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Softwareerweiterbarkeit

  • 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.
    Semi Agent Core Features
    • LLM integration (OpenAI, HuggingFace)
    • Custom tool execution
    • Contextual memory management
    • Stepwise planning module
    • Detailed logging and debugging
  • GPA-LM is an open-source agent framework that decomposes tasks, manages tools, and orchestrates multi-step language model workflows.
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    What is GPA-LM?
    GPA-LM is a Python-based framework designed to simplify the creation and orchestration of AI agents powered by large language models. It features a planner that breaks down high-level instructions into sub-tasks, an executor that manages tool calls and interactions, and a memory module that retains context across sessions. The plugin architecture allows developers to add custom tools, APIs, and decision logic. With multi-agent support, GPA-LM can coordinate roles, distribute tasks, and aggregate results. It integrates seamlessly with popular LLMs like OpenAI GPT and supports deployment on various environments. The framework accelerates the development of autonomous agents for research, automation, and application prototyping.
  • An open-source AI agent framework enabling automated planning, tool integration, decision-making, and workflow orchestration with LLMs.
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    What is MindForge?
    MindForge is a robust orchestration framework designed for building and deploying AI-driven agents with minimal boilerplate. It offers a modular architecture comprising a task planner, reasoning engine, memory manager, and tool execution layer. By leveraging LLMs, agents can parse user input, formulate plans, and invoke external tools—such as web scraping APIs, databases, or custom scripts—to accomplish complex tasks. Memory components store conversational context, enabling multi-turn interactions, while the decision engine dynamically selects actions based on defined policies. With plugin support and customizable pipelines, developers can extend functionality to include custom tools, third-party integrations, and domain-specific knowledge bases. MindForge simplifies AI agent development, facilitating rapid prototyping and scalable deployment in production environments.
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