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EdgeChains

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Basic Information

EdgeChains is an open source framework for building and orchestrating generative AI applications with declarative prompt and chain configuration. The project focuses on keeping prompt and chain logic outside of application code using jsonnet, enabling versioning, diffing and easier testing. It provides a Java-first runtime distributed as a single edgechain.jar artifact so developers can run example chains with minimal setup. EdgeChains targets orchestration challenges in Generative AI such as prompt explosion, prompt drift, token cost measurement and parallel execution of chain-of-thought tasks. The README emphasizes fault tolerance, automatic parallelism across available hardware via the JVM, and the ability to store or load prompts from external locations for production testing and iteration.

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App URL
https://github.com/arakoodev/EdgeChains
Github Repository
https://github.com/arakoodev/EdgeChains/blob/main/README.md

App Details

Features
EdgeChains highlights a small operational surface: one script file plus one jsonnet config to run production-ready GenAI workflows. Prompts are written in jsonnet for versioning and diffs. The runtime auto-parallelizes LLM chains and chain-of-thought tasks across CPUs, GPUs and TPUs via the JVM. Built-in fault tolerance supports retries and backoff for failed requests. The framework measures token costs per prompt or chain to help manage consumption. It supports declarative orchestration inspired by Kubernetes config management and enables storing prompts externally. The distribution is a single Java jar with example jbang usage and minimal local dependencies.
Use Cases
The project helps developers by shifting orchestration out of code into declarative configs so prompts and chains can be versioned, tested and iterated without touching application logic. Automatic parallelism and JVM-based execution speed up complex chain-of-thought workloads, while fault tolerance and retry semantics improve production reliability. Token cost measurement gives teams visibility into cost tradeoffs of prompt designs. Because prompts live in jsonnet and can be hosted externally, teams can manage prompt drift and A/B test prompt variants more easily. The single-jar distribution lowers setup friction for trying examples and deploying orchestration logic.

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