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Build using DSPy.
GEPA, or Genetic-Pareto, is a sample-efficient optimizer based on three principles: Genetic evolution, Pareto filtering, and Reflection using natural...
Multi-Faceted AI Agent and Workflow Autotuning. Automatically optimizes LangChain, LangGraph, DSPy programs for better quality, lower execution...
In-Context Learning for eXtreme Multi-Label Classification (XMC) using only a handful of examples. | Language: Python | License: MIT License
This course teaches you how to use DSPy to build and optimize LLM-powered applications. You’ll write programs using DSPy’s signature-based...
GEPA (Genetic-Pareto) is a framework for optimizing arbitrary systems composed of text components—like AI prompts, code snippets, or textual...
The Ruby framework for programming—rather than prompting—language models. | Language: Ruby | License: Other
Strwythura: construct an entity-resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology...
A DSPy Adapter for exact-fidelity prompt templates with full control over messages. | Language: Python | License: MIT License
This article teaches how to develop authentic AI-generated voice using DSPy and genetic-Pareto optimization to systematically explore a...
This page offers an advanced guide for optimizing DSPy programs using dspy.GEPA, focusing on two key customization features. You can create custom...
For more control over the tool calling process, you can manually handle tools using DSPy's tool types.
DSPy Go implementation | Language: Go | License: MIT License
Extract structured data from any content using LLMs. | Language: Python | License: MIT License
WIP - Allows you to create DSPy pipelines using ComfyUI | Language: Python | License: MIT License
Dzmitry Pletnikau presents the paper "Reflective Prompt Evolution Can Outperform Reinforcement Learning" (GEPA). He situates the method within the...
Performance centered DSPy rewrite to(not port) Rust | Language: Rust | License: Apache License 2.0
Reproducibility Study of “InPars Toolkit: A Unified and Reproducible Synthetic Data Generation Pipeline for Neural Information Retrieval” This...
A framework for optimizing DSPy programs with RL | Language: Python | License: MIT License
Mike is a "multi-hyphenate" tech entrepreneur who bridges the gap between marketing and AI engineering. Former founder of ... | Channel: Information...