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Modaic is the platform for DSOps: DevOps for DSPy programs. We’re building the infrastructure for software 3.0, where agents autonomously improve...
Adding Memory, Production UI, and Multi-Step Reasoning How we evolved from a stateless prototype to a production-ready intelligence system with...
This tutorial demonstrates how to use MLflow to track and analyze your DSPy optimization process. MLflow's built-in integration for DSPy provides...
Darin Kishore explores how combining Anthropic’s Model Context Protocol (MCP) with the DSPy framework can make for better AI workflow development....
A walkthrough of building a product description generator using DSPy, Azure OpenAI, and MLflow. We'll explore how GEPA optimization works with a...
Lessons from building an AI-assisted database debugging platform. Mentions using DSPy.
Learn lightweight context engineering in Ruby. We'll incrementally build a chat agent with ephemeral memory and cost-based routing—starting from the...
This paper introduces an autonomous AI system that automates data abstraction from pathology reports for cancer registries. Key Highlights: - High...
Superagentic AI is proud to announce the DSPy Code, the comprehensive CLI to build and optimize your DSPy and GEPA code. DSPy Code is now live: an...
Found the DSPy / GEPA corner at the Anthropic booth with @tarunsachdeva and @thomastjoshi
The text introduces Feedback Descent, a framework for optimizing text-based items (like prompts, code, or molecules) using detailed, structured...
In this walkthrough I break down the SPLASH 2024 paper on meaning-typed programming and show how its type-driven approach can remove a ton of brittle...
Let your AI use tools to answer questions - the ReAct (Reasoning + Acting) pattern in ax-llm, DSPy
DSPydantic: Auto-Optimize Your Pydantic Models with DSPy | Language: Python | License: Apache License 2.0
Join us for an evening of talks, discussion, and connection at the Bay Area DSPy Meet Up! We've got an incredible lineup of speakers: Lakshya...
Researchers introduce SAVANT, a structured reasoning framework that enhances anomaly detection in driving scenes using vision-language models (VLMs)....
Self-paced bootcamp on Generative AI. Tutorials on ML fundamentals, Ollama, LLMs, RAGs, LangChain, LangGraph, Fine-tuning, DSPy & AI Agents (CrewAI),...
In this episode, Vicente discusses his journey in developing DSPy for Ruby, the significance of BAML in AI development, and the challenges of porting...
A modular, production-ready example of using GEPA (Generative Evolutionary Prompt Adaptation) to optimize prompts in DSPy.
This notebook will illustrate how to create an AI system with DSPy that uses Parallel's Chat with the Web API and Weaviate's Query Agent.