open-web-agent-rs

A Rust-based web agent with an embedded OpenAI-compatible inference server (supports Gemma models only). It is packaged and deployed as a container.

Project Structure

This project is organized as a Cargo workspace with the following crates:

• crates
  • agent-server: The main web agent server
  • inference-engine: An embedded OpenAI-compatible inference server for Gemma models
• packages
  • genaiscript: GenaiScript scripts
  • genaiscript-rust-shim: An embedded OpenAI-compatible inference server for Gemma models

Acknowledgements

Special gratitude and thanks:

• OpenAI: For standards that offer consensus in chaos.
• The Rust community for their excellent tools and libraries
• Google's Gemma team for gemma-3-1b-it

Open source projects that have inspired and enabled this work

• GenAIScript: Automatable GenAI Scripting
• axum: Web framework for building APIs
• tokio: Asynchronous runtime for efficient concurrency
• serde: Serialization/deserialization framework
• rmcp: Model Context Protocol SDK for agent communication
• sled: Embedded database for persistent storage
• tower-http: HTTP middleware components
• candle-core: ML framework for efficient tensor operations
• candle-transformers: Implementation of transformer models in Candle
• hf-hub: Client for downloading models from Hugging Face
• tokenizers: Fast text tokenization for ML models
• safetensors: Secure format for storing tensors

Architecture Diagram

%% High‑fidelity architecture diagram – client‑ready
flowchart LR
    %% ─────────────── Agent‑side ───────────────
    subgraph AGENT_SERVER["Agent Server"]
        direction TB
        AS["Agent Server"]:::core -->|exposes| MCP[["Model Context Protocol API"]]:::api
        AS -->|serves| UI["MCP Inspector UI"]:::ui

        subgraph AGENTS["Agents"]
            direction TB
            A_SEARCH["Search Agent"] -->|uses| SEARX
            A_NEWS["News Agent"]   -->|uses| SEARX
            A_SCRAPE["Web Scrape Agent"] -->|uses| BROWSER
            A_IMG["Image Generator Agent"]-->|uses| EXTERNAL_API
            A_RESEARCH["Deep Research Agent"] -->|leverages| SEARX
        end

        %% Individual fan‑out lines (no “&”)
        MCP -->|routes| A_SEARCH
        MCP -->|routes| A_NEWS
        MCP -->|routes| A_SCRAPE
        MCP -->|routes| A_IMG
        MCP -->|routes| A_RESEARCH
    end

    %% ─────────────── Local inference ───────────────
    subgraph INFERENCE["Inference Engine"]
        direction TB
        LIE["Inference Engine"]:::core -->|loads| MODELS["Gemma Models"]:::model
        LIE -->|exposes| OPENAI_API["OpenAI‑compatible API"]:::api
        MODELS -->|runs on| ACCEL

        subgraph ACCEL["Hardware Acceleration"]
            direction LR
            METAL[Metal]
            CUDA[CUDA]
            CPU[CPU]
        end
    end

    %% ─────────────── External bits ───────────────
    subgraph EXTERNAL["External Components"]
        direction TB
        SEARX["SearXNG Search"]:::ext
        BROWSER["Chromium Browser"]:::ext
        EXTERNAL_API["Public OpenAI API"]:::ext
    end

    %% ─────────────── Clients ───────────────
    subgraph CLIENTS["Client Applications"]
        CLIENT["MCP‑aware Apps"]:::client
    end

    %% ─────────────── Interactions ───────────────
    CLIENT -- "HTTPS / WebSocket" --> MCP
    AS --> |"may call"| OPENAI_API
    AS --> |"optional"| EXTERNAL_API

    %% ─────────────── Styling ───────────────
    classDef core   fill:#A9CEF4,stroke:#36494E,stroke-width:2px,color:#000;
    classDef api    fill:#7EA0B7,stroke:#36494E,stroke-width:2px,color:#000;
    classDef ui     fill:#A9CEF4,stroke:#597081,stroke-dasharray:4 3,color:#000;
    classDef model  fill:#A9CEF4,stroke:#36494E,stroke-width:2px,color:#000;
    classDef ext    fill:#B5D999,stroke:#36494E,stroke-width:2px,color:#000;
    classDef client fill:#FFE69A,stroke:#36494E,stroke-width:2px,color:#000;

Setup

1. Clone the repository
2. Copy the example environment file:

   cp .env.example .env
   

3. Install JavaScript dependencies:

   bun i
   

4. Start the SearXNG search engine:

   docker compose up -d searxng
   

Running the Project

Inference Engine

To run the local inference engine:

cd crates/inference-engine
cargo run --release -- --server

Agent Server

To run the agent server:

cargo run -p agent-server

Development Mode

For development with automatic reloading:

bun dev

Building

To build all crates in the workspace:

cargo build

To build a specific crate:

cargo build -p agent-server
# or
cargo build -p inference-engine