Exo is a developer-centric framework enabling the creation of AI-driven agents capable of communicating with users, invoking external APIs, and preserving conversational context. At its core, Exo uses TypeScript definitions to describe tools, memory layers, and dialogue management. Users can register custom actions for tasks like data retrieval, scheduling, or API orchestration. The framework automatically handles prompt templates, message routing, and error handling. Exo’s memory module can store and recall user-specific information across sessions. Developers deploy agents in Node.js or serverless environments with minimal configuration. Exo also supports middleware for logging, authentication, and metrics. Its modular design ensures components can be reused across multiple agents, accelerating development and reducing redundancy.

Firebase GenKit is a developer framework that streamlines the creation of generative AI features using Firebase services. It includes Cloud Functions templates for invoking LLMs, Firestore triggers to log and manage prompts/responses, authentication integration, and front-end UI components for chat and content generation. Designed for serverless scalability, GenKit lets you plug in your choice of LLM provider (e.g., OpenAI) and Firebase project settings, enabling end-to-end AI workflows without heavy infrastructure management.

Koxy is a cutting-edge AI-powered serverless low-code platform that empowers users to build, deploy, and scale backend tasks within minutes without requiring any coding expertise. The platform features pre-built nodes, custom scripting capabilities in TypeScript and Python, and the ability to generate unique nodes via AI from voice or text instructions. With Koxy, users can configure projects to run on custom containers with dedicated resources, ensuring optimal performance and scalability. Additional features include real-time database updates, built-in databases, unlimited cloud storage, and auto-generated documentation for APIs, nodes, and workflows.

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.

The Multi-Agent AI framework provides an end-to-end solution for orchestrating multiple autonomous agents in cloud environments. It leverages Neon’s Postgres-compatible serverless database to store conversation history and agent state, Azure Functions to run agent logic at scale, and OpenAI APIs to power natural language understanding and generation. Built-in message queues and role-based behaviors allow agents to collaborate on tasks such as research, scheduling, customer support, and data analysis. Developers can customize agent policies, memory rules, and workflows to fit diverse business requirements.

Neuron AI is an end-to-end serverless platform for creating, deploying, and managing intelligent AI agents. It supports major LLM providers (OpenAI, Anthropic, Hugging Face) and enables multi-model pipelines, conversation context handling, and automated workflows via a low-code interface or SDKs. With built-in data ingestion, vector search, and plugin integration, Neuron simplifies knowledge sourcing and service orchestration. Its auto-scaling infrastructure and monitoring dashboards ensure performance and reliability, making it ideal for enterprise-grade chatbots, virtual assistants, and automated data processing bots.

TiDB is an integrated database solution tailored for AI applications. It supports vector search, semantic knowledge graph search, and operational data management. Its serverless architecture ensures reliability and scalability, eliminating the need for manual data synchronization and management of multiple data stores. With enterprise-grade features such as role-based access control, encryption, and high availability, TiDB is ideal for production-ready AI applications that demand performance, security, and ease of use. TiDB's platform compatibility spans both cloud-based and local deployments, making it versatile for various infrastructure needs.

Backx.ai offers an AI companion for developers, facilitating the creation, deployment, and management of backends across various use cases. It aims to boost productivity through its advanced AI capabilities, offering streamlined processes from database management to API development and serverless applications. It features one-click production-grade code generation, context-aware capabilities, versioned artifacts, instant deployment, and auto-documentation. This platform integrates seamlessly with existing tools and frameworks, providing unprecedented accuracy and flexibility.

HyperLLM is an advanced infrastructure solution designed to streamline the development and deployment of large language models (LLMs). By leveraging hybrid retrieval technologies, it significantly enhances the efficiency and effectiveness of AI-driven applications. It integrates a serverless vector database and hyper-retrieval techniques that allow for rapid fine-tuning and experiment management, making it ideal for developers aiming to create sophisticated AI solutions without the complexities typically involved.

AgentCraft is a serverless AI agent development platform that abstracts infrastructure management, allowing teams to focus on designing intelligent assistants. With drag-and-drop workflows, users define conversation flows, set triggers for API calls, and configure custom actions without writing code. The platform leverages pre-built connectors to integrate with CRMs, databases, and communication channels such as Slack, Teams, and web chat. Built-in model versioning and A/B testing allow experimentation with different dialogue strategies. Real-time monitoring dashboards track user engagement, errors, and performance metrics, enabling continuous optimization. Secure authentication, encrypted data storage, and compliance features ensure enterprise-grade security. Agents can be scaled automatically to handle peak loads and deployed globally across edge locations for low-latency access.

AWS Agentic Workflows is a serverless orchestration framework that lets you chain AI tasks into end-to-end workflows. Using Amazon Bedrock foundation models, you can invoke AI agents to perform natural language processing, classification, or custom tasks. AWS Step Functions manages state transitions, retries, and parallel execution. Lambda functions can preprocess inputs and post-process outputs. CloudWatch provides logs and metrics for real-time monitoring and debugging. This enables developers to build reliable, scalable AI pipelines without managing servers or infrastructure.