agent-platform/agent_platform/agent.py

"""Agent-Runner: führt einen Agent-Turn aus.

Agents leben in der DB (Tabelle `agents`).
Beim Start werden Code-Agents aus agents/*.py in DB gesynct (source='code').
"""
import json
import time
import logging
from typing import Dict, Any, List
import aiosqlite

from db import get_agent, add_message, get_messages, touch_conversation
from llm import chat
from mcp_client import get_mcp_client


logger = logging.getLogger("agent")


async def sync_code_agents(db: aiosqlite.Connection):
    """Lädt Code-Agents aus agents/*.py und synct sie in die DB."""
    from agents import discover_agents
    for instance in discover_agents().values():
        cfg = instance.config
        existing = await get_agent(db, cfg.id)
        if existing:
            # Update nur Code-Felder, behalte User-Overrides (enabled, custom)
            merged = {
                "id": cfg.id,
                "name": cfg.name,
                "description": cfg.description,
                "system_prompt": cfg.system_prompt,
                "allowed_tools": cfg.allowed_tools,
                "model": cfg.model,
                "temperature": cfg.temperature,
                "max_tokens": cfg.max_tokens,
                "enabled": existing.get("enabled", cfg.enabled),
            }
            await db.execute(
                """UPDATE agents SET
                    name=?, description=?, system_prompt=?, allowed_tools=?,
                    model=?, temperature=?, max_tokens=?, updated_at=CURRENT_TIMESTAMP
                   WHERE id=?""",
                (
                    merged["name"], merged["description"], merged["system_prompt"],
                    json.dumps(merged["allowed_tools"]),
                    merged["model"], merged["temperature"], merged["max_tokens"],
                    cfg.id,
                )
            )
        else:
            await db.execute(
                """INSERT INTO agents (id, name, description, system_prompt, allowed_tools, model, temperature, max_tokens, enabled, source)
                   VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, 'code')""",
                (
                    cfg.id, cfg.name, cfg.description, cfg.system_prompt,
                    json.dumps(cfg.allowed_tools),
                    cfg.model, cfg.temperature, cfg.max_tokens,
                    int(cfg.enabled),
                )
            )
    await db.commit()


async def run_agent_turn(
    db: aiosqlite.Connection,
    agent_id: str,
    user_id: str,
    user_message: str,
    conversation_id: str,
) -> Dict[str, Any]:
    """Führt einen Agent-Turn aus: User-Message → LLM → optional Tool-Calls → Antwort."""
    agent = await get_agent(db, agent_id)
    if not agent:
        raise ValueError(f"Agent '{agent_id}' nicht gefunden")
    if not agent["enabled"]:
        raise ValueError(f"Agent '{agent_id}' ist deaktiviert")

    # User-Message speichern
    await add_message(db, conversation_id, "user", user_message)

    # History laden (letzte 20 Messages)
    history = await get_messages(db, conversation_id, limit=20)
    messages: List[dict] = [{"role": "system", "content": agent["system_prompt"]}]
    for row in history[:-1]:  # ohne die gerade gespeicherte user-Message
        role, content, tool_calls_json, _, _ = row
        if role == "tool":
            continue
        msg = {"role": role, "content": content or ""}
        if tool_calls_json:
            try:
                msg["tool_calls"] = json.loads(tool_calls_json)
            except Exception:
                pass
        messages.append(msg)

    # MCP-Tools laden (gefiltert nach allowed_tools)
    mcp_client = get_mcp_client()
    tools = []
    try:
        all_tools = await mcp_client.get_tools_for_llm()
        if agent["allowed_tools"]:
            tools = [t for t in all_tools if t.get("function", {}).get("name") in agent["allowed_tools"]]
        else:
            tools = all_tools
    except Exception as e:
        logger.warning(f"MCP-Tools konnten nicht geladen werden: {e}")

    # LLM-Loop
    MAX_ITER = 10
    iterations = 0
    total_input = 0
    total_output = 0
    final_content = ""

    while iterations < MAX_ITER:
        iterations += 1
        kwargs = {}
        if agent["model"]:
            kwargs["model"] = agent["model"]

        start = time.time()
        response = await chat(
            messages=messages,
            tools=tools or None,
            temperature=agent["temperature"],
            max_tokens=agent["max_tokens"],
            **kwargs,
        )
        duration_ms = int((time.time() - start) * 1000)
        total_input += response.input_tokens
        total_output += response.output_tokens

        assistant_msg = {"role": "assistant", "content": response.content}
        if response.tool_calls:
            assistant_msg["tool_calls"] = [
                {
                    "id": tc["id"],
                    "type": "function",
                    "function": {"name": tc["name"], "arguments": tc["arguments"]},
                }
                for tc in response.tool_calls
            ]
        messages.append(assistant_msg)

        if not response.tool_calls:
            final_content = response.content
            break

        for tc in response.tool_calls:
            if agent["allowed_tools"] and tc["name"] not in agent["allowed_tools"]:
                messages.append({
                    "role": "tool",
                    "tool_call_id": tc["id"],
                    "content": f"Tool '{tc['name']}' ist nicht erlaubt",
                })
                continue
            try:
                args = json.loads(tc["arguments"]) if isinstance(tc["arguments"], str) else tc["arguments"]
            except Exception:
                args = {}
            try:
                result = await mcp_client.call_tool(tc["name"], args)
                content_str = json.dumps(result, default=str)[:8000]
            except Exception as e:
                content_str = f"Tool-Fehler: {e}"
            messages.append({"role": "tool", "tool_call_id": tc["id"], "content": content_str})

    # Assistant-Message speichern
    await add_message(
        db, conversation_id, "assistant", final_content,
        tool_calls=response.tool_calls if (response and response.tool_calls) else None,
        token_count=total_input + total_output,
    )
    await touch_conversation(db, conversation_id)

    return {
        "content": final_content,
        "iterations": iterations,
        "input_tokens": total_input,
        "output_tokens": total_output,
        "duration_ms": duration_ms,
    }