The open LLM Ops platform - Traces, Analytics, Evaluations, Datasets and Prompt Optimization ✨

The pretty much "official" DSPy framework for Typescript

Unified Go interface for Language Model (LLM) providers. Simplifies LLM integration with flexible prompt management and common task functions.

Multi-Faceted AI Agent and Workflow Autotuning. Automatically optimizes LangChain, LangGraph, DSPy programs for better quality, lower execution latency, and lower execution cost. Also has a simple agent/workflow framework

WIP - Allows you to create DSPy pipelines using ComfyUI

Construct knowledge graphs from unstructured data sources, use graph algorithms for enhanced GraphRAG with a DSPy-based chat bot locally, and curate semantics for optimizing AI app outcomes within a specific domain.

DSPy Go implementation

Experimental Code for StructuredRAG: JSON Response Formatting with Large Language Models

The Ruby framework for programming—rather than prompting—language models.

Simple Graph Memory for AI applications

Minimal agent runtime built with DSPy modules and a thin Python loop. Includes CLI, FastAPI server, and eval harness with OpenAI/Ollama support.

Build your own RAG and run it locally on your laptop: ColBERT + DSPy + Streamlit

prompt engineering experiments with DSPy GEPA and TextGrad

Quickly get up and running with Event Sourcing and Agentic workflows in Clojure

Extract structured data from any content using LLMs.

This codebase demonstrates various DSPy functionalities through practical examples.

Explore the use of DSPy for extracting features from PDFs 🔎

State-of-the-art prompting techniques implementation with DSpy - Manager-style prompts, role personas, meta-prompting, and more

DSPy program/pipeline inspector widget for Jupyter/VSCode Notebooks.

A framework for pitting LLMs against each other in an evolving library of games ⚔