Awesome GitHub RepositoriesInteractive Agent Chat Interfaces

nlp.js is a JavaScript natural language processing library and development framework used to build natural language understanding engines. It provides a toolkit for creating local machine learning models for intent classification and acts as a multilingual text processor that detects languages and normalizes text across various dialects. The framework distinguishes itself by supporting local execution on both servers and mobile devices, enabling chatbot functionality without an internet connection. It features a specialized system for conversational slot filling to collect mandatory informati

This is a demonstration project from OpenAI that showcases a multi-agent customer service workflow built with the OpenAI Agents SDK. It coordinates several specialized AI agents to handle common airline support tasks such as flight booking and cancellation, refunds and compensation, seat and special service requests, real-time flight information, and airline policy FAQ responses, all within a single conversational interface. The system routes incoming customer requests to the appropriate specialized agent based on intent, and enforces guardrails to block off-topic or malicious requests. It su

LiteRT-LM es un framework de inferencia de alto rendimiento diseñado para ejecutar modelos de lenguaje de gran tamaño localmente en hardware móvil, de escritorio y IoT. Sirve como un runtime de modelos en el dispositivo que utiliza aceleración de CPU, GPU y NPU para proporcionar un procesamiento de baja latencia. El framework se distingue por su capacidad para procesar entradas de texto, visión y audio a través de un único motor de inferencia multimodal. Cuenta con un servidor HTTP local que emula endpoints de API compatibles con OpenAI y un runtime basado en WebGPU para ejecutar modelos directamente dentro de un navegador web. Para garantizar la fiabilidad de la salida, incluye un generador de texto restringido que impone esquemas JSON o reglas gramaticales en las respuestas del modelo. El proyecto proporciona amplias capacidades para la gestión de conversaciones con estado, decodificación especulativa para aumentar las velocidades de generación de tokens y una interfaz de llamada a herramientas que mapea las solicitudes del modelo a funciones externas. También incluye integración especializada para el ecosistema Apple y un plugin dedicado para ejecutar modelos en Flutter. Los usuarios pueden ejecutar modelos a través de una interfaz de línea de comandos o integrarlos en aplicaciones mediante API nativas.

PraisonAI is an autonomous AI agent platform that coordinates multiple LLM-powered agents for research, planning, and execution of complex workflows. It functions as a multi-agent orchestration framework, a workflow builder, and a Model Context Protocol server, while also providing retrieval-augmented generation through vector knowledge bases. Agents can interact via CLI, web, or standardized protocols with sandboxed code execution. The platform distinguishes itself with a rich set of agent communication protocols, including A2A, REST, WebSocket, voice and telephony integration, and MCP, allo

Nexent es un plano de control de IA empresarial y plataforma de orquestación de agentes LLM. Proporciona un entorno sin código (zero-code) para diseñar, desplegar y gestionar agentes de IA de producción a través de un framework de colaboración multi-agente que coordina agentes autónomos especializados utilizando protocolos de mensajería estandarizados. La plataforma integra el Model Context Protocol para conectar agentes con herramientas, plugins y servicios externos mediante una interfaz de comunicación universal. Destaca además con un gestor de base de conocimientos RAG dedicado que importa documentos no estructurados y utiliza búsqueda híbrida para proporcionar contexto fundamentado para las respuestas del modelo. El sistema cubre una amplia gama de capacidades, incluyendo control de acceso basado en roles multi-inquilino, interacción multimodal a través de texto, voz e imágenes, y recuperación vectorial híbrida. También incluye un mercado para la distribución y descubrimiento de agentes, junto con herramientas de observabilidad para capturar trazas de ejecución. La plataforma soporta despliegue seguro mediante empaquetado offline contenedorizado para infraestructura aislada (air-gapped).

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

This repository is a reference implementation and guided tutorial for building an AI coding agent that combines conversational interaction with file system manipulation and sandboxed shell execution. The agent uses a large language model as its core decision-making component, operating within a turn-based conversational loop where it can generate responses or invoke tools, and tool results are fed back into the dialogue. It provides primitives for reading, writing, and listing files on the local filesystem, as well as searching code using regular expressions. The agent’s capabilities are exte

OpenAgents is an open-source platform for deploying, managing, and interacting with language agents through a conversational interface. Agents on this platform can analyze data by generating and executing Python and SQL code, invoke external plugins, browse the web autonomously, and perform tasks like flight search, map directions, and social media posting—all driven by natural language. What distinguishes the platform is its architecture for persistent agent lifecycle management, isolated code execution via a sandbox, multi-agent coordination for complex workflows, and automatic plugin disco

Este proyecto es una biblioteca de muestras y un kit de desarrollo para construir bots conversacionales utilizando el Bot Framework SDK. Proporciona una colección de ejemplos de código enfocados en tareas, plantillas y guías de implementación para ayudar a los desarrolladores a crear interfaces de chat interactivas y flujos de diálogo. El proyecto se centra en patrones de integración para el Bot Framework, ofreciendo ejemplos específicos para implementar middleware personalizado, autenticación de identidad y la conexión de habilidades (skills) de bots externos. Incluye implementaciones de referencia para plantillas de chatbots multicanal que permiten a un solo agente operar a través de múltiples plataformas de mensajería mediante un esquema unificado. La biblioteca cubre una amplia gama de capacidades de IA conversacional, incluyendo la orquestación de agentes de IA, la gestión del estado de la conversación y la persistencia del contexto, y la integración de elementos de interfaz de usuario enriquecidos. También proporciona orientación sobre comprensión del lenguaje natural, el uso de bases de conocimiento y el despliegue de bots en entornos de nube. Se proporcionan recursos y muestras para el desarrollo en C#, JavaScript y Python.

This project is a Llama Stack agentic framework and orchestrator used to build autonomous AI applications. It coordinates model inference and tool execution to decompose complex goals into multi-step reasoning chains and continuous inference loops. The framework incorporates a dedicated safety guardrail system that filters model inputs and outputs through safety models to enforce system-level content restrictions. It also includes a tool integration layer that maps model-generated function requests to external runtime definitions to execute actions beyond text generation. The system provides

Yuxi-Know is an LLM agent orchestration platform that coordinates multiple AI agents through graph-based workflows to decompose and execute complex reasoning tasks. It functions as a multi-tenant AI workspace with an agentic chat interface, combining retrieval-augmented generation with knowledge graph management for enterprise document processing and retrieval. The platform distinguishes itself through graph-based agent orchestration, where directed acyclic graphs define execution dependencies between reasoning steps, enabling parallel or sequential task decomposition. It provides multi-tenan

ms-agent is an LLM agent framework and multi-agent orchestration system designed to build autonomous entities that combine large language models with tool calling and structured workflows. It serves as a tool integration platform and workflow engine for executing complex tasks through the coordination of specialized agents. The project distinguishes itself through a multimodal agent workflow engine capable of automating the production of text, images, and video. It features a sandboxed code execution environment for running generated code and quantitative data analysis in isolated containers,

OpenSquilla es un framework de orquestación de agentes LLM diseñado para coordinar flujos de trabajo de IA de varios pasos y la ejecución de herramientas mediante grafos acíclicos dirigidos. Funciona como un sistema centralizado para gestionar paquetes de habilidades especializadas y ejecutar secuencias de razonamiento complejas. El proyecto se distingue por una pasarela de enrutamiento que dirige las tareas a diferentes proveedores de IA según la complejidad, el coste y el rendimiento. Utiliza un sistema de memoria de IA de varios niveles que organiza el conocimiento de trabajo, episódico y semántico mediante embeddings locales y SQLite, junto con un sandbox de ejecución seguro que aísla el código generado por el agente mediante perfiles de permisos basados en riesgos. La plataforma cubre una amplia gama de capacidades, incluyendo despliegue multicanal en web y plataformas de mensajería, programación automatizada de tareas mediante cron y un puente de Model Context Protocol para conectar con herramientas externas. También proporciona herramientas integrales de monitoreo y observabilidad para rastrear costes de tokens, auditar decisiones en tiempo de ejecución y gestionar un catálogo de habilidades reutilizables. El sistema incluye utilidades de línea de comandos para la inicialización del espacio de trabajo y la gestión del ciclo de vida de las habilidades.

Apollo Kotlin is a strongly-typed GraphQL client and code generation library designed for Kotlin and JVM applications. It functions as a comprehensive development tool that transforms GraphQL schema definitions and query documents into type-safe models during the build process, ensuring that data access errors are identified at compile time rather than at runtime. The project distinguishes itself through its multiplatform runtime abstraction, which allows developers to share data fetching and caching logic across Android, iOS, and desktop environments. It provides a normalized local caching s

Agency Swarm is a multi-agent orchestration framework and development kit designed to coordinate specialized AI agents through defined communication patterns and handoffs. It functions as a system for managing agent swarms, providing an API gateway to expose these coordinated collectives as production-ready HTTP endpoints. The project distinguishes itself through its Model Context Protocol integration layer, which connects agents to external data sources and capabilities. It implements specialized orchestration patterns, such as the orchestrator-worker model and role-based delegation, to tran

This project is an autonomous agent workflow engine and multi-agent orchestration framework. It provides a runtime for managing agent lifecycles and a provider-agnostic abstraction layer for interacting with multiple large language model backends through standardized requests and structured outputs. The framework features a reliability layer for output verification, utilizing sampling-based majority voting and generator-evaluator feedback loops to refine model responses. It supports complex coordination patterns including sequential chaining, parallel execution with fan-in aggregation, and re

This project is a framework for developing and orchestrating autonomous software agents within JVM-based applications. It provides a toolkit for embedding artificial intelligence directly into business logic, enabling agents to perform complex tasks through dynamic, goal-oriented planning rather than rigid state machines. By leveraging declarative annotations, the framework allows developers to define agent capabilities and integrate them into existing object-oriented domain models. The framework distinguishes itself through a vendor-neutral abstraction layer that allows for the seamless swap

mini-sglang is a collection of tools for large language model inference, serving as an OpenAI-compatible inference server, a memory-efficient prefill engine, and a tensor parallelism runtime. It also functions as a local batch processing engine for offline benchmarking and ablation studies. The project focuses on acceleration and memory management through a KV cache manager that reuses precomputed caches for shared request prefixes. It handles large model workloads by distributing tasks across multiple GPUs and manages peak memory consumption by splitting long input sequences into smaller chu

ZeroBot-Plugin is an extensible plugin framework for QQ group bots, providing a modular system that adds automation, AI chat, image generation, and moderation capabilities to chat environments. The project is built around a collection of plugin modules that each handle specific functions, from scheduling recurring commands to managing group member interactions. The framework distinguishes itself through its integration of AI-powered features, including configurable large language model chat with image recognition support, alongside automated content moderation that scans text and images again

Ramalama is a containerized runtime and management tool for large language models. It functions as an OCI AI model manager and registry client, allowing users to package, distribute, and execute AI models as standardized container images. The project differentiates itself by using OCI-compliant distribution for models and retrieval augmented generation assets, enabling the packaging of vector databases into immutable container images. It features hardware-aware image selection that automatically detects GPU or CPU capabilities to pull the most optimized image for the host environment. The sy