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.

Flock provides a developer-friendly, modular framework for chaining multiple LLM calls, managing conversational memory, and integrating external tools into autonomous agents. With support for asynchronous execution and plugin extensions, Flock enables fine-grained control over agent behaviors, triggers, and context handling. It works seamlessly in Node.js and browser environments, letting teams rapidly prototype chatbots, data-processing workflows, virtual assistants, and other AI-driven automation solutions.

vagent_comm is an API client framework that simplifies message exchange between Python applications and VAgent AI agents. It supports secure token authentication, automatic JSON formatting, and dual transport via WebSocket and HTTP REST. Developers can establish sessions, send text or data payloads, handle streaming responses, and manage retries on errors. The library’s asynchronous interface and built-in session management allow seamless integration into chatbots, virtual assistant backends, and automated workflows.

SwiftAgent offers a robust toolkit for constructing intelligent agents by integrating OpenAI's models directly in Swift. Developers can declare custom actions and external tools, which agents invoke based on user queries. The framework maintains conversational memory, enabling agents to reference past interactions. It supports prompt templating and dynamic context injection, facilitating multi-turn dialogues and decision logic. SwiftAgent's async API works seamlessly with Swift concurrency, making it ideal for iOS, macOS, or server-side environments. By abstracting model calls, memory storage, and pipeline orchestration, SwiftAgent empowers teams to prototype and deploy conversational assistants, chatbots, or automation agents quickly within Swift projects.

FastMCP is an open-source Python framework for building MCP (Model Context Protocol) servers and clients that empower LLMs with external tools, data sources, and custom prompts. Developers define tool classes and resource handlers in Python, register them with the FastMCP server, and deploy using transport protocols like HTTP, STDIO, or SSE. The framework’s client library offers an asynchronous interface for interacting with any MCP server, facilitating seamless integration of AI agents into applications.

FastAPI Agents provides a robust service layer for developing LLM-based agents using the FastAPI web framework. It allows you to define agent behaviors with LangChain chains, tools, and memory systems. Each agent can be exposed as a standard REST endpoint, supporting asynchronous requests, streaming responses, and customizable payloads. Integration with vector stores enables retrieval-augmented generation for knowledge-driven applications. The framework includes built-in logging, monitoring hooks, and Docker support for containerized deployment. You can easily extend agents with new tools, middleware, and authentication. FastAPI Agents accelerates the production readiness of AI solutions, ensuring security, scalability, and maintainability of agent-based applications in enterprise and research settings.

A2A4J is a lightweight Java framework designed for building autonomous AI agents. It offers abstractions for agents, tools, memories, and planners, supporting asynchronous execution of tasks and seamless integration with OpenAI and other LLM APIs. Its modular design lets you define custom tools and memory stores, orchestrate multi-step workflows, and manage decision loops. With built-in error handling, logging, and extensibility, A2A4J accelerates the development of intelligent Java applications and microservices.