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 is a high-performance inference framework designed to execute large language models locally on mobile, desktop, and IoT hardware. It serves as an on-device model runtime that utilizes CPU, GPU, and NPU acceleration to provide low-latency processing. The framework is distinguished by its ability to process text, vision, and audio inputs through a single multi-modal inference engine. It features a local HTTP server that emulates OpenAI-compatible API endpoints and a WebGPU-based runtime for executing models directly within a web browser. To ensure output reliability, it includes a con

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 是一个企业级 AI 控制平面和 LLM 智能体编排平台。它提供了一个零代码环境，用于通过多智能体协作框架设计、部署和管理生产级 AI 智能体，该框架使用标准化消息协议协调专门的自主智能体。 该平台集成了模型上下文协议（Model Context Protocol），通过通用通信接口将智能体与外部工具、插件和服务连接起来。它还以专用的 RAG 知识库管理器脱颖而出，该管理器导入非结构化文档并利用混合搜索为模型响应提供扎实的上下文。 该系统涵盖了广泛的功能，包括多租户基于角色的访问控制、跨文本、语音和图像的多模态交互以及混合向量检索。它还包括用于智能体分发和发现的市场，以及用于捕获执行轨迹的可观测性工具。 该平台通过用于气隙基础设施的容器化离线打包支持安全部署。

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

此项目是使用 Bot Framework SDK 构建对话机器人的示例库和开发工具包。它提供了一系列以任务为中心的代码示例、模板和实现指南，帮助开发者创建交互式聊天界面和对话流。 该项目专注于 Bot Framework 的集成模式，提供实现自定义中间件、身份验证和连接外部机器人技能的具体示例。它包括多渠道聊天机器人模板的参考实现，允许单个代理通过统一的模式在多个消息平台上运行。 该库涵盖了广泛的对话式 AI 功能，包括 AI 代理的编排、对话状态和上下文持久性的管理，以及丰富用户界面元素的集成。它还提供了关于自然语言理解、知识库使用以及将机器人部署到云环境的指导。 提供了适用于 C#、JavaScript 和 Python 开发的资源和示例。

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 是一个 LLM 智能体编排框架，旨在利用有向无环图协调多步 AI 工作流和工具执行。它作为一个集中式系统，用于管理专门的技能包并执行复杂的推理序列。 该项目通过一个路由网关脱颖而出，该网关根据复杂性、成本和性能将任务定向到不同的 AI 提供商。它利用多层 AI 记忆系统，通过本地嵌入和 SQLite 组织工作、情景和语义知识，并配有一个安全执行沙盒，通过基于风险的权限配置文件隔离智能体生成的代码。 该平台涵盖了广泛的功能，包括多渠道部署到 Web 和消息平台、通过 cron 进行自动任务调度，以及用于连接外部工具的 Model Context Protocol 网桥。它还提供全面的监控和可观测性工具，用于跟踪 Token 成本、审计运行时决策以及管理可重用技能目录。 该系统包括用于工作区初始化和技能生命周期管理的命令行工具。

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

该项目是一个用于在基于 JVM 的应用中开发和编排自主软件代理的框架。它提供了一个工具包，用于将人工智能直接嵌入业务逻辑中，使代理能够通过动态、面向目标的规划而不是刚性状态机来执行复杂任务。通过利用声明式注解，该框架允许开发者定义代理能力并将其集成到现有的面向对象领域模型中。 该框架通过一个供应商中立的抽象层脱颖而出，允许在运行时无缝切换本地和云端语言模型。它支持分布式协作，使独立代理能够跨不同服务共享信息和委派任务。为了确保对自主决策的可见性，该系统包括全面的仪表化功能，可捕获执行跟踪、性能指标和操作日志，并可导出到外部监控平台。 除了核心编排外，该平台还包括一套用于管理代理生命周期的工具，包括自动化技能发现、验证和环境引导。它具有用于交互式聊天和任务执行的终端界面，以及强制执行文件系统操作访问边界的安全原语。该框架还维护一个集中式内存存储库，为分布式代理进程提供共享上下文。

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