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Show HN: EDDI – Multi-agent AI engine where agent logic lives in JSON, not code
A multi-agent AI engine that ensures control and predictability in production by defining agent logic in JSON configurations rather than dynamic code, preventing arbitrary code execution by LLMs, and offering advanced orchestration, model selection, and cost-optimization features.
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A multi-agent AI engine that ensures control and predictability in production by defining agent logic in JSON configurations rather than dynamic code, preventing arbitrary code execution by LLMs, and offering advanced orchestration, model selection, and cost-optimization features.
EDDI addresses a critical enterprise concern: maintaining control and security over AI agent behavior in production. By externalizing agent logic into JSON configurations, it mitigates the risks associated with dynamic code execution by LLMs, a significant barrier to enterprise AI adoption. The emphasis on predictable, auditable agent behavior, combined with advanced orchestration, multi-model support, and cost-aware cascading, positions EDDI as a robust solution for complex, production-grade AI systems. Its compatibility with MCP and A2A protocols further enhances its interoperability within the emerging agent ecosystem. This product directly targets the operational challenges of deploying and managing AI agents at scale, offering a governance-first approach.
I started EDDI in 2006 as a rule-based dialog engine. Back then it was pattern matching and state machines. When LLMs showed up, the interesting question wasn't "how do I call GPT" but "how do I keep control over what the AI does in production?"My answer was: agent logic belongs in JSON configs, not code. You describe what an agent should do, which LLM to use, what tools it can call, how it should behave. The engine reads that config and runs it. No dynamic code execution, ever. The LLM cannot run arbitrary code by design. The engine is strict so the AI can be creative.v6 is the version where this actually became practical. You can have groups of agents debating a topic in five different orchestration styles (round table, peer review, devil's advocate...). Each agent can use a different model. A cascading system tries cheap models first and only escalates to expensive ones when confidence is low.It also implements MCP as both server and client, so you can control EDDI from Claude Desktop or Cursor. And Google's A2A protocol for agents discovering each other across platforms.The whole thing runs in Java 25 on Quarkus, ships as a single Docker image, and installs with one command. Open source since 2017, Apache 2.0.Would love to hear thoughts on the architecture and feature set. And if you have ideas for what's missing or what you'd want from a system like this, I'm all ears. Always looking for good input on the roadmap.
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What is EDDI – Multi-agent AI engine where agent logic lives in JSON, not code?
EDDI – Multi-agent AI engine where agent logic lives in JSON, not code is analyzed by our AI as: A multi-agent AI engine that ensures control and predictability in production by defining agent logic in JSON configurations rather than dynamic code, preventing arbitrary code execution by LLMs, and offering advanced orchestration, model selection, and cost-optimization features.. It focuses on EDDI addresses a critical enterprise concern: maintaining control and security over AI agent behavior in production. By externalizing agent logic i...
Where did EDDI – Multi-agent AI engine where agent logic lives in JSON, not code originate?
Data for EDDI – Multi-agent AI engine where agent logic lives in JSON, not code was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
When was EDDI – Multi-agent AI engine where agent logic lives in JSON, not code publicly launched?
The initial public indexing or launch date for EDDI – Multi-agent AI engine where agent logic lives in JSON, not code within our tracked developer communities was recorded on April 17, 2026.
How popular is EDDI – Multi-agent AI engine where agent logic lives in JSON, not code?
EDDI – Multi-agent AI engine where agent logic lives in JSON, not code has achieved measurable traction, logging over 2 traction score and facilitating 0 recorded discussions or engagements.
Which technical categories define EDDI – Multi-agent AI engine where agent logic lives in JSON, not code?
Based on metadata extraction, EDDI – Multi-agent AI engine where agent logic lives in JSON, not code is categorized under topics such as: multi-agent AI engine, agent logic, JSON configs, dynamic code execution.
What are some commercial alternatives to EDDI – Multi-agent AI engine where agent logic lives in JSON, not code?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Qwen3.6-Plus, which offers overlapping value propositions.
How does the creator describe EDDI – Multi-agent AI engine where agent logic lives in JSON, not code?
The original author or development team describes the product as follows: "I started EDDI in 2006 as a rule-based dialog engine. Back then it was pattern matching and state machines. When LLMs showed up, the interesting question wasn't "how do I call GPT" but "how do I ke..."
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