When Philosophy meets AI

An MCP Multimodal AI Agent with eyes and ears!

Python command-line tool for interacting with AI models through the OpenRouter API/Cloudflare AI Gateway, or local self-hosted Ollama. Optionally support Microsoft LLMLingua prompt token compression

AI Agent built with Google ADK that leverages Google Maps MCP Server to answer real-world location questions with tool usage and traceable execution via Opik.

Community-maintained PHP SDK for Opik - an LLM observability and evaluation platform.

Building Production-Ready AI Agent Evaluation with Opik MCP Server on AWS AgentCore

🔐 Curated OSINT toolkit for cybersecurity investigations, threat analysis, and public data mapping

A one-stop repository of resources for AI Product Managers and Engineers. Contains code for Evals, prompt templates, Claude skills, and much more!

In this we implement opik llm evaluation metrics on medical data analyzer

Reproducibility code for “Evaluating the Performance of Large Language Models in Taxonomic Classification of Questions in Verbal Protocols of Design” (AI EDAM submission; under review). [WIP]

An autonomous AI Agent that uses Computer Vision and LLM reasoning to monitor focus, "shame" distractions, and ensure your 2026 productivity resolutions actually stick.

tangkurak4you

Project Vyasa is a local-first research execution framework for DGX Spark that helps researchers, journal authors, and domain experts turn unstructured documents into defensible, evidence-bound manuscripts for high-stakes, long-running inquiry. It keeps humans in control of judgment while AI handles extracting, validating, and governing evidence.

A wellness-focused calorie tracker with multi-agent AI architecture for meal analysis and energy balance tracking.

MLOps-driven LLM RAG assistant that learns your writing style from your online content, with an FTI pipeline (Features → Training → Inference) , RAG for context grounding, and ZenML orchestration.

MIT 6.S191 Lab 3 teaches you how to fine-tune large language models like Gemma 2B, structure prompts, and evaluate outputs using tools like Opik and LFM-40B.

A complete Agentic RAG infrastructure using ZenML pipelines including ETL, Dataset generation, Embedding, Inference and Evaluation

Developing Agentic AI with Opik