BotSharp for .NET
A modular .NET framework for multi-agent applications, RAG, and model-provider integration.
What does this agent do, and when should you use it?
BotSharp is an open-source C# and .NET Core framework for connecting language models to existing business applications. Its plugin and pipeline-flow design separates platform concerns into components such as Agent Profile, Conversation & State, Routing & Planning, LLM Provider, caching, file repository, and Rich Content. Built-in plugins cover providers including OpenAI, Anthropic, Google, DeepSeek, Meta, and HuggingFace, along with knowledge-base, Qdrant, channel, and handler extensions. The framework exposes RESTful Open API and WebSocket communication, while a separate SvelteKit project supplies the BotSharp UI. It fits teams that want to self-host and compose multi-agent workflows inside a .NET application rather than adopt a single-provider runtime.
The backend can be started with dotnet run --project ./src/WebStarter/WebStarter.csproj -p SolutionName=BotSharp. Plugin Loader and Hooking load external components, while Agent Profile, Conversation & State, and Routing & Planning organize agent responsibilities, session state, and coordination. Provider plugins such as BotSharp.Plugin.OpenAI, BotSharp.Plugin.AnthropicAI, and BotSharp.Plugin.GoogleAI call supported model platforms. BotSharp.Plugin.KnowledgeBase and BotSharp.Plugin.Qdrant supply RAG-related interfaces and vector search, and BotSharp.OpenAPI plus WebSocket provide application-facing communication. Channel plugins including ChatHub, TelegramBots, WeChat, and Twilio can connect the application to messaging services; built-in MCP integration provides visual MCP management for model-initiated external calls.
- A C# team maintaining an enterprise information system that needs to add agents with distinct responsibilities to an existing workflow.
- A .NET application team that needs persisted conversation state and routing/planning for coordinated multi-agent tasks.
- Developers who need to integrate OpenAI, Anthropic, Gemini, DeepSeek, or HuggingFace through plugins in one application.
- An internal-assistant team building retrieval-augmented answers with a knowledge base and Qdrant vector search.
- A product team exposing conversational functionality through RESTful Open API, WebSocket, or Telegram, WeChat, and Twilio channels.
What are this agent's strengths and limitations?
- C# and .NET Core implementation with plugin and pipeline-flow execution is well aligned with existing .NET business applications.
- Agent Profile, conversation state, routing, and planning are explicit core modules for multi-agent orchestration.
- It combines multiple provider plugins with knowledge-base, Qdrant, messaging, RESTful Open API, and WebSocket integration points.
- Built-in MCP integration includes visual management for connecting model calls to external capabilities.
- It requires .NET 6+ and API-key configuration in `appsettings.json` for the chosen model provider.
- The administration UI is maintained in a separate BotSharp-UI repository and adds npm installation and development steps.
- The supplied material mentions reverse proxies, Nginx/IIS, and separate UI deployment but does not provide concrete production configuration examples.
- A2A, Computer Use, and Browser Use remain unchecked on the documented roadmap.
How do you install or deploy this agent?
Install the .NET 6+ SDK. Clone the repository, enter its directory, and run:dotnet run --project ./src/WebStarter/WebStarter.csproj -p SolutionName=BotSharp
On Windows, use:dotnet run --project .\src\WebStarter\WebStarter.csproj -p SolutionName=BotSharp
For the administration UI, separately clone https://github.com/SciSharp/BotSharp-UI, enter that directory, then run npm install and npm run dev. Before using a model plugin, configure the selected provider's API key in appsettings.json. If dependencies fail at startup, run dotnet restore.
How do you use this agent?
Start the backend and open http://localhost:5015/. Define an Agent Profile and its responsibilities, then select the needed plugins: for example, BotSharp.Plugin.OpenAI or BotSharp.Plugin.AnthropicAI for model access, and BotSharp.Plugin.KnowledgeBase or BotSharp.Plugin.Qdrant for retrieval. Connect a custom frontend through the RESTful Open API or WebSocket, or use channel plugins such as ChatHub, MetaMessenger, TelegramBots, Twilio, or WeChat. If you use the separate administration UI, run npm run dev in the BotSharp-UI project.