The platform for LLM evaluations and AI agent testing
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.
A systematic AI Agent development tutorial covering LLM agents, RAG, tool use, memory systems, multi-agent systems, LangChain, LangGraph, MCP, and agentic RL.｜从零开始学 AI Agent 开发 | 系统、全面、实战导向的 Agent 开发教程 | 每日自动追踪 arXiv 最新论文 | Learn AI Agent Development from Scratch
Production focused Self-harnessed LM runtime (RLM) that allows the LM to call its sub-lm with DSPy signatures. Define your inputs, outputs, and tools — the model handles its own control flow. Get fully interpretable trajectories and performance that scales directly with model improvements. Without context rot.
A multi-agent LLM system for detecting and resolving cognitive dissonance.
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
Automated generation of comprehensive Agents.md for LLMs, driven by the DSPy Recursive language model implementation.
The Ruby framework for programming—rather than prompting—language models.
Strwythura: construct an entity-resolved knowledge graph from structured data sources and unstructured content sources, implementing an ontology pipeline, plus context engineering for optimizing AI application outcomes within a specific domain. This produces a Streamlit app, with MLOps instrumentation.
A comprehensive 0-to-1 guide for building self-improving LLM applications with DSPy framework
WIP - Allows you to create DSPy pipelines using ComfyUI
dspy-cli is a tool for creating, developing, testing, and deploying DSPy programs as HTTP APIs.
Extract structured data from any content using LLMs.
Collection of Claude Skills for DSPy framework - program language models, optimize prompts, and build RAG pipelines systematically
Experimental Code for StructuredRAG: JSON Response Formatting with Large Language Models
Transform an Obsidian vault into PARA + Zettelkasten structure using DSPy and RLM
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