Open Web Agent Rs MCP Server

@seemueller-io

代码仓库

基本信息

分类

AI 与智能体

许可证

MIT license

传输方式

stdio

作者

seemueller-io

关于 Open Web Agent Rs MCP Server

an mcp server with candle inference

#agent #rust#axum#llm #genaiscript

Open Web Agent Rs MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "open-web-agent-rs": {
      "command": "docker",
      "args": [
        "compose",
        "up",
        "-d",
        "searxng"
      ]
    }
  }
}

Open Web Agent Rs MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is open-web-agent-rs?

open-web-agent-rs is a Rust-based web agent that includes an embedded OpenAI‑compatible inference server. It supports only Gemma models and is designed for developers who want a lightweight, local web agent with custom inference.

How to use open-web-agent-rs?

Copy the .env.example to .env, install dependencies with bun i, start the local inference server by running cargo run --release -- --server inside local_inference_server, launch the SearXNG search engine with docker compose up -d searxng, then run bun dev to start the agent.

Key features of open-web-agent-rs

Rust‑based implementation for performance and reliability
Embedded OpenAI‑compatible inference server
Supports Gemma models only
Integrates with SearXNG for web search
Quick setup with Bun, Cargo, and Docker

Use cases of open-web-agent-rs

Creating a private web agent with local Gemma model inference
Experimenting with Rust‑based agent workflows
Building a self‑hosted, search‑enabled assistant

常见问题

Which models does open-web-agent-rs support?

It supports only Gemma models. Other model families are not compatible.

What are the runtime requirements?

You need Rust, Bun, and Docker installed. The local inference server is written in Rust and runs via Cargo.

Where does data live?

Data can be configured in the `.env` file. The SearXNG instance runs locally via Docker.

Are there any known limitations?

The inference server is embedded and supports only Gemma models, so flexibility is limited to that model family.

What transport or authentication does it use?

The README does not specify authentication or transport details. It uses an OpenAI‑compatible API interface locally.

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