AgentSea AI Hub is a robust AI platform and framework that streamlines end-to-end agent development and management. It features a drag-and-drop visual builder for crafting agent personas, conversation flows, and custom skills without deep coding expertise. Developers can integrate external APIs, knowledge bases, and databases, while the built-in memory management module preserves context across sessions. The platform supports multi-channel deployment including web, mobile, chat, voice, and email, ensuring seamless user interactions. Detailed performance monitoring, A/B testing, and version control enable continuous improvement. With role-based access control and collaborative workspaces, teams can efficiently coordinate on complex agent projects. AgentSea AI Hub accelerates digital worker creation, automates repetitive tasks, and enhances customer engagement through intelligent automation.

Divine Agent is a comprehensive AI agent platform that simplifies the design, development, and deployment of autonomous digital workers. Through its intuitive visual workflow builder, users can define agent behavior as a sequence of nodes, connect to any REST or GraphQL API, and select from supported LLMs like OpenAI and Google PaLM. The built-in memory module preserves context across sessions, while real-time analytics track usage, performance, and errors. Once tested, agents can be deployed as HTTP endpoints or integrated with channels like Slack, email, and custom applications, enabling rapid automation of customer support, sales, and knowledge tasks.

ElizaOS provides a robust suite of tools to design, test, and deploy autonomous AI agents within TypeScript projects. Developers define agent personalities, goals, and memory hierarchies, then leverage ElizaOS's planning system to outline task workflows. Its modular connector architecture simplifies integrating with communication platforms—Discord, Telegram, Slack, X—and blockchain networks via Web3 adapters. ElizaOS supports multiple LLM backends (OpenAI, Anthropic, Llama, Gemini), allowing seamless switching between models. Plugin support extends functionality with custom skills, logging, and observability features. Through its CLI and SDK, teams can iterate on agent configurations, monitor live performance, and scale deployments in cloud environments or on-premises. ElizaOS empowers companies to automate customer interactions, social media engagement, and business processes with autonomous digital workers.

Integry AI Agent is a no-code integration and automation platform using advanced language models to let users design, deploy, and manage intelligent digital workers. Leveraging over 1000 pre-built connectors to apps like Salesforce, Slack, HubSpot, and QuickBooks, it transforms natural language instructions into end-to-end workflows without manual API coding. Users define triggers, actions, and conditional logic via a guided interface or chat-based prompts. AI-driven suggestions speed up flow creation, while real-time dashboards offer visibility into task execution, error handling, and performance analytics. Integry also enforces enterprise-grade security and compliance with role-based access control and audit logs, ensuring scalable automation for any team.

Kin Kernel is a lightweight, open-source kernel framework for constructing AI-powered digital workers. It provides a unified system for orchestrating large language models, managing contextual memory, and integrating custom tools or APIs. With an event-driven architecture, Kin Kernel supports asynchronous task execution, session tracking, and extensible plugins. Developers define agent behaviors, register external functions, and configure multi-LLM routing to automate workflows ranging from data extraction to customer support. The framework also includes built-in logging and error handling to facilitate monitoring and debugging. Designed for flexibility, Kin Kernel can be integrated into web services, microservices, or standalone Python applications, enabling organizations to deploy robust AI agents at scale.

AgentAt is a comprehensive AI agent platform and framework designed for enterprises seeking to streamline operations through autonomous digital workers. It empowers non-technical users to design, configure, and deploy agents that can engage via chat or voice across multiple channels—web, Slack, Microsoft Teams, email, and more. Agents can be connected to internal databases, CRMs, and SaaS tools, enabling real-time data retrieval, form generation, and multi-step workflow execution. Built-in memory and context retention allow ongoing conversations to remain coherent over time, while analytics dashboards monitor performance. Teams can train and refine agents iteratively, ensuring continuous improvement in customer support, HR help desks, sales assistance, and beyond—driving efficiency and scalability.

AgentLab is a ServiceNow framework for creating AI agents—also called digital workers—using a visual, drag-and-drop editor. Users link large language models with ServiceNow tables, define intents and actions, and orchestrate workflows for tasks like incident resolution, change approvals, and knowledge retrieval. Agents can be tested in built-in sandboxes, versioned, and monitored in real time. With connectors for external APIs and chat interfaces, AgentLab enables deployment across portals, Microsoft Teams, and Slack. The platform offers governance controls, audit trails, and analytics dashboards to ensure compliance and performance at scale.

Fetch.ai’s Autonomous Economic Agents (AEA) Framework is an open-source Python SDK and CLI toolset for creating modular, autonomous agents that can negotiate, transact, and collaborate in decentralized environments. It includes scaffolding commands to generate agent projects, templates for protocols and skills, connection modules to integrate with multiple ledgers (Ethereum, Cosmos, etc.), contract interfaces, behavior and decision‐making components, testing and simulation utilities, and a publishing mechanism to distribute agents on the Open Economic Framework network. Developers leverage its modular architecture to rapidly prototype digital workers for DeFi trading, data marketplaces, IoT coordination, and supply chain automation.

AIKitchen provides a developer-friendly Python toolkit enabling you to compose AI agents as modular building blocks. At its core, it offers pipeline definitions with stages for input preprocessing, LLM invocation, tool execution, and memory retrieval. Integrations with popular LLM providers allow flexibility, while built-in memory stores track conversational context. Developers can embed custom tasks, leverage retrieval-augmented generation for knowledge access, and gather standardized metrics to monitor performance. The framework also includes workflow orchestration capabilities, supporting sequential and conditional flows across multiple agents. With its plugin architecture, AIKitchen streamlines end-to-end agent development—from prototyping research ideas to deploying scalable digital workers in production environments.

AimeBox provides a comprehensive, self-hosted environment for building and running AI agents. It integrates with major LLM providers, stores dialogue state and embeddings in a vector database, and supports custom tool and function calling. Users can configure memory strategies, define workflows, and extend capabilities via plugins. The platform offers a web-based dashboard, API endpoints, and CLI controls, making it easy to develop chatbots, knowledge assistants, and domain-specific digital workers without relying on third-party services.

Cara is an AI system purpose-built for the insurance sector, offering a modular architecture that empowers leading insurance agencies and brokerages. It helps in accelerating sales while automating various services, effectively functioning as a digital workforce available 24/7. This enables agencies to streamline operations, improve customer service, and ultimately drive growth in a highly competitive market.

CrewAI provides an AI-driven workspace for designing, training and deploying digital workers without writing a line of code. Users record their manual web interactions once, then CrewAI’s engine generalizes and repeats the steps on demand. Digital workers can authenticate, scrape data, complete forms, make decisions based on conditions and connect to external services via API. This accelerates process automation for sales outreach, data reporting and administrative workflows, scaling tasks across teams while ensuring reliability and compliance.