BotSharp Agent Console
Create and manage AI assistants, agents, and conversations from one web interface.
- Source repo
- SciSharp/BotSharp-UI
- Stars
- ★ 157
- Last updated
- 11d ago
- License
- Apache-2.0
- Primary language
- Svelte
- FA score
- 43/100 · Major gaps
At a glance
- How it runs
- Works with
- Portable with changes
- Setup effort
- Medium · a few setup steps
- You'll need
- Typical use
- BotSharp users who need a browser interface for creating and managing AI assistants.
- Not a fit if
- Teams needing a fully local app with no backend service
- Developers seeking direct MCP or named model API integration
- Source review
- 43/100 · Major gaps 2 safety controls not found
What does this agent do, and when should you use it?
BotSharp UI is a web app for managing agents and conversations, including creating agents and managing existing ones. Its Node-based agent-building experience is intended to help users create an AI assistant quickly. The app is written in SvelteKit v2 and uses BotSharp for LLM services. The same interface also runs as a Tauri desktop app using the system WebView. The deployment guide describes Azure Static Web Apps; desktop builds require Rust, and Windows also requires the Desktop development with C++ workload from Visual Studio Installer.
After cloning the repository and installing npm dependencies, users can start the SvelteKit development server and use the UI to create agents, manage existing agents, and work with conversations. The app calls LLM services backed by BotSharp; the README does not specify concrete APIs, model providers, or agent configuration fields. Users can also launch the same web app through Tauri, whose desktop shell exposes no native commands to the page. A frontend production build can be deployed to Azure Static Web Apps using the documented CLI workflow.
- BotSharp users who need a browser interface for creating and managing AI assistants.
- Teams that need to review and manage conversations associated with existing agents.
- Developers who want to use the Node-based builder to create a new assistant quickly.
- Teams that want to run the same management interface as a Windows or macOS desktop app.
- Maintainers deploying the web interface to Azure Static Web Apps.
How do you install or deploy this agent?
Node.js/npm are required; desktop builds also require Rust, and Windows requires the “Desktop development with C++” workload from Visual Studio Installer. The repository documents browser development and build commands:
git clone https://github.com/SciSharp/BotSharp-UI
cd BotSharp-UI
npm install
npm run devTo create a production build:
npm run buildFor Azure Static Web Apps deployment, the guide calls for a credential-verified build:
npm run build:verified -- --mode production
npm install -g @azure/static-web-apps-cli
swa deploy ./build/ --env production --deployment-token {token}The deployment token must be supplied by the deployment environment. The README does not explain how to deploy or configure the BotSharp backend.
How do you use this agent?
Start the development server, open the app in a browser, and use the UI to create agents and manage agents and conversations. To override .env values, create .env.local. To select an environment mode, run:
npm run dev -- --mode botsharpDesktop development and build commands are:
npm run tauri:dev
npm run tauri:dev -- --mode staging
npm run tauri:build -- --mode stagingWithout --mode, Tauri development looks for .env.development and builds look for .env.production; the committed .env supplies defaults. The README does not provide a standalone API usage method or first-time BotSharp service configuration steps.
What are this agent's strengths and limitations?
- One interface supports creating agents, managing agents, and handling conversations.
- Built with SvelteKit v2 and includes a Node-based agent-building experience.
- The browser and Tauri desktop versions use the same app interface, with no page-facing native commands.
- Documents Azure Static Web Apps deployment and uses build:verified to check that login credentials are absent from distributable output.
- LLM services depend on BotSharp; the README does not describe other backend or model-provider paths.
- The README omits BotSharp backend installation, configuration, and credential requirements, leaving the full deployment process unclear.
- Desktop development and builds require Rust; Windows also needs the Visual Studio C++ workload.
- npm run build does not perform credential cleanup and verification; the guide says to use build:verified for distribution.
How does this agent compare with similar options?
Key facts side by side with the most closely related agents.
| Agent | Source review | Form / cost | Stars | Updated | Language | Full support on |
|---|---|---|---|---|---|---|
| BotSharp Agent Console This agent | 43 · Major gaps | Web app | ★ 157 | 11d ago | Svelte | — |
| WorldX AI World Builder | 56 · Major gaps | Web appFree + model costs | ★ 1.5k | 19d ago | TypeScript | OpenAI API |
| Image-to-Editable-PPT Skill | 53 · Major gaps | Agent plugin / skillFree + model costs | ★ 2.9k | 24d ago | Python | Codex |
| ArcReel Video Creation Workspace | 52 · Major gaps | Self-hosted serviceFree + model costs | ★ 5.4k | today | Python | OpenAI API · Claude API |
How does FollowAgents rate this agent?
Why each dimension lost points
The README says the UI builds and manages agents and conversations through BotSharp services, and that the desktop shell exposes no native page commands. This gives limited capability and data-flow context, so least privilege and data-flow transparency score 1 each. There is no evidence of user confirmation, operation rollback, or protections for agent data, so those criteria score 0. The README says distributable builds clear and check embedded credentials, but the committed .env contains local login-prefill credentials and a plain build does not run that check; sensitive-data handling therefore scores 2 rather than 3. The dependency list and workflow show no security audit or pinned-version evidence. External effects and source attribution are only partly described, so each scores 1.
The README's development, build, and Tauri instructions generally match package.json scripts, and CI uses build:verified, supporting a self-consistency score of 2. Installation depends on npm packages and an external BotSharp service, but the supplied material gives no availability or dependency-locking evidence, so dependency availability scores 1. No failure-message or fault-handling behavior is shown, so failure messages score 0.
The materials describe an Agent and conversation management UI and cover browser and Tauri desktop development with environment configuration, supporting scores of 2 for audience and scenarios and environment fit. The desktop shell's lack of native commands is a useful boundary, but Agent capability limits and unsuitable scenarios are sparsely described, so capability boundaries score 1. Guidance on when to select or invoke the product is limited, so trigger precision scores 1.
The README organizes material into installation, development, building, desktop, deployment, and customization, with copyable commands; information architecture and install notes score 2 each. Product and script names are mostly consistent, so naming stability scores 2. Deployment, desktop icon, and environment-mode examples are included, but there is no FAQ, so examples and FAQ score 1. The credential-bundling risk, adapter requirement, and omitted mobile icon sizes are called out, supporting 2 for known limitations. The repository includes the full Apache 2.0 license text, scoring 3. There is no changelog or version history, so versioning and changelog score 0. Maintainer identity and maintenance commitments are unclear, so maintenance responsibility scores 1.
The UI's stated purpose is centralized Agent and conversation building and management, with browser and desktop entry points, so output usability scores 2. The Node-based building experience and unified management provide stated value beyond a basic backend service, so marginal value scores 2. The materials provide no deployment cost, resource requirements, or operational-benefit data, so cost-benefit scores 1.
The README's install and build commands and credential-check description can be matched to package.json scripts and the CI build command, supporting a claim-traceability score of 2. The documentation, manifest, and workflow corroborate some build-path claims, but no implementation, tests, or run results are supplied to cross-check product behavior, so cross-source corroboration scores 1. The README distinguishes local prefill credentials, distributable-build checks, and environment-file fallback risk, supporting a score of 2 for separating facts and constraints; this assessment does not infer unprovided runtime results.
- Not found in source: confirmation before actingTurn on (or add) a confirmation step before it acts, and try it in a sandbox or test environment before real data.
- Not found in source: rollback or recovery pathBack up first, or work on a git branch or snapshot, so its changes can be undone.
- The committed .env contains local login-prefill credentials. Distributable builds rely on build:verified to clear and check them; plain npm run build does not provide the same protection.
- The materials do not describe confirmation, rollback, failure handling, or data retention for Agent operations; verify these behaviors and the BotSharp backend data flow before deployment.
- This assessment uses only the supplied static materials; no build, tests, or runtime behavior were executed or verified.
FAQ
Can this UI provide LLM services without a BotSharp backend?
Can agent management be accessed through an API or MCP?
Can the desktop app call native operating-system features?
How should login credentials be kept out of a deployment bundle?
npm run build:verified; it blanks the public login values used for local prefill and checks the build output. Running npm run build alone does not perform this check.