Synalinks

Synalinks is an open-source, Keras-inspired framework for building neuro-symbolic LM applications (RAGs, agents, self-evolving reasoning) with in-context reinforcement learning. This skill is the single entry point: each section below is a scannable overview that points to a runnable script + its captured run log and a deep-dive reference doc under references/.

Core abstractions

• DataModel, Pydantic-style schema for structured I/O (replaces tensors).
• Module, async computational unit processing JSON data (replaces layers).
• Program, DAG of modules with conditional logic (replaces models).
• Reward / Optimizer, guide training by maximizing reward (no gradients; prompts/examples/plans are the trainable variables).
• DecisionModel, answers typed questions (yes/no, choice, score) with calibrated probabilities instead of generating text; used for decisions.

Universal gotchas (apply everywhere)

1. All module constructors are keyword-only (* after self). Call Generator(data_model=Answer, language_model=lm), never positionally. The one exception is Tool(func, *, ...), func stays positional.
2. instructions MUST be a string, never a list, passing a list raises ValidationError.
3. All call() methods are async, await every module/program call.
4. language_model/embedding_model may be omitted if a process-wide default is set (set_default_language_model(...)); resolved at call time.
5. Always check for None results, a failed LM call or a non-activated branch yields None.
6. A DecisionModel is never a language_model, passing one as language_model raises. Modules that can decide take decision_model=.

Quick Start

import synalinks, asyncio

class Query(synalinks.DataModel):
    query: str = synalinks.Field(description="The user query")