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Building production AI agents with DSPy is straightforward—until you need multiple specialized submodules, each with its own tools and behaviors. At...
Building reliable LLM systems isn’t about chasing a “magic prompt” — it’s about a disciplined, iterative development cycle. DSPy treats LLM pipelines...
Building a research agent with CodeAct where the LLM generates Ruby code on the fly.
In complex agent systems, you might have chained multiple prompts together. You can provide all these prompts together for GEPA to consider and...
This paper reformulates prompt engineering as a classical state-space search, treating prompts as "states" and edits as "transitions." By using...
A tool that serves DSPy programs as HTTP APIs with Docker config, OpenAPI specs, MCP support, and more.
DSPy Pune meetup, December 13, 2025, 4 p.m. and DSPy Bengaluru - Quarterly Meetup, December 20, 2025, 10 a.m.
Show a video on integrated output with claude desktop.
Codex-Agent is a module that wraps the OpenAI Codex SDK in DSPy signatures for type-safe, stateful coding agents. Each instance maintains its own...
Koantek’s AscendAI Agent Factory redefines how enterprises build and deploy AI agents on Databricks. Powered by Agent Bricks and DSPy’s “programming...
DSPy is a research paradigm. We're not trying to building whatever reaches the largest userbase. We're trying to realize a very specific vision and...
The paper introduces Agent-Omni, a framework that overcomes current limitations of multimodal large language models (MLLMs), which typically support...
This talk explores various automatic prompt optimization approaches, ranging from simple ones like bootstrapped few-shot to more complex techniques...
Talk is in Japanese by Tomu Hirata from Databricks In this study session, we will introduce the prompt optimization framework DSPy. This is a...
A comprehensive, production-ready framework for Retrieval-Enhanced Fragmented Reasoning and Generation (REFRAG) that revolutionizes how large...
Build and test signatures interactively. Create multiple cells to experiment with different prompts and see results side by side.
In this blog post, I share my hands-on comparison of DSPy and LlamaBot for building structured LLM applications, using a real-world expense...
Butter is a cache that identifies patterns in LLM responses and saves you money by serving responses directly. It's also deterministic, allowing...
I find it even more painful than usual when something in the DSPyverse is overly complex or insufficiently simplified.
Checked the transcript and there is a short conversation around DSPy. Worth checking for our Japanese readers.