BotSharp is an open-source AI agent framework for .NET developers, enabling the creation of "Conversation as a Platform" applications. It targets enterprise developers seeking to integrate AI into existing business systems, offering a modular, pluggable architecture for building sophisticated AI assistants with natural language understanding, computer vision, and audio processing capabilities.

How It Works

BotSharp employs a plug-in and pipeline execution design, minimizing the core and implementing business functions via external components. This modularity allows for flexible integration of various UI/UX elements and LLM providers. It features an advanced Agent abstraction layer for managing complex enterprise scenarios, supporting multiple LLM planning approaches and built-in RAG interfaces with memory-based vector searching.

Quick Start & Requirements

• Install backend: git clone https://github.com/dotnetcore/BotSharp then cd BotSharp and dotnet run --project .\src\WebStarter\WebStarter.csproj -p SolutionName=BotSharp
• Install UI: git clone https://github.com/SciSharp/BotSharp-UI then cd BotSharp-UI and npm install then npm run dev
• Prerequisites: .NET Core, Node.js.
• Docs: https://botsharp.readthedocs.io and https://botsharp.verdure-hiro.cn

Highlighted Details

• Supports multiple LLM providers including OpenAI, Gemini, LLaMA 3, Claude 3.5, and HuggingFace.
• Built-in RAG interfaces and memory-based vector searching.
• Facilitates multi-agent cooperation for complex task completion.
• Offers RESTful Open API and WebSocket for real-time communication.
• Integrates with popular messaging channels like Facebook Messenger, Slack, and Telegram.

Maintenance & Community

BotSharp is a member project of the SciSharp STACK ecosystem. Community links are not explicitly provided in the README.

Licensing & Compatibility

The README does not explicitly state a license. Compatibility for commercial use or closed-source linking is not detailed.

Limitations & Caveats

The project is described as a framework builder, implying that significant development effort is required to create functional AI applications. Specific performance benchmarks or detailed limitations are not provided.