feat: agent platform initial commit
- 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
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Leopold
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"""Agent-Runner: führt einen Agent-Turn aus.
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Agents leben in der DB (Tabelle `agents`).
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Beim Start werden Code-Agents aus agents/*.py in DB gesynct (source='code').
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"""
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import json
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import time
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import logging
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from typing import Dict, Any, List
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import aiosqlite
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from db import get_agent, add_message, get_messages, touch_conversation
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from llm import chat
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from mcp_client import get_mcp_client
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logger = logging.getLogger("agent")
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async def sync_code_agents(db: aiosqlite.Connection):
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"""Lädt Code-Agents aus agents/*.py und synct sie in die DB."""
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from agents import discover_agents
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for instance in discover_agents().values():
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cfg = instance.config
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existing = await get_agent(db, cfg.id)
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if existing:
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# Update nur Code-Felder, behalte User-Overrides (enabled, custom)
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merged = {
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"id": cfg.id,
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"name": cfg.name,
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"description": cfg.description,
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"system_prompt": cfg.system_prompt,
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"allowed_tools": cfg.allowed_tools,
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"model": cfg.model,
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"temperature": cfg.temperature,
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"max_tokens": cfg.max_tokens,
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"enabled": existing.get("enabled", cfg.enabled),
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}
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await db.execute(
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"""UPDATE agents SET
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name=?, description=?, system_prompt=?, allowed_tools=?,
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model=?, temperature=?, max_tokens=?, updated_at=CURRENT_TIMESTAMP
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WHERE id=?""",
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(
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merged["name"], merged["description"], merged["system_prompt"],
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json.dumps(merged["allowed_tools"]),
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merged["model"], merged["temperature"], merged["max_tokens"],
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cfg.id,
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)
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)
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else:
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await db.execute(
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"""INSERT INTO agents (id, name, description, system_prompt, allowed_tools, model, temperature, max_tokens, enabled, source)
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VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, 'code')""",
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(
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cfg.id, cfg.name, cfg.description, cfg.system_prompt,
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json.dumps(cfg.allowed_tools),
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cfg.model, cfg.temperature, cfg.max_tokens,
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int(cfg.enabled),
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)
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)
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await db.commit()
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async def run_agent_turn(
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db: aiosqlite.Connection,
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agent_id: str,
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user_id: str,
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user_message: str,
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conversation_id: str,
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) -> Dict[str, Any]:
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"""Führt einen Agent-Turn aus: User-Message → LLM → optional Tool-Calls → Antwort."""
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agent = await get_agent(db, agent_id)
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if not agent:
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raise ValueError(f"Agent '{agent_id}' nicht gefunden")
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if not agent["enabled"]:
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raise ValueError(f"Agent '{agent_id}' ist deaktiviert")
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# User-Message speichern
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await add_message(db, conversation_id, "user", user_message)
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# History laden (letzte 20 Messages)
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history = await get_messages(db, conversation_id, limit=20)
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messages: List[dict] = [{"role": "system", "content": agent["system_prompt"]}]
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for row in history[:-1]: # ohne die gerade gespeicherte user-Message
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role, content, tool_calls_json, _, _ = row
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if role == "tool":
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continue
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msg = {"role": role, "content": content or ""}
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if tool_calls_json:
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try:
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msg["tool_calls"] = json.loads(tool_calls_json)
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except Exception:
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pass
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messages.append(msg)
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# MCP-Tools laden (gefiltert nach allowed_tools)
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mcp_client = get_mcp_client()
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tools = []
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try:
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all_tools = await mcp_client.get_tools_for_llm()
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if agent["allowed_tools"]:
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tools = [t for t in all_tools if t.get("function", {}).get("name") in agent["allowed_tools"]]
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else:
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tools = all_tools
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except Exception as e:
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logger.warning(f"MCP-Tools konnten nicht geladen werden: {e}")
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# LLM-Loop
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MAX_ITER = 10
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iterations = 0
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total_input = 0
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total_output = 0
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final_content = ""
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while iterations < MAX_ITER:
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iterations += 1
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kwargs = {}
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if agent["model"]:
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kwargs["model"] = agent["model"]
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start = time.time()
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response = await chat(
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messages=messages,
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tools=tools or None,
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temperature=agent["temperature"],
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max_tokens=agent["max_tokens"],
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**kwargs,
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)
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duration_ms = int((time.time() - start) * 1000)
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total_input += response.input_tokens
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total_output += response.output_tokens
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assistant_msg = {"role": "assistant", "content": response.content}
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if response.tool_calls:
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assistant_msg["tool_calls"] = [
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{
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"id": tc["id"],
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"type": "function",
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"function": {"name": tc["name"], "arguments": tc["arguments"]},
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}
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for tc in response.tool_calls
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]
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messages.append(assistant_msg)
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if not response.tool_calls:
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final_content = response.content
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break
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for tc in response.tool_calls:
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if agent["allowed_tools"] and tc["name"] not in agent["allowed_tools"]:
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messages.append({
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"role": "tool",
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"tool_call_id": tc["id"],
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"content": f"Tool '{tc['name']}' ist nicht erlaubt",
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})
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continue
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try:
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args = json.loads(tc["arguments"]) if isinstance(tc["arguments"], str) else tc["arguments"]
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except Exception:
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args = {}
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try:
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result = await mcp_client.call_tool(tc["name"], args)
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content_str = json.dumps(result, default=str)[:8000]
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except Exception as e:
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content_str = f"Tool-Fehler: {e}"
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messages.append({"role": "tool", "tool_call_id": tc["id"], "content": content_str})
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# Assistant-Message speichern
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await add_message(
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db, conversation_id, "assistant", final_content,
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tool_calls=response.tool_calls if (response and response.tool_calls) else None,
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token_count=total_input + total_output,
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)
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await touch_conversation(db, conversation_id)
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return {
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"content": final_content,
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"iterations": iterations,
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"input_tokens": total_input,
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"output_tokens": total_output,
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"duration_ms": duration_ms,
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}
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