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systèmes AI évolutifs

  • 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.
    kilobees Core Features
    • Multi-agent orchestration and scheduling
    • Customizable agent behavior modules
    • Inter-agent messaging and protocols
    • Workflow and task queue management
    • Prompt template and API integration
    • Performance monitoring dashboards
    • Error handling and recovery
    • Scalable parallel or distributed execution
  • AgentSmith is an open-source framework orchestrating autonomous multi-agent workflows using LLM-based assistants.
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    What is AgentSmith?
    AgentSmith is a modular agent orchestration framework built in Python that enables developers to define, configure, and run multiple AI agents collaboratively. Each agent can be assigned specialized roles—such as researcher, planner, coder, or reviewer—and communicate via an internal message bus. AgentSmith supports memory management through vector stores like FAISS or Pinecone, task decomposition into subtasks, and automated supervision to ensure goal completion. Agents and pipelines are configured via human-readable YAML files, and the framework integrates seamlessly with OpenAI APIs and custom LLMs. It includes built-in logging, monitoring, and error handling, making it ideal for automating software development workflows, data analysis, and decision support systems.
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