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可插拔架構

  • A .NET C# framework to build and orchestrate GPT-based AI agents with declarative prompts, memory, and streaming.
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    What is Sharp-GPT?
    Sharp-GPT empowers .NET developers to create robust AI agents by leveraging custom attributes on interfaces to define prompt templates, configure models, and manage conversational memory. It offers streaming output for real-time interaction, automatic JSON deserialization for structured responses, and built-in support for fallback strategies and logging. With pluggable HTTP clients and provider abstraction, you can switch between OpenAI, Azure, or other LLM services effortlessly. Ideal for chatbots, content generation, summarization, classification, and more, Sharp-GPT reduces boilerplate and accelerates AI agent development on Windows, Linux, or macOS.
    Sharp-GPT Core Features
    • Annotation-driven prompt definitions
    • Contextual memory management
    • Streaming response support
    • Automatic JSON response binding
    • Pluggable LLM provider abstraction
    • Logging and fallback policies
    Sharp-GPT Pro & Cons

    The Cons

    The Pros

    Sharp-GPT Pricing
    Has free planNo
    Free trial details
    Pricing model
    Is credit card requiredNo
    Has lifetime planNo
    Billing frequency
    For the latest prices, please visit: https://julzedz.github.io/Sharp-GPT/
  • Agent Adapters provides pluggable middleware to integrate LLM-based agents with various external frameworks and tools seamlessly.
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    What is Agent Adapters?
    Agent Adapters is designed to provide developers with a consistent interface for connecting AI agents to external services and frameworks. Through its pluggable adapter architecture, it offers prebuilt adapters for HTTP APIs, messaging platforms like Slack and Teams, and custom tool endpoints. Each adapter handles request parsing, response mapping, error handling, and optional logging or monitoring hooks. Developers can also register custom adapters by implementing a defined interface and configuring adapter parameters in their agent settings. This streamlined approach reduces boilerplate code, ensures uniform workflow execution, and accelerates the deployment of agents across multiple environments without rewriting integration logic.
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