Metadata-Version: 2.4
Name: wger-agent
Version: 0.1.22
Summary: Wger Workout Manager — exercise database, workout routines, nutrition plans, body measurements, and progress tracking.
Author-email: Audel Rouhi <knucklessg1@gmail.com>
License: MIT
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: MIT License
Classifier: Environment :: Console
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: agent-utilities[mcp]>=0.2.31
Provides-Extra: agent
Requires-Dist: agent-utilities[agent,logfire]>=0.2.31; extra == "agent"
Provides-Extra: all
Requires-Dist: agent-utilities[agent,logfire,mcp]>=0.2.31; extra == "all"
Dynamic: license-file

# Wger - A2A | AG-UI | MCP

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*Version: 0.1.22*

## Overview

**Wger MCP Server + A2A Agent**

Wger Workout Manager — exercise database, workout routines, nutrition plans, body measurements, and progress tracking.

This repository is actively maintained - Contributions are welcome!

## MCP

### Using as an MCP Server

The MCP Server can be run in two modes: `stdio` (for local testing) or `http` (for networked access).

#### Environment Variables

*   `WGER_INSTANCE`: The URL of the target service.
*   `WGER_ACCESS_TOKEN`: The API token or access token.

#### Run in stdio mode (default):
```bash
export WGER_INSTANCE="http://localhost:8080"
export WGER_ACCESS_TOKEN="your_token"
wger-mcp --transport "stdio"
```

#### Run in HTTP mode:
```bash
export WGER_INSTANCE="http://localhost:8080"
export WGER_ACCESS_TOKEN="your_token"
wger-mcp --transport "http" --host "0.0.0.0" --port "8000"
```

## A2A Agent

### Run A2A Server
```bash
export WGER_INSTANCE="http://localhost:8080"
export WGER_ACCESS_TOKEN="your_token"
wger-agent --provider openai --model-id gpt-4o --api-key sk-...
```

## Docker

### Build

```bash
docker build -t wger-agent .
```

### Run MCP Server

```bash
docker run -d \
  --name wger-agent \
  -p 8000:8000 \
  -e TRANSPORT=http \
  -e WGER_INSTANCE="http://your-service:8080" \
  -e WGER_ACCESS_TOKEN="your_token" \
  knucklessg1/wger-agent:latest
```

### Deploy with Docker Compose

```yaml
services:
  wger-agent:
    image: knucklessg1/wger-agent:latest
    environment:
      - HOST=0.0.0.0
      - PORT=8000
      - TRANSPORT=http
      - WGER_INSTANCE=http://your-service:8080
      - WGER_ACCESS_TOKEN=your_token
    ports:
      - 8000:8000
```

#### Configure `mcp.json` for AI Integration (e.g. Claude Desktop)

```json
{
  "mcpServers": {
    "wger": {
      "command": "uv",
      "args": [
        "run",
        "--with",
        "wger-agent",
        "wger-mcp"
      ],
      "env": {
        "WGER_INSTANCE": "http://your-service:8080",
        "WGER_ACCESS_TOKEN": "your_token"
      }
    }
  }
}
```

## Install Python Package

```bash
python -m pip install wger-agent
```
```bash
uv pip install wger-agent
```

## Repository Owners

<img width="100%" height="180em" src="https://github-readme-stats.vercel.app/api?username=Knucklessg1&show_icons=true&hide_border=true&&count_private=true&include_all_commits=true" />

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## Graph Architecture

This agent uses `pydantic-graph` orchestration for intelligent routing and optimal context management.

```mermaid
---
title: Wger Agent Graph Agent
---
stateDiagram-v2
  [*] --> RouterNode: User Query
  RouterNode --> DomainNode: Classified Domain
  RouterNode --> [*]: Low confidence / Error
  DomainNode --> [*]: Domain Result
```

- **RouterNode**: A fast, lightweight LLM (e.g., `gpt-4o-mini`) that classifies the user's query into one of the specialized domains.
- **DomainNode**: The executor node. For the selected domain, it dynamically sets environment variables to temporarily enable ONLY the tools relevant to that domain, creating a highly focused sub-agent (e.g., `gpt-4o`) to complete the request. This preserves LLM context and prevents tool hallucination.
