94 lines
2.6 KiB
Markdown
94 lines
2.6 KiB
Markdown
|
# 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
|