AiChat offers a versatile toolkit for creating intelligent chat agents by providing role-based prompt management, memory handling, and streaming response capabilities. Users can set up multiple conversational roles, such as system, assistant, and user, to shape dialogue context and behavior. The framework supports plugin integrations for external APIs, data retrieval, or custom logic, enabling seamless extension of functionalities. AiChat's modular design allows easy swapping of language models and configuration of feedback loops to refine responses. Built-in memory features provide context persistence across sessions, while streaming API support delivers low-latency interactions. Developers benefit from clear documentation and sample projects to accelerate deployment of chatbots across web, desktop, or server environments.

AIFoundry-AgentService-Streamlit is an open-source demo application built with Streamlit that lets users quickly spin up AI agents via AIFoundry’s AgentService API. The interface includes options to select agent profiles, adjust conversational parameters like temperature and max tokens, and display conversation history. It supports streaming responses, multiple agent environments, and logs requests and responses for debugging. Written in Python, it simplifies testing and validating different agent configurations, accelerating the prototyping cycle and reducing integration overhead before production deployment.

AutoGen UI is a frontend toolkit designed to render and manage multi-agent conversational flows. It offers ready-made components such as chat windows, agent selectors, message timelines, and debugging panels. Developers can configure multiple AI agents, stream responses in real time, log each step of the conversation, and apply custom styling. It integrates easily with backend orchestration libraries to provide a complete end-to-end interface for building and monitoring AI agent interactions.

DelphiGemini provides a lightweight, easy-to-use wrapper around Google’s Gemini LLM API for Delphi developers. It handles authentication, request formatting, and response parsing, allowing you to send prompts and receive text completions or chat responses. With support for streaming output, you can display tokens in real time. The library also offers synchronous and asynchronous methods, configurable timeouts, and detailed error reporting. Use it to build chatbots, content generators, translators, summarizers, or any AI-powered feature directly in your Delphi applications.

LLM-Powered RAG System is a developer-focused framework for building retrieval-augmented generation (RAG) pipelines. It provides modules for embedding document collections, indexing via FAISS, Pinecone, or Weaviate, and retrieving relevant context at runtime. The system uses LangChain wrappers to orchestrate LLM calls, supports prompt templates, streaming responses, and multi-vector store adapters. It simplifies end-to-end RAG deployment for knowledge bases, allowing customization at each stage—from embedding model configuration to prompt design and result post-processing.

MIU CS589 AI Agent API offers a standardized interface for building custom AI agents. Developers can define agent behaviors, integrate external tools or services, and handle streaming or batch responses via HTTP endpoints. The framework handles authentication, request routing, error handling and logging out of the box. It is fully extensible—users can register new tools, adjust agent memory, and configure LLM parameters. Suitable for experimentation, demos, and production prototypes, it simplifies multi-tool orchestration and accelerates AI agent development without locking you into a monolithic platform.

The AWS Sample Model Context Protocol Demos is an open-source repository showcasing standardized patterns for Large Language Model (LLM) context management and tool invocation. It features two complete demos—one in JavaScript/TypeScript and one in Python—that implement the Model Context Protocol, enabling developers to build AI agents that call AWS Lambda functions, preserve conversation history, and stream responses. Sample code demonstrates message formatting, function argument serialization, error handling, and customizable tool integrations, accelerating prototyping of generative AI applications.

Stellar Chat empowers teams to build conversational AI agents by providing a robust framework that abstracts LLM interactions, memory management, and tool integrations. At its core, it features an extensible pipeline that handles user input preprocessing, context enrichment through vector-based memory retrieval, and LLM invocation with configurable prompting strategies. Developers can plug in popular vector storage solutions like Pinecone, Weaviate, or FAISS, and integrate third-party APIs or custom plugins for tasks like web search, database queries, or enterprise application control. With support for streaming outputs and real-time feedback loops, Stellar Chat ensures responsive user experiences. It also includes starter templates and best-practice examples for customer support bots, knowledge search, and internal workflow automation. Deployed with Docker or Kubernetes, it scales to meet production demands while remaining fully open-source under the MIT license.

HyperChat is a developer-centric AI agent framework that simplifies embedding conversational AI into applications. It unifies connections to various LLM providers, handles session context and memory persistence, and delivers streamed partial replies for responsive UIs. Built-in function calling and plugin support enable executing external APIs, enriching conversations with real-world data and actions. Its modular architecture and UI toolkit allow rapid prototyping and production-grade deployments across web, Electron, and Node.js environments.

Goat SDK is designed to simplify the creation and orchestration of AI agents in Go. It provides pluggable LLM integrations (OpenAI, Anthropic, Azure, local models), a tool registry for custom actions, and memory stores for stateful conversations. Developers can define chains, representer strategies, and publishers to output interactions via CLI, WebSocket, REST endpoints, or a built-in Web UI. Goat supports streaming responses, customizable logging, and easy error handling. By combining these components, you can develop chatbots, automation workflows, and decision-support systems in Go with minimal boilerplate, while maintaining flexibility to swap or extend providers and tools as needed.

Rusty Agent is a lightweight yet powerful Rust library designed to simplify the creation of autonomous AI agents that leverage large language models. It introduces core abstractions such as Agents, Tools, and Memory modules, allowing developers to define custom tool integrations—e.g., HTTP clients, knowledge bases, calculators—and orchestrate multi-step conversations programmatically. Rusty Agent supports dynamic prompt building, streaming responses, and contextual memory storage across sessions. It integrates seamlessly with OpenAI API (GPT-3.5/4) and can be extended for additional LLM providers. Its strong typing and performance benefits of Rust ensure safe, concurrent execution of agent workflows. Use cases include automated data analysis, interactive chatbots, task automation pipelines, and more—empowering Rust developers to embed intelligent language-driven agents into their applications.

Rags provides a modular pipeline to build retrieval-augmented generative applications. It integrates with popular vector stores (e.g., FAISS, Pinecone), offers configurable prompt templates, and includes memory modules to maintain conversational context. Developers can switch between LLM providers like Llama-2, GPT-4, and Claude2 through a unified API. Rags supports streaming responses, custom preprocessing, and evaluation hooks. Its extensible design enables seamless integration into production services, allowing automated document ingestion, semantic search, and generation tasks for chatbots, knowledge assistants, and document summarization at scale.