Awesome GitHub RepositoriesAgent Execution Traces

Spring AI is an application framework for Java that provides a portable, fluent API for integrating AI models, tools, and vector stores into applications. It wraps multiple AI providers behind a common interface, allowing developers to switch between chat, embedding, image, and speech models without changing application code. The framework includes a chainable chat client API similar to WebClient or RestClient, supports both synchronous and streaming interactions, and offers structured output conversion that transforms unstructured AI responses into strongly-typed Java objects. The framework

IntentKit is an open-source platform for deploying and managing a collaborative team of AI agents that can work together to complete complex tasks. It provides a self-hosted agent orchestrator that coordinates multiple agents through a modular pipeline of entrypoints, orchestration, and storage, all running as containerized services using Docker Compose or Swarm for production-grade deployment. The platform distinguishes itself by offering a plugin-based system for extending agent capabilities without modifying the core codebase, along with built-in integrations for connecting agents to socia

NeMo-Guardrails is a toolkit for adding programmable safety constraints and dialogue boundaries to large language model conversational systems. It functions as security middleware that intercepts inputs and outputs to block prompt injections, jailbreaks, and sensitive data leaks, while providing a conversational dialogue manager to define structured interaction flows through configuration files. The framework includes a hallucination filter to screen model outputs for factual accuracy and a specialized modeling language for defining conversational flows and constraints. It provides capabiliti

Open Multi-Agent is a TypeScript framework for multi-agent orchestration that decomposes natural language goals into a runtime-generated directed acyclic graph of tasks. It functions as a task orchestrator and workflow state manager, coordinating multiple AI models to execute parallel and sequential operations. The framework is distinguished by a proposer-judge consensus protocol used to validate agent outputs through a quorum of agreement. It employs provider-agnostic model routing to assign specific models to tasks based on roles or execution phases and utilizes state-based workflow checkpo

This is an open-source Python SDK for building and orchestrating production-grade AI agents. It provides a unified framework for creating conversational agents that can use tools, maintain state, and coordinate across multiple language model providers including OpenAI, Anthropic, Google, Amazon Bedrock, and locally-hosted models. The SDK supports multi-agent orchestration through graphs, teams, and swarms, allowing several specialized agents to collaborate on complex tasks. Agents can be composed as callable tools that other agents invoke, and the framework includes policy handlers that inspe

Coze-loop est une plateforme d'optimisation et une suite de gestion d'orchestration pour les agents de grands modèles de langage (LLM). Elle fonctionne comme un environnement complet pour le développement, le débogage, l'évaluation et la surveillance des performances des agents IA. Le projet fournit un terrain de jeu dédié au prompt engineering pour l'itération et la validation en temps réel des réponses du modèle. Il inclut un framework d'évaluation qui exécute des évaluations automatisées sur des jeux de données pour générer des métriques de performance et vérifier la précision des sorties. Le système couvre l'observabilité via le traçage d'exécution en temps réel et l'analyse historique du comportement des agents. Il prend en charge la gestion du cycle de vie avec des capacités de débogage distribué, de configuration des paramètres du modèle et de personnalisation du déploiement en cluster.

ZenML is an extensible machine learning orchestration framework designed to manage the end-to-end lifecycle of data pipelines and AI agent workflows. It functions as a durable orchestrator that executes machine learning tasks as directed acyclic graphs, ensuring that every step is containerized for consistent performance across local, cloud, and hybrid infrastructure. By decoupling pipeline code from underlying compute and storage backends, the platform allows developers to define infrastructure-agnostic stacks that remain portable across diverse environments. The project distinguishes itself

Youtu Agent is an open-source framework for building, running, and evaluating autonomous agents powered by large language models. It provides the core infrastructure for creating agents that follow reasoning loops, use toolkits, and coordinate with other agents to solve complex tasks, all managed through YAML-driven configuration files. The framework distinguishes itself through its support for multi-agent orchestration, where a planner agent decomposes tasks and coordinates specialized worker agents, and through its integration with the Model Context Protocol for connecting to external toolk

Pandera is a data pipeline validation framework and statistical type validation tool. It functions as a library for defining and enforcing schemas on datasets to ensure data quality and consistency, specifically providing validation capabilities for Pandas dataframes. The project includes a schema inference tool that automates setup by analyzing existing dataset samples to generate validation schemas. It also serves as a synthetic data generator, creating artificial datasets based on predefined schemas to verify data-producing functions. The framework covers data engineering quality assuranc

Ce projet est une pile de surveillance IA auto-hébergée qui fonctionne comme une plateforme d'observabilité LLM, un framework d'évaluation IA et un analyseur de traces OpenTelemetry. Il est conçu pour capturer et analyser les traces, sessions et télémétries LLM afin de surveiller les performances des agents IA. La plateforme se distingue en tant que serveur Model Context Protocol, exposant les fonctions de l'espace de travail comme outils pour les agents de codage IA. Elle permet la conversion des traces de production défaillantes en jeux de données de test pour les tests de régression et utilise le clustering de sessions basé sur la sémantique pour découvrir les modèles de comportement émergents des utilisateurs. Le système couvre de larges domaines de capacités, notamment la collecte de télémétrie pour les chemins d'exécution des agents, le scoring d'évaluation automatisé pour le trafic en direct et la recherche sémantique pour isoler les modèles d'interaction. Il fournit également des alertes pour les régressions de signaux, l'analyse comportementale pour les échecs d'outils et la rédaction de PII pour les données de télémétrie. Le logiciel peut être déployé sur une infrastructure privée en tant qu'installation à hôte unique ou en tant que cluster évolutif utilisant Docker Compose, Kubernetes ou des charts Helm.

Agenta is a Prompt Ops lifecycle manager and prompt management platform that decouples prompt engineering from application code. It serves as a centralized system for developing, versioning, and deploying prompt templates and model configurations across different environments. The platform functions as an AI agent orchestrator with a visual interface for building agent workflows and connecting models to external tools. It further acts as an evaluation framework and observability tool, utilizing OpenTelemetry to capture execution traces, monitor latency, and track token costs. The system cove

ruby_llm is an LLM integration framework and AI agent orchestrator designed to connect applications to multiple large language model providers through a unified interface. It serves as a toolkit for building autonomous assistants with custom personas, managing structured output via JSON schemas, and implementing vector embedding engines for semantic search. The project distinguishes itself as an observability suite and multimodal toolkit. It provides specialized capabilities for tracking token usage, calculating model costs, and tracing workflows via OpenTelemetry, while supporting the proces

This project provides a translation layer and set of adapters designed to bridge AI agents with the Model Context Protocol. It functions as an integration layer that allows agents to operate as protocol-compliant servers and enables the conversion of protocol-based tools into formats compatible with agent frameworks and logic graphs. The adapters facilitate tool interoperability by wrapping external protocol tools for use within agent workflows and exposing internal agent capabilities to any client implementing the Model Context Protocol. This creates a communication bridge that supports inte

Lmnr is an LLM observability platform and evaluation framework designed for tracing, logging, and monitoring language model executions. It provides the tools necessary to debug agent behavior, analyze performance, and identify failure patterns in AI agents. The platform differentiates itself through a trace-to-dataset pipeline that converts production logs into labeled test sets for regression testing. It includes a prompt-variant replay engine to compare different prompts or models side-by-side and a state-cached debugging system to replay agent loops without restarting the process. The sys