dynamic-agent

# Dynamic Agent

[![Rust](https://img.shields.io/badge/rust-stable-blue.svg)](https://www.rust-lang.org/)


Dynamic Agent is a flexible and configurable AI agent framework built in Rust. It provides a foundation for creating Retrieval-Augmented Generation (RAG) agents that can interact with users over WebSockets, leveraging multiple LLM providers and vector stores.

**Live Demo:** Experience Dynamic Agent in action at [thanon.dev/chat](https://thanon.dev/chat) (powered by the [Leptos Portfolio Admin](https://github.com/DevsHero/leptos_portfolio_admin) frontend).

## Key Features

*   **Multi-LLM Support:** Integrates with various Large Language Model providers for chat completion, text embedding, and query generation.
    *   Supported: Ollama, OpenAI, Anthropic, DeepSeek, DeepSeek, XAI, Groq.
    *   Easily configurable via environment variables or CLI arguments.
*   **Streaming and Thinking Process:** Supports both streaming responses and exposing the LLM's reasoning process.
    *   Stream responses token by token for a responsive user experience.
    *   Capture and stream the model's thinking process separately from the final response.
    *   Control thinking display and duration based on client capabilities.
*   **GitHub Flavored Markdown Support:** LLM responses can be formatted using GitHub Flavored Markdown, enabling rich text rendering on compatible frontends (e.g., code blocks, lists, bold/italics, tables).
*   **Multi-Vector Store Support:** Leverages the `vector-nexus` crate to connect to different vector databases for RAG.
    *   Supported: Redis, Qdrant, Chroma, Milvus, SurrealDB, Pinecone.
*   **Configurable RAG Pipeline:**
    *   Define agent behavior, intents, and prompt templates using local JSON files or Firebase Remote Config.
    *   `vector-nexus` automatically detects your vector data structure, eliminating manual `index_schema.json` creation.
    *   Supports LLM-driven intent classification and dynamic RAG query generation.
*   **Dynamic P