Awesome Infra for AI › Prompt Management

SynaLinks/synalinks

⭐ 462 Python repository created 2025-02-03

SynaLinks is a neuro-symbolic Language Model (LM) framework designed to simplify the development, training, evaluation, and deployment of complex LLM applications. It provides a clean, declarative API, similar to Keras, allowing users to compose modules, optimize them with in-context reinforcement learning, and deploy them as REST or MCP APIs. The framework emphasizes progressive complexity, neuro-symbolic learning paradigms that combine logic, structure, and language models, and in-context optimization to improve model reasoning without traditional weight retraining. Key features include prompt/anything optimization via in-context RL, versionable and JSON-serializable pipelines, constrained structured outputs (JSON) for correctness, automatic async and parallel execution, and built-in metrics, rewards, and datasets. SynaLinks seamlessly integrates with various LLM providers through LiteLLM and offers embeddable fast knowledge base support based on DuckDB. It's API-ready with FastAPI and FastMCP deployment options, compatible with KerasTuner for hyperparameter search, and includes callbacks and hooks for observability, such as an MLflow Monitor callback. Distinguishing itself from other frameworks like DSPy, SynaLinks supports optimization of any variable, not just prompts, operates with async by default, handles parallel branches with asyncio, employs logic-based Python operators for data model manipulation and control flow, and utilizes constrained JSON decoding for production robustness. It is fully compatible with Pydantic BaseModel, easing integration with existing services, and provides introspection tools like `summary()` and `plot_program()` for documentation and understanding.

https://github.com/SynaLinks/synalinks

neuro-symbolic-aillmopsagent-orchestrationin-context-learningreinforcement-learningragllm-deploymentllm-evaluationprompt-managementllm-servingapi-generation