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automatisation des workflows AI

  • kilobees is a Python framework for creating, orchestrating, and managing multiple AI agents collaboratively in modular workflows.
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    What is kilobees?
    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.
  • PlugBear integrates custom GPT solutions into everyday tools for enhanced productivity.
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    What is PlugBear?
    PlugBear offers an intuitive solution to embed customized GPT models into a variety of everyday tools and platforms your team already uses. By effortlessly connecting GPT-powered applications such as OpenAI Assistants and Anthropic Claude to services like Slack, Teams, and HubSpot, PlugBear enhances your team's productivity. With user-friendly steps to integrate, it ensures smooth and efficient adoption of AI in daily operations, simplifying complex workflows and enabling smarter decision-making across your organization.
  • An open-source AI agent framework enabling modular planning, memory management, and tool integration for automated, multi-step workflows.
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    What is Pillar?
    Pillar is a comprehensive AI agent framework designed to simplify the development and deployment of intelligent multi-step workflows. It features a modular architecture with planners for task decomposition, memory stores for context retention, and executors that perform actions via external APIs or custom code. Developers can define agent pipelines in YAML or JSON, integrate any LLM provider, and extend functionality through custom plugins. Pillar handles asynchronous execution and context management out of the box, reducing boilerplate code and accelerating time-to-market for AI-driven applications such as chatbots, data analysis assistants, and automated business processes.
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