LinkAgent provides a lightweight microkernel for building AI agents with pluggable components. Users can register language model backends, retrieval modules, and external APIs as tools, then assemble them into workflows using built-in planners and routers. LinkAgent supports memory handlers for context persistence, dynamic tool invocation, and configurable decision logic for complex multi-step reasoning. With minimal code, teams can automate tasks like QA, data extraction, process orchestration, and report generation.

ThreeAgents is built in Python, leveraging OpenAI's chat completions API to instantiate multiple AI agents with distinct roles (system, assistant, user). It provides abstractions for agent prompting, role-based message handling, and context memory management. Developers can define custom prompt templates, configure agent personalities, and chain interactions to simulate realistic dialogues or task-oriented workflows. The framework handles message passing, context window management, and logging, enabling experiments in collaborative decision-making or hierarchical task decomposition. With support for environment variables and modular agents, ThreeAgents allows seamless swapping between OpenAI and local LLM backends, facilitating rapid prototyping of multi-agent AI systems. It ships with example scripts and Docker support for quick setup.

X AI Agent is a developer-focused framework that simplifies building custom AI agents using large language models. It provides native support for function calling, memory storage, tool and plugin integration, chain-of-thought reasoning, and orchestration of multi-step tasks. Users can define custom actions, connect external APIs, and maintain conversational context across sessions. The framework’s modular design ensures extensibility and allows seamless integration with popular LLM providers, enabling robust automation and decision-making workflows.

AI Foundry offers a comprehensive no-code environment to construct custom AI agents that autonomously perform complex tasks. Users create workflows using a visual builder, chaining language models, REST APIs, database connectors and memory stores. Each agent can be configured with event triggers, scheduling options, execution logs and collaboration features. Test agents interactively before deploying them as API endpoints or embedding them into applications. Built-in monitoring and analytics provide real-time insights into performance and usage. AI Foundry scales horizontally, supports role-based access controls for teams, and ensures secure data handling, enabling businesses and developers to automate processes such as customer support automation, research assistance, report generation or lead qualification quickly and reliably.

AnYi AI Agent Framework helps developers integrate autonomous AI agents into their applications. Agents can plan and execute multi-step tasks, leverage external tools and APIs, and maintain conversation context through configurable memory modules. The framework abstracts interactions with various LLM providers and supports custom tool and memory backends. With built-in logging, monitoring, and asynchronous execution, AnYi accelerates deployment of intelligent assistants for research, customer support, data analysis, or any workflow requiring automated reasoning and action.

BlueMarz.ai provides a comprehensive environment to design, build, and operate intelligent AI agents for a variety of business use cases. Users can choose from an extensive library of templates or define custom conversation flows using a visual builder. The platform’s memory management feature stores and retrieves context throughout interactions, enabling agents to deliver personalized responses. Integration with APIs, databases, and third-party services ensures seamless data access, while built-in connectors allow deployment on web channels, Slack, and Microsoft Teams. Administrators can monitor agent performance through real-time dashboards, manage version control, and set security permissions. Overall, BlueMarz.ai reduces development complexity, accelerates time-to-market, and scales agent deployments to meet evolving operational demands.

Autonomys Agents empowers developers to create autonomous AI agents capable of executing complex tasks without manual intervention. Built on Python, the framework provides tools for defining agent behaviors, integrating external APIs and custom functions, and maintaining conversational memory across interactions. Agents can collaborate in multi-agent setups, sharing knowledge and coordinating actions. Observability modules offer real-time logging, performance tracking, and debugging insights. With its modular architecture, teams can extend core components, incorporate new LLMs, and deploy agents across environments. Whether automating customer support, performing data analysis, or orchestrating research workflows, Autonomys Agents streamlines end-to-end development and management of intelligent autonomous systems.

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.