Comprehensive 逐步規劃 Tools for Every Need

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逐步規劃

  • FreeAct is an open-source framework enabling autonomous AI agents to plan, reason, and execute actions via LLM-driven modules.
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    What is FreeAct?
    FreeAct leverages a modular architecture to streamline the creation of AI agents. Developers define high-level objectives and configure the planning module to generate stepwise plans. The reasoning component evaluates plan feasibility, while the execution engine orchestrates API calls, database queries, and external tool interactions. Memory management tracks conversation context and historical data, allowing agents to make informed decisions. An environment registry simplifies the integration of custom tools and services, enabling dynamic adaptation. FreeAct supports multiple LLM backends and can be deployed on local servers or cloud environments. Its open-source nature and extensible design facilitate rapid prototyping of intelligent agents for research and production use cases.
    FreeAct Core Features
    • Planning Module
    • Reasoning Engine
    • Execution Engine
    • Memory Management
    • Environment Registry
    • Tool & API Integration
    • LLM Backend Support
    • Extensible Plugin System
    FreeAct Pro & Cons

    The Cons

    Requires familiarity with Python programming to define and use tools effectively.
    May have a steeper learning curve for users not accustomed to coding compared to simpler JSON configurations.
    No explicit pricing or commercial support information available.

    The Pros

    Uses Python code to define actions, allowing more complex and reusable tool integrations.
    Supports any LLM provider via LiteLLM for flexible model usage.
    Enables stateful processing and behavior-rich tool definitions beyond simple JSON.
    Improves success rates of code actions compared to JSON-based methods.
  • A lightweight Python framework to build autonomous AI agents with memory, planning, and LLM-powered tool execution.
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    What is Semi Agent?
    Semi Agent provides a modular architecture for building AI agents that can plan, execute actions, and remember context over time. It integrates with popular language models, supports tool definitions for custom functionality, and maintains conversational or task-oriented memory. Developers can define step-by-step plans, connect external APIs or scripts as tools, and leverage built-in logging to debug and optimize agent behavior. Its open-source design and Python basis allow easy customization, extensibility, and integration into existing pipelines.
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