kilobees is a comprehensive multi-agent orchestration platform built in Python that streamlines the development of complex AI workflows. Developers can define individual agents with specialized roles, such as data extraction, natural language processing, API integration, or decision logic. kilobees automatically manages inter-agent messaging, task queues, error recovery, and load balancing across execution threads or distributed nodes. Its plugin architecture supports custom prompt templates, performance monitoring dashboards, and integrations with external services like databases, web APIs, or cloud functions. By abstracting the common challenges of multi-agent coordination, kilobees accelerates prototyping, testing, and deployment of sophisticated AI systems that require collaborative agent interactions, parallel execution, and modular extensibility.

Multi-Agent-RAG provides a modular framework for constructing retrieval-augmented generation (RAG) applications by orchestrating multiple specialized AI agents. Developers configure individual agents: a retrieval agent connects to vector stores to fetch relevant documents; a reasoning agent performs chain-of-thought analysis; and a generation agent synthesizes final responses using large language models. The framework supports plugin extensions, configurable prompts, and comprehensive logging, enabling seamless integration with popular LLM APIs and vector databases to improve RAG accuracy, scalability, and development efficiency.

AIBrokers provides a unified interface for managing and executing workflows that involve multiple AI agents and models. It allows developers to define brokers that oversee task distribution, selecting the most suitable model—such as GPT-4 for language tasks or a vision model for image analysis—based on customizable routing rules. ConversationManager supports context awareness by storing and retrieving past dialogues, while the MemoryStore module offers persistent state handling across sessions. PluginManager enables seamless integration of external APIs or custom functions, extending the broker’s capabilities. With built-in logging, monitoring hooks, and customizable error handling, AIBrokers simplifies the development and deployment of complex AI-driven applications in production environments.

Huly Labs is a cloud-native AI agent platform that empowers developers and product teams to design, deploy, and monitor intelligent assistants. Agents can maintain context via persistent memory, call external APIs or databases, and execute multi-step workflows through a visual builder. The platform includes role-based access controls, a Node.js SDK and CLI for local development, customizable UI components for chat and voice, and real-time analytics for performance and usage. Huly Labs handles scaling, security, and logging out of the box, enabling rapid iteration and enterprise-grade deployments.

AI-Agents provides a flexible, modular platform for creating autonomous AI-driven agents. Developers can define agent objectives, chain tasks, and incorporate memory modules to store and retrieve contextual information across sessions. The framework supports integration with leading LLMs via API keys, enabling agents to generate, evaluate, and revise outputs. Customizable tool and plugin support allows agents to interact with external services like web scraping, database queries, and reporting tools. Through clear abstractions for planning, execution, and feedback loops, AI-Agents accelerates prototyping and deployment of intelligent automation workflows.

AgentDock provides a drag-and-drop interface for building and managing coordinated AI agents. Each agent can be assigned specific roles—such as web research, summarization, data analysis, or content creation—and linked through triggers and actions. With pre-built templates, API integrations, scheduling, and real-time monitoring, teams can automate end-to-end workflows, gain insights from curated data, and scale operations without developer overhead.

AgentIn is a Python-based AI agent framework designed to accelerate the development of conversational and task-driven agents. It offers built-in memory modules to persist context, dynamic tool integration to call external APIs or local functions, and a flexible prompt templating system for customized interactions. Multi-agent orchestration enables parallel workflows, while logging and caching improve reliability and auditability. Easily configurable via YAML or Python code, AgentIn supports major LLM providers and can be extended with custom plugins for domain-specific capabilities.