bring your own via any OpenAI-compatible endpoint.
| Category | Providers |
|---|---|
| Cloud APIs | OpenAI · Anthropic Claude · Google Gemini · Mistral AI |
| Enterprise Cloud | Azure OpenAI · Amazon Bedrock · Oracle GenAI · Google Vertex AI |
| Self-Hosted | Ollama · Jlama · Hugging Face |
| Compatible | Any OpenAI-compatible endpoint (DeepSeek, Cohere, etc.) via baseUrl |
8 Questions Every CIO Should Ask
When evaluating AI agent orchestration platforms, these are the questions that separate production-grade infrastructure from fragile prototypes:
Total Cost of Ownership: Build vs. Deploy
The hidden cost of using AI libraries is not the library itself, it's the invisible infrastructure teams must build, maintain, and secure around it:
Building with Libraries
- Custom REST API layer (2–4 weeks)
- Authentication & RBAC system (2–3 weeks)
- Conversation state persistence (1–2 weeks)
- Audit trail & compliance logging (2–4 weeks)
- Management UI for non-developers (4–8 weeks)
- Secret management integration (1–2 weeks)
- Horizontal scaling & coordination (2–4 weeks)
- Ongoing maintenance & security patching
Deploying EDDI
- One-command install (5 minutes)
- All of the above included out of the box
- Team focuses on business logic, not infrastructure
- Maintained continuously since 2006, open source since 2018
The Business Case
EDDI's value is measured in what teams don't have to build: the REST APIs, authentication systems, audit infrastructure, management UIs, and compliance tooling that would otherwise consume months of engineering time. Model cascading alone can reduce LLM costs by up to 60–80% in typical multi-model workloads by routing simple queries to cheaper models, escalating to powerful models only when confidence is low.
For regulated industries, the cost equation is even clearer: the alternative to EDDI's built-in compliance infrastructure is a custom implementation covering GDPR, EU AI Act, HIPAA, and potentially 15+ additional regulatory frameworks, each requiring its own data subject rights implementation, audit trail, and governance tooling.
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