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Introduces Flex, a new DSPy module that lets language models rewrite the program code itself rather than just prompts, enabling better optimization, fewer model calls, and safer execution via sandboxing.
Flex is a new DSPy feature where optimizers can replace LLM calls with deterministic code, sometimes improving accuracy while drastically reducing the number of LLM calls needed.
DSPy 3.3.0 is released, featuring dspy.Flex for GEPA-optimized code and prompts, and dspy.ReActV2 with native parallel tool calling, with praise for lead maintainer Isaac Miller.
Using the DSPy framework, the author evaluates and improves the system prompts for Datasette Agent's SQL query feature, identifying issues such as column-name guessing and error-retry loops.
Maxime Rivest announces a main stage keynote for DSPy at the AI Engineer conference, aiming to grow the tool's accessibility and invites feedback.
A tweet recommends watching Angie Jones' keynote on building AI agents, highlighting core engineering principles and mentioning Python frameworks like DSPy, Functai, and Mirascope.
A practical guide explaining three levels of building self-improving AI agents, from manual loops to automated design, with recommended tools and frameworks.
Discussing a figure from an essay on AI's impact on software engineering that highlights irreducible complexity and predicts evolution of software engineering, with DSPy cited as an early example.
The article explains how GEPA (Genetic-Pareto Optimization) within DSPy is used for efficient prompt tuning, specifically applied to pretraining data curation at Microsoft AI, allowing researchers to replace manual prompt engineering with automated compute-driven optimization.
GEPA-optimized LLM judges from dspy are used for data filtering in Microsoft's MAI-Thinking-1 model pre-training pipeline.
The author compares Structural Equation Modeling, Neural ODEs, and AI Programs like DSPy as declarative frameworks for defining and optimizing computational graphs, arguing that structured flows are essential for trustworthy AI agents.
Ax is an open-source TypeScript library that implements DSPy-style typed signatures and agent frameworks for building reliable AI applications with minimal prompting. It supports multiple LLM providers and includes features like agents, flows, RAG, and self-improving pipelines.
DSPy has a new front page and documentation for easier onboarding, and is approaching a major DSPy 4.0 release with radical new ideas.
Ported the PEEK method to DSPy, allowing any DSPy agent to benefit from improved performance and cost reduction as demonstrated in the linked paper.
PEEK feature is coming to ax-agent, a TypeScript library for automatic prompt generation and AI agents, supporting multiple providers.
Manning Books announces a new early access book 'Building LLM Applications with DSPy', teaching how to use the DSPy framework to optimize LLM prompts with Python. The book is 50% off through June 3rd.
kg-gen is an open-source Python package that uses language models (via LiteLLM and DSPy) to extract knowledge graphs from plain text or conversation messages, featuring chunking, clustering, and flexible provider routing.
MaximeRivest explains DSPy's five core components—Optimizers, Signatures, LMs, Modules, and Adapters—and argues that effective AI engineering requires mastering these elements, highlighting the often-overlooked role of rendering structured outputs.
ROMA is a recursive multi-agent framework built on DSPy, designed to solve complex reasoning tasks through a hierarchical recursive structure. It supports task decomposition, parallel processing, and multiple LLM providers.
Maxime Rivest argues that compound AI systems for images are undervalued and suggests leveraging optimization frameworks like DSPy and GEPA to automate pipeline creation involving SAM and classifiers.