Python SDK for Agent AI Observability, Monitoring and Evaluation Framework. Includes features like agent, llm and tools tracing, debugging multi-agentic system, self-hosted dashboard and advanced analytics with timeline and execution graph view

An exporter of openclaw traces to langfuse using logs

A Streamlit-based AI assistant generates custom Streamlit app code from user-provided images or text using the Google Gemini model.

Site Sleuth is a chatbot that assists users in efficiently using websites by analyzing and answering questions provided through a link.

Open-source evaluation and observability platform for AI agent applications.

🤖 Build advanced AI agents with a collection of production-ready applications using modern frameworks for single and multi-agent systems.

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

Single-binary, MLflow-API-compatible experiment tracker with first-class LLM trace support. 143× faster cold start than MLflow.

🎛️ Build efficient workflows with Deevs' Agent System, a plugin marketplace for Claude Code featuring multi-session chains and terminal control.

Drop-in observability for LLM apps — quality scoring, hallucination detection, cost tracking and agent debugging

LangSmith tracing plugin for opencode — captures sessions, LLM calls, tool calls, reasoning, and context compaction. Trace taxonomy compatible with langsmith-claude-code-plugins.

Mr.Dhamodharan's Curriculum vitae

Execution trace schema for AI agent pipelines.

This project aims to build a Coding Agent for the energy market. Based on the user’s goals and the tools provided, the coding agent intelligently selects and executes the appropriate actions to complete the task.

Developer focused LLM experimentation and debugging server for testing prompts, tools, MCP integrations, traces, and scheduled AI workflows across models

WhyOps - AI Agent Observability & Evaluation Platform

Implement a configurable neuromorphic processor with LIF neurons, STDP learning, and RISC-V management for FPGA-based neural computing.

Self-hosted control plane for AI agent teams. Push objectives at Claude Code or OpenAI Codex; capture every LLM call.

A Python proof-of-concept for tracing multi-turn Agent-to-Agent (A2A) conversations as a single unified MLflow trace for LLM observability and evaluation.