Awesome GitHub RepositoriesAgent Framework Integrations

Agno is an agent operating system designed to manage the lifecycle, tool execution, and persistent state of autonomous agents across distributed infrastructure. It provides a unified runtime environment that wraps diverse agent frameworks into a consistent, interoperable protocol, allowing developers to build and deploy complex multi-agent systems that coordinate tasks and delegate sub-processes. The platform distinguishes itself through a robust governance and orchestration layer that includes human-in-the-loop approval gates, role-based access control, and a centralized API gateway. It feat

Deepagents is an LLM agent orchestration platform and stateful application server designed for deploying and managing AI agents built with computational graphs. It provides a containerized runtime environment that handles agent execution, state persistence, and the versioning of AI assistants. The platform distinguishes itself through deep integration with the Model Context Protocol, allowing agents to function as servers that expose tools and capabilities to external clients. It features a sophisticated observability suite for capturing execution traces, performing LLM-based evaluations agai

Memori is an AI agent memory middleware platform designed to provide persistent, context-aware recall for language models. It functions as a non-intrusive layer that intercepts outbound model requests to automatically capture interaction history and execution traces, ensuring that agents maintain continuity across sessions without requiring modifications to existing application logic. The platform distinguishes itself through a dual-model storage architecture that maintains information as both structured relational primitives for precise fact retrieval and rolling narrative summaries for situ

Agent Lightning is an optimization framework designed to refine the performance of individual AI agents within complex multi-agent systems. It provides a platform for improving decision-making and task execution by applying reinforcement learning, supervised fine-tuning, and automated prompt optimization. The framework distinguishes itself through its ability to isolate specific agents for targeted tuning, allowing developers to enhance individual behaviors while maintaining the stability of the broader system architecture. By utilizing a modular interface, it integrates with diverse agent fr

DataHub is a metadata management platform designed to unify technical, operational, and business context across diverse data ecosystems. By utilizing a graph-based metadata model and an event-driven ingestion architecture, it creates a centralized source of truth that maps complex data relationships, lineage, and ownership. This foundational framework enables organizations to maintain a synchronized view of their data landscape, supporting both human-led discovery and automated data operations. The platform distinguishes itself through its focus on grounding artificial intelligence and autono

This project is an artificial intelligence gateway that functions as a centralized middleware layer for managing, securing, and observing interactions with language, vision, and audio models. It provides a unified interface that standardizes requests across multiple providers, enabling teams to integrate AI capabilities into their applications through a consistent set of tools and protocols. The gateway distinguishes itself through its comprehensive infrastructure governance and traffic management capabilities. It allows for policy-driven routing, automated failover, and load balancing across

Promptfoo is an evaluation framework designed for testing, benchmarking, and red-teaming language models and agentic workflows. It provides a unified environment to run prompts against multiple providers, allowing developers to systematically validate model outputs against objective assertions, semantic similarity metrics, and custom grading rubrics. The platform distinguishes itself through a provider-agnostic execution layer and a stateful orchestrator capable of simulating multi-turn conversations and complex tool-use trajectories. It includes a dedicated adversarial mutation pipeline that

This project is a platform for the deployment of open source large language and multimodal models. It provides a unified interface to serve text, image, and speech models across local or cloud hardware. The system enables distributed AI inference by orchestrating model workloads across multiple nodes and devices. It includes a unified API adapter layer to standardize inputs and outputs, as well as tools for multimodal chat and structural image generation. The platform covers a broad capability surface including request batching for throughput optimization, dynamic model loading, and integrat

53AIHub is a centralized orchestration platform for deploying and managing AI agents and prompts across multiple large language model providers. It functions as a multi-model AI gateway and an operation portal for AI services, providing a unified interface to coordinate agents and prompts from various external platforms. The project distinguishes itself as a white-label AI portal designed for self-hosted infrastructure, allowing for full control over operational data on private servers or containers. It includes a comprehensive AI SaaS administration layer with a multi-tenant subscription eng

AgentOps este o platformă de observabilitate și un toolkit pentru dezvoltatori, destinat monitorizării execuției, performanței și fiabilității agenților autonomi bazați pe modele de limbaj mari (LLM). Servește drept sistem pentru urmărirea comportamentului agenților AI, depanarea fluxurilor de lucru complexe și benchmarking-ul performanței modelelor. Platforma se distinge prin capacitatea de a vizualiza fluxurile de lucru multi-agent prin grafuri ale căilor de execuție și reluări de sesiune. Oferă instrumente specifice pentru calcularea costurilor financiare la diverși furnizori de modele de limbaj și suportă un stack de observabilitate self-hosted pentru utilizatorii care necesită control total asupra datelor lor pe hardware sau cloud privat. Sistemul acoperă un set larg de capabilități, inclusiv detectarea erorilor agenților, analiza utilizării instrumentelor și urmărirea metricilor de performanță personalizate prin etichetarea evenimentelor. Se integrează cu framework-uri AI pentru a captura telemetria și datele de performanță.

rllm is an asynchronous reinforcement learning framework for training language agents. It provides a unified pipeline that runs the same agent code for both evaluation and training, automatically capturing traces for gradient computation. The framework supports distributed reinforcement learning across multiple GPUs and nodes using pluggable backends, and executes agents in isolated sandboxes—either locally or in the cloud—for safe and scalable rollout collection. It trains agents built with LangGraph, SmolAgents, OpenAI Agents SDK, or custom frameworks without requiring core logic changes. T

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

ZenML is an orchestration platform designed for building, deploying, and monitoring reproducible machine learning pipelines and agentic workflows. It provides a unified framework that manages the entire lifecycle of machine learning assets, from data processing and model training to the deployment of persistent inference services. By decoupling pipeline logic from underlying compute and storage, the platform enables teams to transition workflows seamlessly from local development environments to production-grade cloud infrastructure. The platform distinguishes itself through a service-oriented

AIOS is an LLM agent operating system and orchestration kernel designed to manage memory, resource scheduling, and tool execution for multiple autonomous AI agents. It serves as a comprehensive framework for developing and deploying agents, featuring a dedicated resource manager that coordinates model backends, GPU memory, and isolated kernel instances. The system distinguishes itself through a semantic memory engine that uses vector search and autonomous clustering for long-term knowledge management, and a semantic file system that allows users to control computer files and system operations

MemMachine is a centralized memory management server and model-agnostic memory layer for large language models. It functions as a persistence layer that stores user profiles and conversational context, providing a decoupled data store that prevents vendor lock-in by serving different AI models through a consistent API. The system implements the Model Context Protocol to share persistent agent memories and session data with compatible AI clients. It utilizes a multi-tiered memory hierarchy, combining a graph-based conversation store for episodic interactions with a vector knowledge base for se

TypeDB este o bază de date graf și un sistem de gestionare a cunoștințelor (knowledge graph) puternic tipizat. Servește ca un magazin de date multi-model care unifică structurile relaționale, document și graf într-un singur mediu, funcționând atât ca o bază de date conformă ACID, cât și ca un motor de interogare declarativ. Sistemul se distinge prin utilizarea modelării n-ary hypergraph și a ierarhiilor de tip polimorfice. Utilizează o schemă puternic tipizată pentru a impune reguli structurale și a valida integritatea datelor, permițând inferența polimorfică bazată pe tip și polimorfismul de interfață bazat pe roluri pentru a rezolva automat relațiile complexe în timpul execuției interogărilor. Platforma acoperă o gamă largă de capabilități, inclusiv calcularea relațiilor recursive prin tabling, tranzacții cu izolare de snapshot și regăsirea declarativă a datelor. De asemenea, suportă disponibilitatea ridicată prin replicarea clusterelor bazată pe consens, controlul accesului bazat pe roluri și integrarea cu agenți AI pentru regăsirea datelor structurate. Gestionarea este susținută printr-o interfață de linie de comandă, iar sistemul oferă instrumente pentru vizualizarea schemelor graf și auditarea activității administrative.

Memgraph is an in-memory, distributed graph database designed for high-performance labeled property graph management. It utilizes a Cypher query engine for declarative data retrieval and manipulation, providing a scalable knowledge graph backend that integrates vector search and graph traversals. The system distinguishes itself as a real-time graph analytics platform, employing native C++ and CUDA implementations to execute complex network analysis and dynamic community detection on streaming data. It provides specialized support for AI integration, including GraphRAG capabilities, the constr

Atmosphere is a Java-based framework for building and coordinating AI agents. It provides a real-time transport layer for streaming data via WebSockets, SSE, gRPC, and WebTransport, alongside a multi-agent orchestration framework for managing agent fleets through sequential, parallel, and graph-based execution workflows. The project features a durable workflow engine that persists agent state as snapshots, allowing long-running tasks to survive system restarts and incorporate human-in-the-loop approvals. It also implements Model Context Protocol servers to expose tools, resources, and prompt

Acest proiect este un framework pentru dezvoltarea și orchestrarea agenților software autonomi în cadrul aplicațiilor bazate pe JVM. Oferă un toolkit pentru încorporarea inteligenței artificiale direct în logica de business, permițând agenților să execute sarcini complexe prin planificare dinamică, orientată spre obiective, în loc de mașini de stare rigide. Prin utilizarea adnotărilor declarative, framework-ul permite dezvoltatorilor să definească capabilitățile agenților și să îi integreze în modelele de domeniu orientate pe obiecte existente. Framework-ul se distinge printr-un strat de abstractizare neutru față de furnizor, care permite schimbarea fără probleme a modelelor de limbaj locale și cloud la runtime. Suportă colaborarea distribuită, permițând agenților independenți să partajeze informații și să delege sarcini între diferite servicii. Pentru a asigura vizibilitatea în luarea deciziilor autonome, sistemul include instrumente cuprinzătoare care captează urme de execuție, metrici de performanță și log-uri de operațiuni, care pot fi exportate către platforme de monitorizare externe. Dincolo de orchestrarea de bază, platforma include o suită de instrumente pentru gestionarea ciclurilor de viață ale agenților, inclusiv descoperirea automată a abilităților, validarea și bootstrapping-ul mediului. Dispune de o interfață bazată pe terminal pentru chat interactiv și execuția sarcinilor, alături de primitive de securitate care impun limite de acces pentru operațiunile sistemului de fișiere. Framework-ul menține, de asemenea, un repository de memorie centralizat pentru a oferi context partajat între procesele agenților distribuiți.