Chainlit is an open-source async Python framework designed to help developers build and deploy scalable Conversational AI and agentic applications rapidly. It supports integrations with popular Python libraries and frameworks to provide a seamless development experience. With Chainlit, users can create production-ready chat applications that can handle complex interactions and retain conversational context.

Gemini Computer Use transforms how you interact with your operating system by embedding AI-driven decision-making directly into your workflow. By integrating Google Gemini, the agent can understand context-rich English commands to perform various actions: executing terminal commands, creating, editing, or deleting files, monitoring system status, and automating multi-step routines. Users communicate directly through a CLI or API, making it easier to prototype solutions, manage servers, or develop scripts without extensive manual coding. The modular architecture allows developers to extend capabilities, integrate with custom tools, and build domain-specific agents. Whether orchestrating complex deployment processes or simplifying everyday file operations, Gemini Computer Use brings the power of conversational AI to your desktop.

Cloudflare Agents is an AI agent platform built on top of Cloudflare Workers, offering a developer-friendly environment to design autonomous agents at the network edge. It integrates with leading language models (e.g., OpenAI, Anthropic), providing configurable prompts, routing logic, memory storage, and data connectors like Workers KV, R2, and D1. Agents perform tasks such as data enrichment, content moderation, conversational interfaces, and workflow automation, executing pipelines across distributed edge locations. With built-in version control, logging, and performance metrics, Cloudflare Agents deliver reliable, low-latency responses with secure data handling and seamless scaling.

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.