Platform vs. Library vs. Builder
AI编排市场有三种原型：可视化节点构建器、代码库和云平台。EDDI是一个可部署的中间件平台。
vs. Visual Node Builders
Flowise · n8n · Similar Platforms
可视化节点构建器使原型设计变得快速。但是，它们的架构在企业规模上会带来限制。
Architecture Comparison
| Dimension | Visual Node Builders | EDDI |
|---|---|---|
| Runtime | Node.js single-threaded event loop | JVM with millions of virtual threads |
| Code Execution | Dynamic eval() / code blocks | Zero eval() — declarative JSON only |
| Security | Multiple critical CVEs documented | No dynamic code execution by design |
| Authentication | Basic auth or community plugins | Enterprise OIDC/Keycloak with RBAC |
| Database | SQLite (some support PostgreSQL) | MongoDB or PostgreSQL |
| Audit Trail | Application-level logging | HMAC-SHA256 immutable audit ledger |
| Compliance | Manual implementation | 17+ frameworks built in |
安全考量
2026年初，AI代理生态系统经历了一次重大的安全清算。
EDDI采用了截然不同的方法：绝对禁止运行时代码评估。
vs. Code Libraries & Frameworks
LangGraph · CrewAI · AutoGen · LangChain · Spring AI
代码库是出色的构建块。但选择一个库意味着需要自己构建所有的生产基础设施。
“Day 2运营”的差距
团队必须从零开始构建企业基础设施：
- REST API controllers
- Authentication (OIDC, RBAC)
- Conversation state management
- Audit logging
- Management UI
- Secret management
- Horizontal scaling
- Cost tracking
- GDPR compliance API
- Durable execution
Framework Comparison
| Framework | Abstraction | Learning Curve | State & Memory | Production Infrastructure |
|---|---|---|---|---|
| LangGraph | Nodes & Edges (DAG) | Moderate–High | Built-in persistence | Requires custom infrastructure |
| CrewAI | Role-based delegation | Low | Ephemeral | Prototyping-focused |
| AutoGen | Multi-party dialogues | Low–Moderate | Conversation history | Transitioning framework |
| EDDI | Orchestration Platform | Low (Config-as-Code) | Persistent memory + dream consolidation | Fully packaged |
库提供逻辑；EDDI提供基础设施。
vs. Cloud AI Platforms
AWS Bedrock · Azure AI Studio · Google Vertex AI
云AI平台提供托管基础设施，但会造成严重的供应商锁定。
Sovereignty & Portability
| Dimension | Cloud AI Platforms | EDDI |
|---|---|---|
| Deployment | Locked to provider | Docker-native — any environment |
| Model Choice | Provider portfolio | 12 LLM providers + OpenAI-compatible |
| Cost Control | Provider pricing | Model cascading reduces costs 60–80% |
| Data Residency | Provider infrastructure | Full data sovereignty |
| Portability | Provider-specific APIs | MCP, A2A, OpenAPI, REST |
| Air-Gap | Not possible | Full offline with Ollama/Jlama |
EDDI的自托管架构提供了锁定在云端平台所无法比拟的基础设施主权。
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