agent-platform/README.md

# Agent Platform

Eine schlanke, business-taugliche Agent-Plattform mit LiteLLM, Pydantic AI und MCP.

## Architektur

```
┌─────────────────────────────────────────────┐
│  agent-platform (Port 8000)                 │
│  - FastAPI + LiteLLM + Pydantic AI          │
│  - HTMX-UI (kein JS-Build)                  │
│  - SQLite                                   │
│  - Audit-Log, Auth                          │
└──────────────────┬──────────────────────────┘
                   │ MCP (HTTP)
                   ▼
┌─────────────────────────────────────────────┐
│  mcp-tools (Port 8501, intern)              │
│  - fastmcp                                  │
│  - 6 generische Tools                       │
└─────────────────────────────────────────────┘
```

## Quickstart

```bash
# 1. Env-Datei anlegen
cp .env.example .env
# .env editieren: LLM_API_KEY und AUTH_TOKEN setzen

# 2. Starten
docker compose up -d --build

# 3. UI öffnen
http://localhost:8000

# 4. Ersten Agent anlegen
# Im UI: Agents → "Neuen Agent erstellen"
# System-Prompt: "Du bist ein hilfreicher Assistent."
# Erlaubte Tools: "echo, calculate, get_time"
```

## API

```bash
# Health
curl http://localhost:8000/health

# Agents
curl -H "Authorization: Bearer $AUTH_TOKEN" http://localhost:8000/api/agents

# Chat
curl -X POST -H "Authorization: Bearer $AUTH_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"message": "Hallo!"}' \
  http://localhost:8000/api/chat/general_assistant

# MCP-Tools
curl http://localhost:8000/api/tools
```

## Verfügbare MCP-Tools

- `echo(text)` - Echo-Tool
- `get_time(timezone_name)` - Aktuelle Zeit
- `calculate(expression)` - Mathe-Ausdruck (sicher)
- `http_get(url)` - HTTP-Request (SSRF-Schutz)
- `list_env(prefix)` - Umgebungsvariablen
- `json_format(data)` - JSON formatieren

## Konfiguration

Alle Env-Vars sind in `.env.example` dokumentiert.

## Entwicklung

```bash
# Backend lokal starten (ohne Docker)
cd agent_platform
pip install -e .
LLM_API_KEY=sk-xxx uvicorn api:app --reload

# MCP-Server lokal
cd mcp_tools
pip install -r requirements.txt
python server.py
```

## Ressourcen

- agent-platform: 256 MB RAM, 0.5 CPU
- mcp-tools: 128 MB RAM, 0.25 CPU
- SQLite: 5-10 MB

## Deploy (Coolify)

1. In Coolify: **Projects → New Project** → name it (e.g. `agent-platform`).
2. Inside the project: **Resources → New → Docker Compose**.
3. **Repository URL**: `https://forgejo.media-on.de/Leopoldadmin/agent-platform.git`, **Branch**: `main`.
4. **Domain**: bind a hostname to the `agent-platform` service (Traefik routes via the `expose: 8000` label).
5. **Environment Variables**: set `AUTH_TOKEN` (≥32 chars, NOT `change-me-in-production`) and `LLM_API_KEY`; other vars can keep their `.env.example` defaults.
6. Click **Deploy** — Coolify builds the two images, creates the `agent-data` volume, and brings up the stack.
7. **Health check**: `curl https://<your-domain>/health` returns `{"status":"ok"}` once `mcp-tools` is healthy and `agent-platform` is up.