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.

10x Rules is a comprehensive AI agent framework allowing organizations to construct and deploy smart agents based on custom rule sets and business logic. By defining triggers, conditions, and actions in an intuitive interface, users can instruct AI agents to perform tasks such as extracting data from documents, scoring leads, sending personalized emails, and updating CRM records. The platform seamlessly integrates with popular services through pre-built connectors, supports real-time monitoring and debugging, and provides analytics on agent performance. Both technical and non-technical users can streamline repetitive workflows, reduce manual errors, and accelerate operations with AI-driven automation.

ByteChef offers a modular architecture to build, test, and deploy AI agents. Developers define agent profiles, attach custom skill plugins, and orchestrate multi-agent workflows through a visual web IDE or SDK. It integrates with major LLM providers (OpenAI, Cohere, self-hosted models) and external APIs. Built-in debugging, logging, and observability tools streamline iteration. Projects can be deployed as Docker services or serverless functions, enabling scalable, production-ready AI agents for customer support, data analysis, and automation.

Deerflow provides a visual interface where users can assemble AI workflows from modular components—input processors, LLM or model executors, conditional logic, and output handlers. Out of the box connectors allow you to pull data from databases, APIs, or document stores, then pass results through one or more AI models in sequence. Built-in tools handle logging, error recovery, and metric tracking. Once configured, workflows can be tested interactively and deployed as REST endpoints or event-driven triggers. A dashboard gives real-time insights, version history, alerts, and team collaboration features, making it simple to iterate, scale, and maintain AI agents in production.

Rawr Agent is a modular, open-source Python framework that empowers developers to build autonomous AI agents by orchestrating complex workflows of LLM interactions. Leveraging LangChain under the hood, Rawr Agent lets you define task sequences either through YAML configurations or Python code, specifying tool integrations such as web APIs, database queries, and custom scripts. It includes memory components for storing conversational history and vector embeddings, caching mechanisms to optimize repeated calls, and robust logging and error handling to monitor agent behavior. Rawr Agent’s extensible architecture allows adding custom tools and adapters, making it suitable for tasks like automated research, data analysis, report generation, and interactive chatbots. With its simple API, teams can rapidly prototype and deploy intelligent agents for diverse applications.