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

Strwythura: construct a knowledge graph from unstructured data sources, organized by results from entity resolution, implementing an enhanced GraphRAG approach, and also implementing an ontology pipeline plus context engineering for optimizing AI application outcomes within a specific domain

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

DSPy Go implementation

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

Simple Graph Memory for AI applications

Adaptive Agentic AI Reasoning using Microsoft Agent Framework -- Join the Discord for suggestion or support ! https://discord.gg/ebgy7gtZHK

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

Extract structured data from any content using LLMs.

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

prompt engineering experiments with DSPy GEPA and TextGrad

dspy-cli is a tool for creating, developing, testing, and deploying DSPy programs as HTTP APIs.

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

This codebase demonstrates various DSPy functionalities through practical examples.

Explore the use of DSPy for extracting features from PDFs 🔎

ZON → 35-70% cheaper LLM prompts than JSON/TOON. Zero overhead.