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.
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
Quickly get up and running with Event Sourcing and Agentic workflows in Clojure
prompt engineering experiments with DSPy GEPA and TextGrad
Extract structured data from any content using LLMs.
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
A framework for pitting LLMs against each other in an evolving library of games ⚔
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