#### Lightweight, Real-time Debugging for AI Agents Debug your Agents in Real Time. Trace, analyze, and optimize instantly. Seamless with LangChain, Google ADK, OpenAI, and all major frameworks. **[Documentation](https://vllora.dev/docs)** | **[Issues](https://github.com/vllora/vllora/issues)**

## Quick Start First, install [Homebrew](https://brew.sh) if you haven't already, then: ```bash brew tap vllora/vllora brew install vllora ``` ### Start the vLLora: ```bash vllora ``` > The server will start on `http://localhost:9090` and the UI will be available at `http://localhost:9091`. vLLora uses OpenAI-compatible chat completions API, so when your AI agents make calls through vLLora, it automatically collects traces and debugging information for every interaction.

![vLLora Demo](https://raw.githubusercontent.com/vllora/vllora/main/assets/gifs/traces.gif)

### Test Send your First Request 1. **Configure API Keys**: Visit `http://localhost:9091` to configure your AI provider API keys through the UI 2. **Make a request** to see debugging in action: ```bash curl http://localhost:9090/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{ "model": "gpt-4o-mini", "messages": [{"role": "user", "content": "What is the capital of France?"}] }' ``` ### Rust streaming example (OpenAI-compatible) In `llm/examples/openai_stream_basic/src/main.rs` you can find a minimal Rust example that: - **Builds an OpenAI-style request** using `CreateChatCompletionRequestArgs` with: - `model("gpt-4.1-mini")` - a **system message**: `"You are a helpful assistant."` - a **user message**: `"Stream numbers 1 to 20 in separate lines."` - **Constructs a `VlloraLLMClient`** and configures credentials via: ```bash export VLLORA_OPENAI_API_KEY="your-openai-compatible-key" ``` Inside the example, the client is created roughly as: ```rust let client = VlloraLLMClient::new() .with_credentials(Credentials::ApiKey(ApiKeyCredentials { api_key: std::env::var("VLLORA_OPENAI_API_KEY") .expect("VLLORA_OPENAI_API_KEY must be set") })); ``` Then it **streams the completion** using the original OpenAI-style request: ```rust let mut stream = client .completions() .create_stream(openai_req) .await?; while let Some(chunk) = stream.next().await { let chunk = chunk?; for choice in chunk.choices { if let Some(delta) = choice.delta.content { print!("{delta}"); } } } ``` This will print the streamed response chunks (in this example, numbers 1 to 20) to stdout as they arrive. ## Features **Real-time Tracing** - Monitor AI agent interactions as they happen with live observability of calls, tool interactions, and agent workflow. See exactly what your agents are doing in real-time. ![Real-time Tracing](https://raw.githubusercontent.com/vllora/vllora/main/assets/images/traces-vllora.png) **MCP Support** - Full support for Model Context Protocol (MCP) servers, enabling seamless integration with external tools by connecting with MCP Servers through HTTP and SSE ![MCP Configuration](https://raw.githubusercontent.com/vllora/vllora/main/assets/images/mcp-config.png) ## Development To get started with development: 1. **Clone the repository**: ```bash git clone https://github.com/vllora/vllora.git cd vLLora cargo build --release ``` The binary will be available at `target/release/vlora`. 2. **Run tests**: ```bash cargo test ``` ## Contributing We welcome contributions! Please check out our [Contributing Guide](CONTRIBUTING.md) for guidelines on: - How to submit issues - How to submit pull requests - Code style conventions - Development workflow - Testing requirements Have a bug report or feature request? Check out our [Issues](https://github.com/vllora/vllora/issues) to see what's being worked on or to report a new issue. ## Roadmap Check out our [Roadmap](https://vllora.dev/docs/roadmap) to see what's coming next! ## License vLLora is [fair-code](https://faircode.io/) distributed under the [Elastic License 2.0 (ELv2)](https://github.com/vllora/vllora/blob/main/LICENSE.md). The inner package `llm` is distributed under the [Apache License 2.0](llm/LICENSE-APACHE). vLLora includes [Distri](https://distri.dev/) as an optional component for AI agent functionality. Distri is distributed under the [Elastic License 2.0 (ELv2)](https://github.com/distrihub/distri/blob/main/LICENSE) and is downloaded separately at runtime. Distri is a separate project maintained by [DistriHub](https://github.com/distrihub). - **Source Available**: Always visible vLLora source code - **Self-Hostable**: Deploy vLLora anywhere you need - **Extensible**: Add your own providers, tools, MCP servers, and custom functionality For Enterprise License, contact us at [hello@vllora.dev](mailto:hello@vllora.dev). Additional information about the license model can be found in the docs.

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