synalinks

# Synalinks Framework Synalinks is an open-source Keras-inspired framework for building neuro-symbolic LLM applications with in-context reinforcement learning. ## Core Concepts - **DataModel**: Pydantic-style schema defining structured I/O (replaces tensors) - **Module**: Computational unit processing JSON data (replaces layers) - **Program**: DAG of modules with conditional logic (replaces models) - **Rewards**: Guide training (maximize reward, not minimize loss) - **Optimizers**: Update prompts/examples via LLM reasoning (no gradients) ## Quick Start ```python import synalinks import asyncio class Query(synalinks.DataModel): query: str = synalinks.Field(description="The user query") class Answer(synalinks.DataModel): answer: str = synalinks.Field(description="The answer") async def main(): lm = synalinks.LanguageModel(model="ollama/mistral") inputs = synalinks.Input(data_model=Query) outputs = await synalinks.Generator( data_model=Answer, language_model=lm, )(inputs) program = synalinks.Program( inputs=inputs, outputs=outputs, name="simple_qa", description="A simple Q&A program", ) result = await program(Query(query="What is the capital of France?")) print(result.prettify_json()) asyncio.run(main()) ``` ## Four Ways to Build Programs ### 1. Functional API (Recommended for most cases) ```python inputs = synalinks.Input(data_model=Query) outputs = await synalinks.Generator(d...