Leopold
64b840c94c
- FastAPI + HTMX UI: dashboard, agents CRUD, chat, audit, tools - SQLite schema: agents, conversations, messages, audit, sessions - Code-agent sync (agents/*.py -> DB on startup) - MCP client with health check - Token auth (Bearer + session) - LiteLLM integration via llm.py - Docker Compose: agent-platform + mcp-tools services - Smoke tests pass: /health 200, /api/agents 200, / 401
94 lines
2.6 KiB
Markdown
94 lines
2.6 KiB
Markdown
# Agent Platform
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Eine schlanke, business-taugliche Agent-Plattform mit LiteLLM, Pydantic AI und MCP.
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## Architektur
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```
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┌─────────────────────────────────────────────┐
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│ agent-platform (Port 8000) │
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│ - FastAPI + LiteLLM + Pydantic AI │
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│ - HTMX-UI (kein JS-Build) │
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│ - SQLite │
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│ - Audit-Log, Auth │
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└──────────────────┬──────────────────────────┘
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│ MCP (HTTP)
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▼
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┌─────────────────────────────────────────────┐
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│ mcp-tools (Port 8501, intern) │
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│ - fastmcp │
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│ - 6 generische Tools │
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└─────────────────────────────────────────────┘
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```
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## Quickstart
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```bash
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# 1. Env-Datei anlegen
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cp .env.example .env
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# .env editieren: LLM_API_KEY und AUTH_TOKEN setzen
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# 2. Starten
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docker compose up -d --build
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# 3. UI öffnen
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http://localhost:8000
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# 4. Ersten Agent anlegen
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# Im UI: Agents → "Neuen Agent erstellen"
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# System-Prompt: "Du bist ein hilfreicher Assistent."
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# Erlaubte Tools: "echo, calculate, get_time"
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```
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## API
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```bash
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# Health
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curl http://localhost:8000/health
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# Agents
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curl -H "Authorization: Bearer $AUTH_TOKEN" http://localhost:8000/api/agents
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# Chat
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curl -X POST -H "Authorization: Bearer $AUTH_TOKEN" \
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-H "Content-Type: application/json" \
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-d '{"message": "Hallo!"}' \
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http://localhost:8000/api/chat/general_assistant
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# MCP-Tools
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curl http://localhost:8000/api/tools
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```
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## Verfügbare MCP-Tools
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- `echo(text)` - Echo-Tool
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- `get_time(timezone_name)` - Aktuelle Zeit
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- `calculate(expression)` - Mathe-Ausdruck (sicher)
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- `http_get(url)` - HTTP-Request (SSRF-Schutz)
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- `list_env(prefix)` - Umgebungsvariablen
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- `json_format(data)` - JSON formatieren
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## Konfiguration
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Alle Env-Vars sind in `.env.example` dokumentiert.
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## Entwicklung
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```bash
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# Backend lokal starten (ohne Docker)
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cd agent_platform
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pip install -e .
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LLM_API_KEY=sk-xxx uvicorn api:app --reload
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# MCP-Server lokal
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cd mcp_tools
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pip install -r requirements.txt
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python server.py
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```
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## Ressourcen
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- agent-platform: 256 MB RAM, 0.5 CPU
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- mcp-tools: 128 MB RAM, 0.25 CPU
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- SQLite: 5-10 MB
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