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workflows autonomes

  • Thousand Birds is a developer framework enabling AI agents to plan and execute multi-step tasks with plugin integrations.
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    What is Thousand Birds?
    Thousand Birds is an extensible AI agent framework allowing developers to define and configure agent behaviors using a Python SDK and CLI. Agents can plan multi-step workflows, integrate web search, interact with browser sessions, read and write files, call external APIs, and manage stateful memory. It supports plugin modules to add custom tools and data connectors. The built-in orchestration engine schedules tasks, handles retries, and logs execution details. Developers can chain agents, enable parallel execution, and monitor performance through structured outputs. Thousand Birds accelerates deployment of autonomous assistants for research, data extraction, automation, and experimental prototypes.
    Thousand Birds Core Features
    • Multi-step task planning and execution
    • Plugin-based tool integration
    • Stateful memory management
    • Web browsing and API interaction
    • CLI and Python SDK
    • Multi-agent orchestration
    Thousand Birds Pro & Cons

    The Cons

    No clear pricing information available.
    Not explicitly open source based on the information provided.
    No direct links to mobile or desktop applications.

    The Pros

    Provides a robust framework for building complex autonomous agent systems.
    Supports integration of generative AI with traditional software and web services.
    Features deep observability, debugging, and management tools for agent systems.
    Includes capabilities such as code composition, event triggers, and execution visualization.
  • A Python framework orchestrating planning, execution, and reflection AI agents for autonomous multi-step task automation.
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    What is Agentic AI Workflow?
    Agentic AI Workflow is an extensible Python library designed to orchestrate multiple AI agents for complex task automation. It includes a planning agent to break down objectives into actionable steps, execution agents to perform those steps via connected LLMs, and a reflection agent to review outcomes and refine strategies. Developers can customize prompt templates, memory modules, and connector integrations for any major language model. The framework provides reusable components, logging, and performance metrics to streamline the creation of autonomous research assistants, content pipelines, and data processing workflows.
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