Comprehensive IA multiagente Tools for Every Need

Get access to IA multiagente solutions that address multiple requirements. One-stop resources for streamlined workflows.

IA multiagente

  • Chat with multiple AI agents to collaboratively solve your goals.
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    What is CircleChat?
    CircleChat is a platform where users can interact with multiple AI agents concurrently. Each AI agent offers unique insights and expertise, making it easier for users to get a well-rounded perspective on their queries or issues. Designed to facilitate smart conversations, CircleChat aims at becoming your go-to virtual brainstorming tool. Whether you're problem-solving, planning, or just in need of creative input, CircleChat connects you with several AIs, each contributing to a more comprehensive understanding.
  • Eigent is an open-source AI workforce platform managing complex workflows via multi-agent collaboration.
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    What is Eigent?
    Eigent is an AI platform that enables creating a dynamic AI workforce composed of multiple collaborative agents working in parallel to automate complex workflows. It supports full customization of worker nodes and task-specific tools, providing secure local deployment to ensure data privacy and control. Eigent's infrastructure delivers superior performance and cost-efficiency by optimizing multi-agent interactions, making it ideal for businesses aiming to leverage AI for scalable automation.
  • A Python-based framework orchestrating dynamic AI agent interactions with customizable roles, message passing, and task coordination.
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    What is Multi-Agent-AI-Dynamic-Interaction?
    Multi-Agent-AI-Dynamic-Interaction offers a flexible environment to design, configure, and run systems composed of multiple autonomous AI agents. Each agent can be assigned specific roles, objectives, and communication protocols. The framework manages message passing, conversation context, and sequential or parallel interactions. It supports integration with OpenAI GPT, other LLM APIs, and custom modules. Users define scenarios via YAML or Python scripts, specifying agent details, workflow steps, and stopping criteria. The system logs all interactions for debugging and analysis, allowing fine-grained control over agent behaviors for experiments in collaboration, negotiation, decision-making, and complex problem-solving.
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