de synalinks-skills898
Keras-inspired framework for building structured, neuro-symbolic LLM programs with DataModel schemas, modular Programs, and training/optimization tools.
Synalinks provides a Keras-like API for constructing structured LLM applications: define DataModel schemas, compose Modules and Programs, run Generators and Decisions, and train with optimizer wrappers. It focuses on structured JSON I/O, parallel branching patterns, and optimizer-driven prompt/example evolution for reliable outputs. Concrete use case: build a QA program that defines input/output schemas, runs multiple generator branches for self-consistency, and merges results with guards and rewards.
Use Synalinks when you need predictable, schema-driven LLM outputs (RAG, function-calling, or tool-enabled agents), or when you want to treat LLM pipelines like trainable programs (optimizers, rewards, and program checkpoints). Ideal for structured-output tasks, RAG/KAG workflows, and programmatic agent tooling.
Targets LLM-based agent runtimes and tool-enabled agents (OpenAI-style chat models, Claude, Ollama/Mistral local runtimes, OpenRouter-backed models).
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