feat(brain): Phase B — Vector-DB-Memory, Conversation-Loop, Skills, Tool-Use

OpenClaw (aria-core) ist raus, ARIA laeuft jetzt mit eigenem Agent-Framework
im aria-brain Container. Vector-DB-basiertes Gedaechtnis statt Sessions,
eigener Conversation-Loop mit Hot+Cold-Memory + Rolling Window, Tool-Use
fuer Skills, Memory-Destillat-Pipeline.

aria-brain/ (neuer Container)
  - main.py            FastAPI auf 8080, alle Endpoints
  - agent.py           Conversation-Loop mit Tool-Use (skill_create + run_<skill>)
  - conversation.py    Rolling Window, JSONL-Persistenz, Distill-Marker
  - proxy_client.py    httpx-Wrapper zum Claude-Proxy, OpenAI-Format
  - prompts.py         System-Prompt aus Hot+Cold+Skills
  - migration.py       Markdown-Parser fuer brain-import/ → atomare Memories
  - skills.py          Filesystem-Layer fuer /data/skills/<name>/ (Python-only,
                       venv pro Skill, tar.gz Export/Import, Run-Logs)
  - memory/            Embedder (sentence-transformers, multilingual MiniLM)
                       + VectorStore (Qdrant-Wrapper)

docker-compose.yml
  - aria-core (OpenClaw) raus, openclaw-config Volume raus
  - aria-brain Service (FastAPI + Memory)
  - aria-qdrant Service (Vector-DB) mit Bind-Mount aria-data/brain/qdrant/
  - Diagnostic teilt jetzt Netzwerk mit Bridge (vorher: aria-core)
  - Brain bekommt SSH-Mount fuer aria-wohnung + /import fuer brain-import/

bridge/aria_bridge.py
  - send_to_core → HTTP-Call an aria-brain:8080/chat (statt OpenClaw-WS)
  - aria-core-spezifische Handler raus: doctor_fix, aria_restart,
    aria_session_reset, Auto-Compact-Logik, OpenClaw-Handshake
  - Generischer container_restart-Handler (Whitelist Bridge/Brain/Qdrant)
  - Side-Channel-Events aus /chat-Response (z.B. skill_created) werden
    als RVS-Events forwarded
  - file_list_request / file_delete_request → an Diagnostic forwarded
  - Tote OpenClaw-Connection-Logik bleibt im Code als Referenz (nicht aktiv)

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

aria-brain/Dockerfile

@@ -0,0 +1,35 @@
+# ════════════════════════════════════════════════════════════
+#  ARIA Brain — Agent + Memory Container
+#
+#  FastAPI-Server mit Vector-DB-Memory (Qdrant).
+#  Spricht via HTTP/WebSocket mit Bridge und Diagnostic.
+#  LLM-Calls gehen ueber den Proxy (claude-max-api-proxy).
+# ════════════════════════════════════════════════════════════
+
+FROM python:3.12-slim
+
+# System-Tools die Skills brauchen koennten (curl, jq, git, ssh-client,
+# Build-Basics fuer venv-Compiles). Bewusst sparsam — alles weitere
+# bringt der Skill selbst mit (siehe execution=local-bin).
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    curl \
+    jq \
+    git \
+    openssh-client \
+    ca-certificates \
+ && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY . .
+
+# Embedding-Model-Cache und Skills landen unter /data (Volume)
+ENV SENTENCE_TRANSFORMERS_HOME=/data/_models
+ENV ARIA_DATA_DIR=/data
+
+EXPOSE 8080
+
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]

aria-brain/agent.py

@@ -0,0 +1,385 @@
+"""
+Conversation-Loop. Eine Anfrage von Stefan, eine Antwort von ARIA.
+
+Pro Turn:
+  1. user-Turn an die laufende Conversation appenden
+  2. Hot Memory holen (alle pinned Punkte)
+  3. Cold Memory holen (Top-K semantisch zur user-Nachricht)
+  4. System-Prompt aus Hot+Cold bauen
+  5. Messages = [system, *window, user]
+  6. Claude via Proxy aufrufen
+  7. Assistant-Reply in Conversation appenden + zurueckgeben
+
+Memory-Destillat laeuft asynchron NACH dem Reply, gesteuert vom
+/chat-Endpoint ueber BackgroundTasks.
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+from typing import Optional
+
+from conversation import Conversation, Turn
+from memory import Embedder, VectorStore, MemoryPoint
+from prompts import build_system_prompt
+from proxy_client import ProxyClient, Message as ProxyMessage
+import skills as skills_mod
+
+logger = logging.getLogger(__name__)
+
+
+# Meta-Tool: ARIA kann selbst neue Skills bauen
+META_TOOLS = [
+    {
+        "type": "function",
+        "function": {
+            "name": "skill_create",
+            "description": (
+                "Erstelle einen neuen Skill (wiederverwendbare Faehigkeit). "
+                "Skills sind IMMER Python — jeder Skill bekommt seine eigene venv "
+                "mit den pip_packages die er braucht.\n\n"
+                "HARTE REGEL — IMMER Skill anlegen wenn: die Loesung erfordert eine "
+                "pip-Library. Sonst muesste der Install bei jedem Container-Restart "
+                "neu laufen (Brain hat keinen persistenten State ausser /data/skills/).\n\n"
+                "Sonst NUR wenn ALLE Kriterien erfuellt sind:\n"
+                "  1) wiederkehrend (Aufgabe kommt realistisch nochmal),\n"
+                "  2) nicht-trivial (mehrere Schritte),\n"
+                "  3) parametrisierbar (nimmt Eingaben, gibt Ergebnis),\n"
+                "  4) wiederverwendbar als ganzes Paket.\n"
+                "NICHT fuer einzelne Shell-Befehle (date, hostname, ls etc.) und "
+                "nicht fuer Einmal-Faelle. Stefan kann Skill-Erstellung explizit "
+                "triggern (\"bau daraus einen Skill\").\n\n"
+                "Wenn etwas nur via apt-Paket geht — Stefan fragen ob es ins "
+                "Brain-Dockerfile soll, NICHT als Skill bauen."
+            ),
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "name": {"type": "string", "description": "kurz, kebab-case, a-z 0-9 - _"},
+                    "description": {"type": "string", "description": "Was kann der Skill? 1 Satz."},
+                    "entry_code": {
+                        "type": "string",
+                        "description": (
+                            "Python-Code. Args lesen via os.environ['ARG_NAME']. "
+                            "Resultat per print() (stdout) zurueck. Bei Fehler: "
+                            "non-zero exit (sys.exit(1) o.ae.)."
+                        ),
+                    },
+                    "readme": {"type": "string", "description": "Markdown — was macht der Skill, Beispiel-Aufrufe"},
+                    "pip_packages": {
+                        "type": "array",
+                        "items": {"type": "string"},
+                        "description": "pip-Pakete die in der venv installiert werden (z.B. requests, yt-dlp, pypdf)",
+                    },
+                    "args": {
+                        "type": "array",
+                        "items": {"type": "object"},
+                        "description": "Argumente-Schema [{name, type, required, description}]",
+                    },
+                },
+                "required": ["name", "description", "entry_code"],
+            },
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "skill_list",
+            "description": "Zeigt alle Skills (inkl. deaktivierte). Sollte selten noetig sein — die Liste steht eh im System-Prompt.",
+            "parameters": {"type": "object", "properties": {}},
+        },
+    },
+]
+
+
+def _skill_to_tool(s: dict) -> dict:
+    """Mappt einen Skill auf ein OpenAI-Function-Tool."""
+    args = s.get("args") or []
+    props = {}
+    required = []
+    for a in args:
+        if not isinstance(a, dict):
+            continue
+        name = a.get("name") or ""
+        if not name:
+            continue
+        props[name] = {
+            "type": a.get("type", "string"),
+            "description": a.get("description", ""),
+        }
+        if a.get("required"):
+            required.append(name)
+    return {
+        "type": "function",
+        "function": {
+            "name": f"run_{s['name']}",
+            "description": s.get("description", "(ohne Beschreibung)"),
+            "parameters": {
+                "type": "object",
+                "properties": props,
+                "required": required,
+            },
+        },
+    }
+
+
+class Agent:
+    def __init__(self, store: VectorStore, embedder: Embedder,
+                 conversation: Conversation, proxy: ProxyClient,
+                 cold_k: int = 5):
+        self.store = store
+        self.embedder = embedder
+        self.conversation = conversation
+        self.proxy = proxy
+        self.cold_k = cold_k
+        # Side-Channel-Events die im Turn entstehen (z.B. skill_create).
+        # Werden vom /chat-Endpoint in der Response mitgeschickt, damit
+        # Stefan in der App und Diagnostic eine sichtbare Bubble bekommt.
+        self._pending_events: list[dict] = []
+
+    def pop_events(self) -> list[dict]:
+        """Holt die Events des letzten chat()-Calls und leert die Liste."""
+        events = self._pending_events
+        self._pending_events = []
+        return events
+
+    # ── Hauptpfad: ein User-Turn → Tool-Loop → finaler Reply ──
+
+    MAX_TOOL_ITERATIONS = 8  # Schutz vor Endlos-Loops
+
+    def chat(self, user_message: str, source: str = "") -> str:
+        user_message = (user_message or "").strip()
+        if not user_message:
+            raise ValueError("Leere Nachricht")
+
+        # Events vom letzten Turn weglassen
+        self._pending_events = []
+
+        # 1. User-Turn an die Konversation
+        self.conversation.add("user", user_message, source=source)
+
+        # 2. Hot Memory (alle pinned Punkte)
+        hot = self.store.list_pinned()
+
+        # 3. Cold Memory (Top-K semantic)
+        try:
+            qvec = self.embedder.embed(user_message)
+            cold = self.store.search(qvec, k=self.cold_k, exclude_pinned=True)
+        except Exception as exc:
+            logger.warning("Cold-Search fehlgeschlagen: %s", exc)
+            cold = []
+
+        # 4. Aktive Skills holen + Tool-Liste bauen
+        all_skills = skills_mod.list_skills(active_only=False)
+        active_skills = [s for s in all_skills if s.get("active", True)]
+        tools = list(META_TOOLS) + [_skill_to_tool(s) for s in active_skills]
+
+        # 5. System-Prompt + Window-Messages
+        system_prompt = build_system_prompt(hot, cold, skills=all_skills)
+        messages = [ProxyMessage(role="system", content=system_prompt)]
+        for t in self.conversation.window():
+            messages.append(ProxyMessage(role=t.role, content=t.content))
+
+        logger.info("chat: pinned=%d cold=%d skills=%d/%d window=%d prompt_chars=%d",
+                    len(hot), len(cold), len(active_skills), len(all_skills),
+                    len(self.conversation.window()), len(system_prompt))
+
+        # 6. Tool-Use-Loop
+        final_reply = ""
+        for iteration in range(self.MAX_TOOL_ITERATIONS):
+            result = self.proxy.chat_full(messages, tools=tools)
+            if result.tool_calls:
+                # Assistant-Turn mit tool_calls in messages anhaengen (nicht in Conversation!)
+                messages.append(ProxyMessage(
+                    role="assistant",
+                    content=result.content or None,
+                    tool_calls=[{
+                        "id": tc["id"], "type": "function",
+                        "function": {"name": tc["name"], "arguments": json.dumps(tc["arguments"])},
+                    } for tc in result.tool_calls],
+                ))
+                # Tools ausfuehren + Ergebnis als role=tool zurueck
+                for tc in result.tool_calls:
+                    tool_result = self._dispatch_tool(tc["name"], tc["arguments"])
+                    messages.append(ProxyMessage(
+                        role="tool",
+                        tool_call_id=tc["id"],
+                        name=tc["name"],
+                        content=tool_result[:8000],
+                    ))
+                continue  # next iteration mit Tool-Results
+            # Kein Tool-Call mehr → final reply
+            final_reply = (result.content or "").strip()
+            break
+        else:
+            # Loop-Limit erreicht
+            final_reply = "[Tool-Loop-Limit erreicht — ARIA hat zu viele Tool-Calls gemacht ohne fertig zu werden]"
+            logger.warning("Tool-Loop hit MAX_TOOL_ITERATIONS=%d", self.MAX_TOOL_ITERATIONS)
+
+        if not final_reply:
+            raise RuntimeError("Leerer Reply vom Proxy")
+
+        # 7. Assistant-Turn (final reply) in die Conversation
+        self.conversation.add("assistant", final_reply)
+        return final_reply
+
+    # ── Tool-Dispatcher ───────────────────────────────────────
+
+    def _dispatch_tool(self, name: str, arguments: dict) -> str:
+        """Fuehrt einen Tool-Call aus und gibt ein kurzes Text-Resultat zurueck.
+        Niemals werfen — Fehler werden als Text-Resultat reportet damit Claude
+        weitermachen kann."""
+        try:
+            if name == "skill_create":
+                # ARIA-Skills sind immer Python — execution ist nicht mehr im Schema
+                manifest = skills_mod.create_skill(
+                    name=arguments["name"],
+                    description=arguments["description"],
+                    execution="local-venv",
+                    entry_code=arguments["entry_code"],
+                    readme=arguments.get("readme", ""),
+                    args=arguments.get("args", []),
+                    pip_packages=arguments.get("pip_packages", []),
+                    author="aria",
+                )
+                # Side-Channel-Event: Stefan soll sehen wenn ARIA was anlegt
+                self._pending_events.append({
+                    "type": "skill_created",
+                    "skill": {
+                        "name": manifest["name"],
+                        "description": manifest.get("description", ""),
+                        "execution": manifest.get("execution", ""),
+                        "active": manifest.get("active", True),
+                        "setup_error": manifest.get("setup_error"),
+                    },
+                })
+                return f"OK — Skill '{manifest['name']}' erstellt (active={manifest['active']})."
+            if name == "skill_list":
+                items = skills_mod.list_skills(active_only=False)
+                if not items:
+                    return "(keine Skills vorhanden)"
+                return "\n".join(
+                    f"- {s['name']} ({s['execution']}) {'aktiv' if s.get('active', True) else 'DEAKTIVIERT'}: {s.get('description', '')}"
+                    for s in items
+                )
+            if name.startswith("run_"):
+                skill_name = name[len("run_"):]
+                res = skills_mod.run_skill(skill_name, args=arguments)
+                snippet = (res.get("stdout") or "")[:2000] or "(kein stdout)"
+                err = (res.get("stderr") or "")[:500]
+                marker = "OK" if res["ok"] else f"FEHLER (exit={res['exit_code']})"
+                out = f"{marker} · {res['duration_sec']}s\nstdout:\n{snippet}"
+                if err:
+                    out += f"\nstderr:\n{err}"
+                return out
+            return f"Unbekanntes Tool: {name}"
+        except Exception as exc:
+            logger.exception("Tool '%s' fehlgeschlagen", name)
+            return f"FEHLER: {exc}"
+
+    # ── Memory-Destillat (laeuft im Hintergrund) ──────────────
+
+    def distill_old_turns(self) -> dict:
+        """Nimmt die N aeltesten Turns und destilliert sie zu fact-Memories.
+
+        Pattern: separater Claude-Call, lieferte 3-7 JSON-Facts, die als
+        type=fact, source=distilled gespeichert werden. Erfolgreiches
+        Schreiben → Turns aus dem Window entfernen.
+        """
+        if not self.conversation.needs_distill():
+            return {"distilled": 0, "reason": "kein Bedarf"}
+
+        old_turns = self.conversation.take_oldest_for_distill()
+        if not old_turns:
+            return {"distilled": 0, "reason": "keine alten Turns"}
+
+        # Konversation als Klartext bauen
+        transcript = "\n".join(
+            f"[{t.role.upper()}] {t.content}" for t in old_turns
+        )[:30000]  # Cap auf 30k Zeichen damit der Prompt nicht explodiert
+
+        system = (
+            "Du extrahierst aus einer Konversation zwischen Stefan und ARIA die "
+            "wichtigsten dauerhaft relevanten Fakten — keine Smalltalk-Details, "
+            "keine flüchtigen Zustände. Antworte AUSSCHLIESSLICH mit gültigem JSON "
+            "im Format: {\"facts\": [{\"title\": \"kurz, max 80 Zeichen\", "
+            "\"content\": \"1-3 Sätze, konkret und nützlich\"}]}. "
+            "Mindestens 0, höchstens 7 Facts. Wenn nichts wichtig genug ist: leeres Array."
+        )
+        user = (
+            "Hier ist der Konversations-Abschnitt:\n\n"
+            f"{transcript}\n\n"
+            "Extrahiere die wichtigsten Fakten als JSON."
+        )
+
+        try:
+            raw = self.proxy.chat([
+                ProxyMessage(role="system", content=system),
+                ProxyMessage(role="user", content=user),
+            ])
+        except Exception as exc:
+            logger.warning("Destillat-Call fehlgeschlagen: %s — Turns bleiben", exc)
+            return {"distilled": 0, "error": str(exc)}
+
+        facts = self._parse_facts(raw)
+        if facts is None:
+            logger.warning("Destillat lieferte unparsbares JSON: %r", raw[:200])
+            return {"distilled": 0, "error": "JSON parse failed", "raw": raw[:200]}
+
+        # Facts in die DB schreiben
+        created = 0
+        for f in facts:
+            content = (f.get("content") or "").strip()
+            if not content:
+                continue
+            title = (f.get("title") or "").strip()[:120] or "Fakt"
+            point = MemoryPoint(
+                id="",
+                type="fact",
+                title=title,
+                content=content,
+                pinned=False,
+                category="konversation",
+                source="distilled",
+                tags=[],
+            )
+            try:
+                vec = self.embedder.embed(content)
+                self.store.upsert(point, vec)
+                created += 1
+            except Exception as exc:
+                logger.warning("Fakt schreiben fehlgeschlagen: %s", exc)
+
+        # Erst nach erfolgreichem Schreiben aus dem Window entfernen
+        last_ts = old_turns[-1].ts
+        self.conversation.commit_distill(last_ts)
+        logger.info("Destillat: %d Facts geschrieben, %d Turns aus Window entfernt",
+                    created, len(old_turns))
+        return {"distilled": created, "removed_turns": len(old_turns)}
+
+    @staticmethod
+    def _parse_facts(raw: str) -> Optional[list]:
+        if not raw:
+            return None
+        # JSON robust extrahieren — Claude kann Code-Fences setzen
+        cleaned = raw.strip()
+        if cleaned.startswith("```"):
+            # ```json oder ``` rauswerfen
+            cleaned = cleaned.split("\n", 1)[1] if "\n" in cleaned else cleaned[3:]
+            if cleaned.endswith("```"):
+                cleaned = cleaned[: -3]
+            cleaned = cleaned.strip()
+        # Erstes { bis letztes }
+        start = cleaned.find("{")
+        end = cleaned.rfind("}")
+        if start == -1 or end == -1 or end < start:
+            return None
+        try:
+            obj = json.loads(cleaned[start: end + 1])
+        except Exception:
+            return None
+        facts = obj.get("facts") if isinstance(obj, dict) else None
+        if not isinstance(facts, list):
+            return None
+        return facts

aria-brain/conversation.py

@@ -0,0 +1,130 @@
+"""
+Conversation-State — ein einziger Rolling-Window-State fuer ARIAs
+laufendes Gespraech mit Stefan.
+
+Stefan-Entscheidung: KEINE Sessions, KEIN Multi-Thread. EIN Strang,
+intern rollend. Was rausfaellt, wird ggf. destilliert und landet
+als type=fact Memory in der Vector-DB.
+
+Persistenz: append-only JSONL unter /data/conversation.jsonl.
+Bei Restart wird die letzte N gelesen (komplett vermeidet Memory-
+Overhead bei sehr langen Verlaeufen).
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from dataclasses import dataclass, field, asdict
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import List, Optional
+
+logger = logging.getLogger(__name__)
+
+CONVERSATION_FILE = Path(os.environ.get("CONVERSATION_FILE", "/data/conversation.jsonl"))
+
+
+@dataclass
+class Turn:
+    role: str         # "user" | "assistant"
+    content: str
+    ts: str = field(default_factory=lambda: datetime.now(timezone.utc).isoformat())
+    source: str = ""  # "app" / "diagnostic" / "stt" — optional
+
+
+class Conversation:
+    """In-Memory Rolling Window, mit JSONL-Persistenz."""
+
+    def __init__(self, max_window: int = 50, distill_threshold: int = 60,
+                 distill_count: int = 30):
+        self.max_window = max_window
+        self.distill_threshold = distill_threshold
+        self.distill_count = distill_count
+        self.turns: List[Turn] = []
+        self._load()
+
+    def _load(self):
+        if not CONVERSATION_FILE.exists():
+            return
+        try:
+            lines = CONVERSATION_FILE.read_text(encoding="utf-8").splitlines()
+        except Exception as exc:
+            logger.warning("Konversation laden fehlgeschlagen: %s", exc)
+            return
+        loaded: List[Turn] = []
+        for line in lines:
+            line = line.strip()
+            if not line:
+                continue
+            try:
+                obj = json.loads(line)
+            except Exception:
+                continue
+            if obj.get("op") == "distill":
+                # Marker: bis hierhin wurde alles destilliert
+                drop_until_ts = obj.get("ts", "")
+                if drop_until_ts:
+                    loaded = [t for t in loaded if t.ts > drop_until_ts]
+                continue
+            role = obj.get("role")
+            content = obj.get("content")
+            if role in ("user", "assistant") and isinstance(content, str):
+                loaded.append(Turn(role=role, content=content,
+                                   ts=obj.get("ts", ""),
+                                   source=obj.get("source", "")))
+        self.turns = loaded
+        logger.info("Konversation geladen: %d Turns aus %s", len(self.turns), CONVERSATION_FILE)
+
+    def _append_to_file(self, record: dict):
+        try:
+            CONVERSATION_FILE.parent.mkdir(parents=True, exist_ok=True)
+            with CONVERSATION_FILE.open("a", encoding="utf-8") as f:
+                f.write(json.dumps(record, ensure_ascii=False) + "\n")
+        except Exception as exc:
+            logger.warning("Konversation persist fehlgeschlagen: %s", exc)
+
+    def add(self, role: str, content: str, source: str = "") -> Turn:
+        t = Turn(role=role, content=content, source=source)
+        self.turns.append(t)
+        self._append_to_file({
+            "ts": t.ts, "role": t.role, "content": t.content, "source": t.source,
+        })
+        return t
+
+    def window(self) -> List[Turn]:
+        """Die letzten max_window Turns — gehen in den LLM-Prompt."""
+        return self.turns[-self.max_window:]
+
+    def needs_distill(self) -> bool:
+        return len(self.turns) > self.distill_threshold
+
+    def take_oldest_for_distill(self) -> List[Turn]:
+        """Gibt die N aeltesten Turns zurueck — fuer den Destillat-Call.
+        Entfernt sie NICHT — das macht commit_distill nach erfolgreichem Call."""
+        return self.turns[: self.distill_count]
+
+    def commit_distill(self, last_distilled_ts: str):
+        """Schreibt einen Distill-Marker, entfernt aus dem In-Memory-Window."""
+        self._append_to_file({"op": "distill", "ts": last_distilled_ts})
+        self.turns = [t for t in self.turns if t.ts > last_distilled_ts]
+        logger.info("Distill commit bei ts=%s — Window jetzt %d Turns", last_distilled_ts, len(self.turns))
+
+    def reset(self):
+        """Hardes Reset — verwende vorsichtig (Diagnostic-Button)."""
+        try:
+            if CONVERSATION_FILE.exists():
+                CONVERSATION_FILE.unlink()
+        except Exception:
+            pass
+        self.turns = []
+        logger.warning("Konversation komplett zurueckgesetzt")
+
+    def stats(self) -> dict:
+        return {
+            "turns": len(self.turns),
+            "max_window": self.max_window,
+            "distill_threshold": self.distill_threshold,
+            "needs_distill": self.needs_distill(),
+        }

aria-brain/main.py

@@ -0,0 +1,518 @@
+"""
+ARIA Brain — FastAPI-Einstieg.
+
+Phase B Punkt 1: nur Skeleton.
+- /health                        → Liveness
+- /memory/list                   → alle Punkte (gefiltert)
+- /memory/pinned                 → Hot Memory
+- /memory/search?q=...&k=5       → semantische Suche
+- /memory/save                   → neuen Punkt anlegen
+- /memory/update/{id}            → Punkt aendern (re-embed wenn content geaendert)
+- /memory/delete/{id}            → Punkt loeschen
+- /memory/stats                  → Anzahl Punkte pro Type
+
+/chat (Conversation-Loop) und /skills/* kommen in spaeteren Phasen.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import List, Optional
+
+from fastapi import FastAPI, HTTPException, BackgroundTasks, Request
+from fastapi.responses import Response
+from pydantic import BaseModel, Field
+
+from memory import Embedder, VectorStore, MemoryPoint
+from conversation import Conversation
+from proxy_client import ProxyClient
+from agent import Agent
+import skills as skills_mod
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(name)s: %(message)s")
+logger = logging.getLogger("aria-brain")
+
+QDRANT_HOST = os.environ.get("QDRANT_HOST", "aria-qdrant")
+QDRANT_PORT = int(os.environ.get("QDRANT_PORT", "6333"))
+
+app = FastAPI(title="ARIA Brain", version="0.1.0")
+
+_embedder: Optional[Embedder] = None
+_store: Optional[VectorStore] = None
+_conversation: Optional[Conversation] = None
+_proxy: Optional[ProxyClient] = None
+_agent: Optional[Agent] = None
+
+
+def embedder() -> Embedder:
+    global _embedder
+    if _embedder is None:
+        _embedder = Embedder()
+    return _embedder
+
+
+def store() -> VectorStore:
+    global _store
+    if _store is None:
+        _store = VectorStore(host=QDRANT_HOST, port=QDRANT_PORT)
+    return _store
+
+
+def conversation() -> Conversation:
+    global _conversation
+    if _conversation is None:
+        _conversation = Conversation()
+    return _conversation
+
+
+def proxy_client() -> ProxyClient:
+    global _proxy
+    if _proxy is None:
+        _proxy = ProxyClient()
+    return _proxy
+
+
+def agent() -> Agent:
+    global _agent
+    if _agent is None:
+        _agent = Agent(store(), embedder(), conversation(), proxy_client())
+    return _agent
+
+
+# ─── Pydantic-Schemas ─────────────────────────────────────────────────
+
+class MemoryIn(BaseModel):
+    type: str = Field(..., description="identity|rule|preference|tool|skill|fact|conversation|reminder")
+    title: str
+    content: str
+    pinned: bool = False
+    category: str = ""
+    source: str = "manual"
+    tags: List[str] = Field(default_factory=list)
+    conversation_id: Optional[str] = None
+
+
+class MemoryUpdate(BaseModel):
+    title: Optional[str] = None
+    content: Optional[str] = None
+    pinned: Optional[bool] = None
+    category: Optional[str] = None
+    tags: Optional[List[str]] = None
+
+
+class MemoryOut(BaseModel):
+    id: str
+    type: str
+    title: str
+    content: str
+    pinned: bool
+    category: str
+    source: str
+    tags: List[str]
+    created_at: str
+    updated_at: str
+    conversation_id: Optional[str] = None
+    score: Optional[float] = None
+
+    @classmethod
+    def from_point(cls, p: MemoryPoint) -> "MemoryOut":
+        return cls(**p.__dict__)
+
+
+# ─── Health ───────────────────────────────────────────────────────────
+
+@app.get("/health")
+def health():
+    try:
+        n = store().count()
+        return {"status": "ok", "memory_count": n, "qdrant": f"{QDRANT_HOST}:{QDRANT_PORT}"}
+    except Exception as exc:
+        return {"status": "degraded", "error": str(exc), "qdrant": f"{QDRANT_HOST}:{QDRANT_PORT}"}
+
+
+# ─── Memory-Endpoints ─────────────────────────────────────────────────
+
+@app.get("/memory/stats")
+def memory_stats():
+    s = store()
+    points = s.list_all()
+    by_type = {}
+    pinned = 0
+    for p in points:
+        by_type[p.type] = by_type.get(p.type, 0) + 1
+        if p.pinned:
+            pinned += 1
+    return {"total": len(points), "pinned": pinned, "by_type": by_type}
+
+
+@app.get("/memory/list", response_model=List[MemoryOut])
+def memory_list(type: Optional[str] = None, limit: int = 200):
+    s = store()
+    points = s.list_by_type(type, limit=limit) if type else s.list_all(limit=limit)
+    return [MemoryOut.from_point(p) for p in points]
+
+
+@app.get("/memory/pinned", response_model=List[MemoryOut])
+def memory_pinned():
+    return [MemoryOut.from_point(p) for p in store().list_pinned()]
+
+
+@app.get("/memory/search", response_model=List[MemoryOut])
+def memory_search(q: str, k: int = 5, type: Optional[str] = None, include_pinned: bool = False):
+    vec = embedder().embed(q)
+    points = store().search(vec, k=k, type_filter=type, exclude_pinned=not include_pinned)
+    return [MemoryOut.from_point(p) for p in points]
+
+
+@app.post("/memory/save", response_model=MemoryOut)
+def memory_save(body: MemoryIn):
+    s = store()
+    vec = embedder().embed(body.content)
+    point = MemoryPoint(
+        id="",
+        type=body.type,
+        title=body.title,
+        content=body.content,
+        pinned=body.pinned,
+        category=body.category,
+        source=body.source,
+        tags=body.tags,
+        conversation_id=body.conversation_id,
+    )
+    pid = s.upsert(point, vec)
+    saved = s.get(pid)
+    return MemoryOut.from_point(saved)
+
+
+@app.patch("/memory/update/{point_id}", response_model=MemoryOut)
+def memory_update(point_id: str, body: MemoryUpdate):
+    s = store()
+    existing = s.get(point_id)
+    if not existing:
+        raise HTTPException(404, f"Memory {point_id} nicht gefunden")
+
+    content_changed = body.content is not None and body.content != existing.content
+    if body.title is not None:
+        existing.title = body.title
+    if body.content is not None:
+        existing.content = body.content
+    if body.pinned is not None:
+        existing.pinned = body.pinned
+    if body.category is not None:
+        existing.category = body.category
+    if body.tags is not None:
+        existing.tags = body.tags
+
+    vec = embedder().embed(existing.content) if content_changed else None
+    if vec is None:
+        # Vektor unveraendert lassen — nur Payload neu schreiben
+        from qdrant_client.http import models as qm
+        from memory.vector_store import COLLECTION
+        s.client.set_payload(
+            collection_name=COLLECTION,
+            payload=existing.to_payload() | {"updated_at": __import__("datetime").datetime.now(__import__("datetime").timezone.utc).isoformat()},
+            points=[point_id],
+        )
+        saved = s.get(point_id)
+    else:
+        s.upsert(existing, vec)
+        saved = s.get(point_id)
+    return MemoryOut.from_point(saved)
+
+
+@app.delete("/memory/delete/{point_id}")
+def memory_delete(point_id: str):
+    s = store()
+    if not s.get(point_id):
+        raise HTTPException(404, f"Memory {point_id} nicht gefunden")
+    s.delete(point_id)
+    return {"deleted": point_id}
+
+
+# ─── Migration aus brain-import/ ──────────────────────────────────────
+
+IMPORT_DIR = os.environ.get("IMPORT_DIR", "/import")
+
+
+@app.post("/memory/migrate")
+def memory_migrate():
+    """Liest /import/*.md und schreibt atomare Memory-Punkte in die DB.
+    Idempotent: bei Re-Run werden Punkte mit gleicher migration_key ersetzt."""
+    from pathlib import Path
+    from migration import run_migration
+    s = store()
+    e = embedder()
+    result = run_migration(Path(IMPORT_DIR), s, e)
+    return result
+
+
+@app.get("/memory/import-files")
+def memory_import_files():
+    """Listet was unter /import/ liegt — fuer die Diagnostic-UI."""
+    from pathlib import Path
+    d = Path(IMPORT_DIR)
+    if not d.exists():
+        return {"import_dir": str(d), "exists": False, "files": []}
+    out = []
+    for p in sorted(d.iterdir()):
+        if p.is_file():
+            try:
+                out.append({"name": p.name, "size": p.stat().st_size})
+            except Exception:
+                pass
+    return {"import_dir": str(d), "exists": True, "files": out}
+
+
+# ─── Bootstrap-Snapshot ───────────────────────────────────────────────
+# "Bootstrap" = alle pinned Memories. Export/Import zum schnellen
+# Wiederherstellen einer schlanken ARIA nach Wipe.
+
+@app.get("/memory/export-bootstrap")
+def memory_export_bootstrap():
+    """Gibt alle pinned Memories als JSON zurueck — fuer Browser-Download."""
+    s = store()
+    pinned = s.list_pinned()
+    return {
+        "version": 1,
+        "exported_at": __import__("datetime").datetime.now(
+            __import__("datetime").timezone.utc
+        ).isoformat(),
+        "count": len(pinned),
+        "memories": [
+            {
+                "type": p.type,
+                "title": p.title,
+                "content": p.content,
+                "pinned": True,
+                "category": p.category,
+                "source": p.source,
+                "tags": p.tags,
+            }
+            for p in pinned
+        ],
+    }
+
+
+class BootstrapBundle(BaseModel):
+    version: int = 1
+    memories: List[dict]
+
+
+@app.post("/memory/import-bootstrap")
+def memory_import_bootstrap(body: BootstrapBundle):
+    """Loescht alle pinned Memories und importiert die im Bundle.
+    Cold Memory (unpinned) bleibt unangetastet.
+
+    Wenn keine Memories im Bundle: nur loeschen ist NICHT erlaubt — der
+    Caller soll erst exportieren und dann importieren.
+    """
+    if not body.memories:
+        raise HTTPException(400, "Bundle hat keine memories — Abbruch zur Sicherheit")
+
+    s = store()
+    e = embedder()
+
+    # Alle aktuell pinned Punkte loeschen
+    from qdrant_client.http import models as qm
+    from memory.vector_store import COLLECTION
+    s.client.delete(
+        collection_name=COLLECTION,
+        points_selector=qm.FilterSelector(filter=qm.Filter(must=[
+            qm.FieldCondition(key="pinned", match=qm.MatchValue(value=True))
+        ])),
+    )
+
+    # Neue Punkte einspeisen
+    created = 0
+    for m in body.memories:
+        content = (m.get("content") or "").strip()
+        if not content:
+            continue
+        point = MemoryPoint(
+            id="",
+            type=m.get("type", "fact"),
+            title=m.get("title", "(ohne Titel)"),
+            content=content,
+            pinned=True,
+            category=m.get("category", ""),
+            source=m.get("source", "bootstrap-import"),
+            tags=list(m.get("tags", [])),
+        )
+        vec = e.embed(content)
+        s.upsert(point, vec)
+        created += 1
+
+    return {"created": created, "deleted_previous_pinned": True}
+
+
+# ─── Conversation-Loop ──────────────────────────────────────────────
+
+class ChatIn(BaseModel):
+    message: str
+    source: str = ""  # "app" / "diagnostic" / "stt" — optional
+
+
+class ChatOut(BaseModel):
+    reply: str
+    turns: int
+    distilling: bool
+    events: list = Field(default_factory=list)
+
+
+@app.post("/chat", response_model=ChatOut)
+def chat(body: ChatIn, background: BackgroundTasks):
+    """Hauptpfad. Antwort kommt synchron. Memory-Destillat laeuft
+    im Hintergrund nachdem die Response rausging."""
+    a = agent()
+    try:
+        reply = a.chat(body.message, source=body.source)
+    except ValueError as exc:
+        raise HTTPException(400, str(exc))
+    except RuntimeError as exc:
+        logger.error("chat fehlgeschlagen: %s", exc)
+        raise HTTPException(502, str(exc))
+
+    needs_distill = a.conversation.needs_distill()
+    if needs_distill:
+        background.add_task(a.distill_old_turns)
+    return ChatOut(
+        reply=reply,
+        turns=len(a.conversation.turns),
+        distilling=needs_distill,
+        events=a.pop_events(),
+    )
+
+
+@app.get("/conversation/stats")
+def conversation_stats():
+    return conversation().stats()
+
+
+@app.post("/conversation/reset")
+def conversation_reset():
+    """Hardes Reset — der Rolling-Window-Verlauf wird komplett geleert.
+    Destillierte facts bleiben in der DB."""
+    conversation().reset()
+    return {"ok": True, "turns": 0}
+
+
+@app.post("/conversation/distill")
+def conversation_distill_now():
+    """Manueller Trigger fuer Destillat — fuer Tests oder vor einem
+    bewussten Reset."""
+    return agent().distill_old_turns()
+
+
+# ─── Skills ─────────────────────────────────────────────────────────
+
+class SkillCreate(BaseModel):
+    name: str
+    description: str
+    execution: str  # local-venv | local-bin | bash
+    entry_code: str
+    readme: str = ""
+    args: list = Field(default_factory=list)
+    requires: dict = Field(default_factory=dict)
+    pip_packages: list = Field(default_factory=list)
+    author: str = "stefan"
+
+
+class SkillRun(BaseModel):
+    name: str
+    args: dict = Field(default_factory=dict)
+    timeout_sec: int = 300
+
+
+class SkillPatch(BaseModel):
+    description: str | None = None
+    active: bool | None = None
+    args: list | None = None
+
+
+@app.get("/skills/list")
+def skills_list(active_only: bool = False):
+    return {"skills": skills_mod.list_skills(active_only=active_only)}
+
+
+@app.get("/skills/{name}")
+def skills_get(name: str):
+    m = skills_mod.read_manifest(name)
+    if m is None:
+        raise HTTPException(404, f"Skill '{name}' nicht gefunden")
+    readme = skills_mod.read_readme(name)
+    return {"manifest": m, "readme": readme}
+
+
+@app.post("/skills/create")
+def skills_create(body: SkillCreate):
+    try:
+        return skills_mod.create_skill(
+            name=body.name,
+            description=body.description,
+            execution=body.execution,
+            entry_code=body.entry_code,
+            readme=body.readme,
+            args=body.args,
+            requires=body.requires,
+            pip_packages=body.pip_packages,
+            author=body.author,
+        )
+    except ValueError as exc:
+        raise HTTPException(400, str(exc))
+
+
+@app.post("/skills/run")
+def skills_run(body: SkillRun):
+    try:
+        return skills_mod.run_skill(body.name, args=body.args, timeout_sec=body.timeout_sec)
+    except ValueError as exc:
+        raise HTTPException(400, str(exc))
+
+
+@app.patch("/skills/{name}")
+def skills_patch(name: str, body: SkillPatch):
+    patch = {k: v for k, v in body.model_dump().items() if v is not None}
+    try:
+        return skills_mod.update_skill(name, patch)
+    except ValueError as exc:
+        raise HTTPException(404, str(exc))
+
+
+@app.delete("/skills/{name}")
+def skills_delete(name: str):
+    try:
+        skills_mod.delete_skill(name)
+    except ValueError as exc:
+        raise HTTPException(404, str(exc))
+    return {"deleted": name}
+
+
+@app.get("/skills/{name}/logs")
+def skills_logs(name: str, limit: int = 50):
+    return {"logs": skills_mod.list_logs(name, limit=limit)}
+
+
+@app.get("/skills/{name}/export")
+def skills_export(name: str):
+    try:
+        data = skills_mod.export_skill(name)
+    except ValueError as exc:
+        raise HTTPException(404, str(exc))
+    return Response(
+        content=data,
+        media_type="application/gzip",
+        headers={"Content-Disposition": f'attachment; filename="skill-{name}.tar.gz"'},
+    )
+
+
+@app.post("/skills/import")
+async def skills_import(request: Request, overwrite: bool = False):
+    data = await request.body()
+    if not data:
+        raise HTTPException(400, "Leerer Body")
+    try:
+        manifest = skills_mod.import_skill(data, overwrite=overwrite)
+    except ValueError as exc:
+        raise HTTPException(400, str(exc))
+    return {"imported": manifest}

aria-brain/memory/__init__.py

@@ -0,0 +1,4 @@
+from .embedder import Embedder
+from .vector_store import VectorStore, MemoryPoint, MemoryType
+
+__all__ = ["Embedder", "VectorStore", "MemoryPoint", "MemoryType"]

aria-brain/memory/embedder.py

@@ -0,0 +1,42 @@
+"""
+Lokaler Embedder fuer Memory-Texte.
+
+Nutzt sentence-transformers (paraphrase-multilingual-MiniLM-L12-v2):
+- Deutsch + Englisch
+- 384-dimensionale Vektoren
+- Laeuft auf CPU, ~30ms pro kurzer Text
+- Modell wird beim ersten Aufruf in /data/_models gecached
+"""
+
+from __future__ import annotations
+
+import logging
+from typing import List
+
+logger = logging.getLogger(__name__)
+
+MODEL_NAME = "paraphrase-multilingual-MiniLM-L12-v2"
+VECTOR_DIM = 384
+
+
+class Embedder:
+    def __init__(self, model_name: str = MODEL_NAME):
+        self.model_name = model_name
+        self._model = None
+
+    def _load(self):
+        if self._model is None:
+            logger.info("Lade Embedding-Modell %s ...", self.model_name)
+            from sentence_transformers import SentenceTransformer
+            self._model = SentenceTransformer(self.model_name)
+            logger.info("Embedding-Modell geladen.")
+
+    def embed(self, text: str) -> List[float]:
+        self._load()
+        vec = self._model.encode(text, convert_to_numpy=True, normalize_embeddings=True)
+        return vec.tolist()
+
+    def embed_batch(self, texts: List[str]) -> List[List[float]]:
+        self._load()
+        vecs = self._model.encode(texts, convert_to_numpy=True, normalize_embeddings=True)
+        return vecs.tolist()

aria-brain/memory/vector_store.py

@@ -0,0 +1,209 @@
+"""
+Vector-Store-Wrapper um Qdrant.
+
+Eine Collection "aria_memory" haelt ALLE Memory-Punkte.
+Trennung nach Type/Pinned-Status via Payload-Filter.
+
+Punkt-Schema (Payload):
+    type      — identity | rule | preference | tool | skill | fact | conversation | reminder
+    category  — frei, fuer UI-Gruppierung
+    title     — kurze Ueberschrift
+    content   — eigentlicher Text (wird embedded)
+    pinned    — bool, True = Hot Memory (immer in Prompt)
+    source    — import | conversation | manual
+    tags      — Liste von Strings
+    created_at, updated_at — ISO-Strings
+    conversation_id — optional, nur fuer type=conversation
+"""
+
+from __future__ import annotations
+
+import logging
+import uuid
+from dataclasses import dataclass, field
+from datetime import datetime, timezone
+from enum import Enum
+from typing import List, Optional
+
+from qdrant_client import QdrantClient
+from qdrant_client.http import models as qm
+
+from .embedder import VECTOR_DIM
+
+logger = logging.getLogger(__name__)
+
+COLLECTION = "aria_memory"
+
+
+class MemoryType(str, Enum):
+    IDENTITY = "identity"
+    RULE = "rule"
+    PREFERENCE = "preference"
+    TOOL = "tool"
+    SKILL = "skill"
+    FACT = "fact"
+    CONVERSATION = "conversation"
+    REMINDER = "reminder"
+
+
+@dataclass
+class MemoryPoint:
+    id: str
+    type: str
+    title: str
+    content: str
+    pinned: bool = False
+    category: str = ""
+    source: str = "manual"
+    tags: List[str] = field(default_factory=list)
+    created_at: str = ""
+    updated_at: str = ""
+    conversation_id: Optional[str] = None
+    score: Optional[float] = None  # nur bei Search gesetzt
+
+    def to_payload(self) -> dict:
+        p = {
+            "type": self.type,
+            "title": self.title,
+            "content": self.content,
+            "pinned": self.pinned,
+            "category": self.category,
+            "source": self.source,
+            "tags": self.tags,
+            "created_at": self.created_at,
+            "updated_at": self.updated_at,
+        }
+        if self.conversation_id:
+            p["conversation_id"] = self.conversation_id
+        return p
+
+    @classmethod
+    def from_qdrant(cls, point) -> "MemoryPoint":
+        payload = point.payload or {}
+        return cls(
+            id=str(point.id),
+            type=payload.get("type", "fact"),
+            title=payload.get("title", ""),
+            content=payload.get("content", ""),
+            pinned=payload.get("pinned", False),
+            category=payload.get("category", ""),
+            source=payload.get("source", "manual"),
+            tags=payload.get("tags", []),
+            created_at=payload.get("created_at", ""),
+            updated_at=payload.get("updated_at", ""),
+            conversation_id=payload.get("conversation_id"),
+            score=getattr(point, "score", None),
+        )
+
+
+def _now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+class VectorStore:
+    def __init__(self, host: str, port: int = 6333):
+        self.client = QdrantClient(host=host, port=port)
+        self._ensure_collection()
+
+    def _ensure_collection(self):
+        existing = [c.name for c in self.client.get_collections().collections]
+        if COLLECTION not in existing:
+            logger.info("Erstelle Collection %s ...", COLLECTION)
+            self.client.create_collection(
+                collection_name=COLLECTION,
+                vectors_config=qm.VectorParams(size=VECTOR_DIM, distance=qm.Distance.COSINE),
+            )
+            # Indexe fuer typische Filter-Felder
+            for field_name in ("type", "pinned", "category", "source", "migration_key"):
+                self.client.create_payload_index(
+                    collection_name=COLLECTION,
+                    field_name=field_name,
+                    field_schema=qm.PayloadSchemaType.KEYWORD if field_name != "pinned"
+                    else qm.PayloadSchemaType.BOOL,
+                )
+
+    # ─── Schreib-Operationen ─────────────────────────────────────────
+
+    def upsert(self, point: MemoryPoint, vector: List[float]) -> str:
+        if not point.id:
+            point.id = str(uuid.uuid4())
+        if not point.created_at:
+            point.created_at = _now()
+        point.updated_at = _now()
+
+        self.client.upsert(
+            collection_name=COLLECTION,
+            points=[qm.PointStruct(id=point.id, vector=vector, payload=point.to_payload())],
+        )
+        return point.id
+
+    def delete(self, point_id: str):
+        self.client.delete(
+            collection_name=COLLECTION,
+            points_selector=qm.PointIdsList(points=[point_id]),
+        )
+
+    # ─── Lese-Operationen ────────────────────────────────────────────
+
+    def get(self, point_id: str) -> Optional[MemoryPoint]:
+        result = self.client.retrieve(collection_name=COLLECTION, ids=[point_id], with_payload=True)
+        if not result:
+            return None
+        return MemoryPoint.from_qdrant(result[0])
+
+    def list_pinned(self) -> List[MemoryPoint]:
+        """Alle pinned Punkte — Hot Memory."""
+        return self._scroll(filter=qm.Filter(must=[
+            qm.FieldCondition(key="pinned", match=qm.MatchValue(value=True))
+        ]))
+
+    def list_by_type(self, type_: str, limit: int = 100) -> List[MemoryPoint]:
+        return self._scroll(
+            filter=qm.Filter(must=[
+                qm.FieldCondition(key="type", match=qm.MatchValue(value=type_))
+            ]),
+            limit=limit,
+        )
+
+    def list_all(self, limit: int = 1000) -> List[MemoryPoint]:
+        return self._scroll(filter=None, limit=limit)
+
+    def _scroll(self, filter, limit: int = 1000) -> List[MemoryPoint]:
+        points, _ = self.client.scroll(
+            collection_name=COLLECTION,
+            scroll_filter=filter,
+            limit=limit,
+            with_payload=True,
+            with_vectors=False,
+        )
+        return [MemoryPoint.from_qdrant(p) for p in points]
+
+    def search(
+        self,
+        query_vector: List[float],
+        k: int = 5,
+        type_filter: Optional[str] = None,
+        exclude_pinned: bool = True,
+    ) -> List[MemoryPoint]:
+        """Semantische Search. Standard: pinned-Punkte ausgeschlossen
+        (die kommen separat via list_pinned in den Prompt)."""
+        must = []
+        must_not = []
+        if type_filter:
+            must.append(qm.FieldCondition(key="type", match=qm.MatchValue(value=type_filter)))
+        if exclude_pinned:
+            must_not.append(qm.FieldCondition(key="pinned", match=qm.MatchValue(value=True)))
+
+        flt = qm.Filter(must=must or None, must_not=must_not or None)
+
+        results = self.client.search(
+            collection_name=COLLECTION,
+            query_vector=query_vector,
+            query_filter=flt if (must or must_not) else None,
+            limit=k,
+            with_payload=True,
+        )
+        return [MemoryPoint.from_qdrant(p) for p in results]
+
+    def count(self) -> int:
+        return self.client.count(collection_name=COLLECTION, exact=True).count

aria-brain/migration.py

@@ -0,0 +1,399 @@
+"""
+Migration aus aria-data/brain-import/ → Vector-DB.
+
+Parst die mitgelieferten Markdown-Dateien (AGENT.md, USER.md, TOOLING.md)
+und zerlegt sie in atomare Memory-Punkte. Jeder Punkt bekommt:
+
+  source       = "import"
+  migration_key = stabiler Identifier (z.B. "agent.md/rule-1") fuer Idempotenz
+  pinned       = True
+
+Beim Re-Run werden vorhandene Punkte mit gleicher migration_key entfernt
+und neu geschrieben.
+
+Mapping pro Datei:
+
+  AGENT.md
+    "Identitaet"                    → 1 Punkt type=identity
+    "Persoenlichkeit" (Intro)       → 1 Punkt type=identity
+    "Kern-Eigenschaften" (Liste)    → 1 Punkt pro Bullet  type=identity
+    "Tool-Freigaben"                → 1 Punkt type=tool
+    "Sicherheitsregeln" (Liste)     → 1 Punkt pro Bullet  type=rule
+    "Arbeitsprinzipien" (Liste)     → 1 Punkt pro Bullet  type=rule
+    "Dateien an Stefan zurueckgeben"→ 1 Punkt type=skill
+    "Stimme"                        → 1 Punkt type=tool
+
+  USER.md
+    "Allgemein" (Liste)             → 1 Punkt pro Bullet  type=preference
+    "Bestaetigung erforderlich"     → 1 Punkt type=preference
+    "Autonomes Arbeiten OK fuer"    → 1 Punkt type=preference
+    "Tools & Infrastruktur"         → 1 Punkt type=preference
+
+  TOOLING.md
+    gesamter Inhalt                 → 1 Punkt type=tool, title="Tooling-Stack"
+
+BOOTSTRAP.md ist eine Variante von AGENT.md — wird (vorerst) ignoriert
+damit keine doppelten Punkte landen.
+"""
+
+from __future__ import annotations
+
+import logging
+import re
+from dataclasses import dataclass
+from pathlib import Path
+from typing import List, Optional
+
+from memory import Embedder, VectorStore, MemoryPoint
+from memory.vector_store import COLLECTION
+from qdrant_client.http import models as qm
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class _Block:
+    title: str
+    content: str
+
+
+def _split_h2(md: str) -> List[_Block]:
+    """Zerlegt Markdown in H2-Bloecke. Inhalt vor dem ersten H2 wird verworfen."""
+    blocks: List[_Block] = []
+    current: Optional[_Block] = None
+    for line in md.splitlines():
+        m = re.match(r"^##\s+(.+?)\s*$", line)
+        if m and not line.startswith("### "):
+            if current:
+                blocks.append(current)
+            current = _Block(title=m.group(1).strip(), content="")
+            continue
+        if current is not None:
+            current.content += line + "\n"
+    if current:
+        blocks.append(current)
+    return blocks
+
+
+def _split_h3(content: str) -> List[_Block]:
+    """Zerlegt einen H2-Block in H3-Untersektionen + 'header'-Block davor."""
+    blocks: List[_Block] = []
+    header_lines: List[str] = []
+    current: Optional[_Block] = None
+    for line in content.splitlines():
+        m = re.match(r"^###\s+(.+?)\s*$", line)
+        if m:
+            if current is None and header_lines:
+                blocks.append(_Block(title="_intro", content="\n".join(header_lines).strip()))
+            if current:
+                blocks.append(current)
+            current = _Block(title=m.group(1).strip(), content="")
+            continue
+        if current is None:
+            header_lines.append(line)
+        else:
+            current.content += line + "\n"
+    if current:
+        blocks.append(current)
+    elif header_lines:
+        blocks.append(_Block(title="_intro", content="\n".join(header_lines).strip()))
+    return blocks
+
+
+def _extract_bullets(content: str) -> List[tuple[str, str]]:
+    """Findet "- **Title** — Body" oder "N. **Title** — Body" Bullets.
+
+    Returns: Liste von (title, full_bullet_text).
+    """
+    bullets: List[tuple[str, str]] = []
+    current_lines: List[str] = []
+    current_title: Optional[str] = None
+
+    def flush():
+        if current_title and current_lines:
+            bullets.append((current_title, "\n".join(current_lines).strip()))
+
+    for line in content.splitlines():
+        m = re.match(r"^\s*(?:[-*]|\d+\.)\s+\*\*([^*]+?)\*\*\s*[—\-:]?\s*(.*)$", line)
+        if m:
+            flush()
+            current_title = m.group(1).strip()
+            current_lines = [line]
+            continue
+        # Folge-Zeilen mit Einrueckung gehoeren zum aktuellen Bullet
+        if current_title and (line.startswith(" ") or line.startswith("\t") or not line.strip()):
+            current_lines.append(line)
+            continue
+        if current_title and not re.match(r"^\s*(?:[-*]|\d+\.)\s+", line):
+            current_lines.append(line)
+            continue
+        # Neuer Bullet ohne **Title** Format
+        if re.match(r"^\s*(?:[-*]|\d+\.)\s+", line):
+            flush()
+            text = re.sub(r"^\s*(?:[-*]|\d+\.)\s+", "", line).strip()
+            short_title = (text[:60] + "…") if len(text) > 60 else text
+            bullets.append((short_title, line.strip()))
+            current_title = None
+            current_lines = []
+    flush()
+    return bullets
+
+
+# ─── Pro Datei eine Parser-Funktion ──────────────────────────────────
+
+def _parse_agent_md(md: str, source_file: str) -> List[MemoryPoint]:
+    points: List[MemoryPoint] = []
+    h2_blocks = _split_h2(md)
+    for h2 in h2_blocks:
+        title = h2.title
+        content = h2.content.strip()
+        if not content:
+            continue
+
+        if title.lower() == "identitaet" or title.lower() == "identität":
+            points.append(_mk(
+                type_="identity", title="ARIA — Identitaet",
+                content=f"## {title}\n\n{content}",
+                category="persoenlichkeit",
+                migration_key=f"{source_file}/identity",
+            ))
+
+        elif title.lower() == "persoenlichkeit" or title.lower() == "persönlichkeit":
+            # Intro-Absatz + Kern-Eigenschaften-Liste trennen
+            sub = _split_h3(content)
+            for s in sub:
+                if s.title == "_intro" and s.content.strip():
+                    points.append(_mk(
+                        type_="identity", title="Persoenlichkeit — Grundsatz",
+                        content=s.content.strip(),
+                        category="persoenlichkeit",
+                        migration_key=f"{source_file}/personality-intro",
+                    ))
+                elif s.title.lower().startswith("kern"):
+                    for idx, (btitle, btext) in enumerate(_extract_bullets(s.content), 1):
+                        points.append(_mk(
+                            type_="identity", title=f"Eigenschaft: {btitle}",
+                            content=btext, category="persoenlichkeit",
+                            migration_key=f"{source_file}/personality-trait-{idx}",
+                        ))
+
+        elif "sicherheitsregel" in title.lower():
+            for idx, (btitle, btext) in enumerate(_extract_bullets(content), 1):
+                points.append(_mk(
+                    type_="rule", title=f"Sicherheit: {btitle}",
+                    content=btext, category="sicherheit",
+                    migration_key=f"{source_file}/security-{idx}",
+                ))
+
+        elif "arbeitsprinzipien" in title.lower() or "arbeitsprinzip" in title.lower():
+            for idx, (btitle, btext) in enumerate(_extract_bullets(content), 1):
+                points.append(_mk(
+                    type_="rule", title=f"Prinzip: {btitle}",
+                    content=btext, category="arbeitsweise",
+                    migration_key=f"{source_file}/work-principle-{idx}",
+                ))
+
+        elif "tool-freigaben" in title.lower() or "tool freigaben" in title.lower():
+            points.append(_mk(
+                type_="tool", title="Tool-Freigaben — Vollzugriff",
+                content=content, category="infrastruktur",
+                migration_key=f"{source_file}/tool-access",
+            ))
+
+        elif "dateien an stefan" in title.lower() or "dateien zurueckgeben" in title.lower() or "dateien zur" in title.lower():
+            points.append(_mk(
+                type_="skill", title="Dateien an User zurueckgeben",
+                content=content, category="ausgabe",
+                migration_key=f"{source_file}/file-return-skill",
+            ))
+
+        elif title.lower() == "stimme":
+            points.append(_mk(
+                type_="tool", title="Stimme (F5-TTS)",
+                content=content, category="infrastruktur",
+                migration_key=f"{source_file}/voice",
+            ))
+
+        # Permanente Freigaben (in BOOTSTRAP) — als rule
+        elif "freigaben" in title.lower():
+            points.append(_mk(
+                type_="rule", title=title,
+                content=content, category="freigaben",
+                migration_key=f"{source_file}/permissions",
+            ))
+
+        else:
+            # Unbekannter Block: als generischer fact ablegen, NICHT pinned
+            logger.info("Unbekannter H2-Block '%s' in %s — als fact (unpinned)", title, source_file)
+            points.append(_mk(
+                type_="fact", title=f"{source_file}: {title}",
+                content=content, pinned=False,
+                migration_key=f"{source_file}/section-{title.lower().replace(' ', '-')}",
+            ))
+    return points
+
+
+def _parse_user_md(md: str, source_file: str) -> List[MemoryPoint]:
+    points: List[MemoryPoint] = []
+    for h2 in _split_h2(md):
+        title = h2.title
+        content = h2.content.strip()
+        if not content:
+            continue
+        # Template-Platzhalter herausfiltern: Beispiel-Zeilen mit <Tag>
+        if "<Beispiel-Tool>" in content or "<Username>" in title:
+            continue
+        if title.lower() == "allgemein":
+            for idx, (btitle, btext) in enumerate(_extract_bullets(content), 1):
+                # Template-Platzhalter ueberspringen
+                if "<z.B." in btext or "<XYZ>" in btext:
+                    continue
+                points.append(_mk(
+                    type_="preference", title=f"User: {btitle}",
+                    content=btext, category="allgemein",
+                    migration_key=f"{source_file}/general-{idx}",
+                ))
+        else:
+            cat_key = re.sub(r"[^a-z0-9]+", "-", title.lower()).strip("-") or "allgemein"
+            points.append(_mk(
+                type_="preference", title=title,
+                content=content, category=cat_key,
+                migration_key=f"{source_file}/{cat_key}",
+            ))
+    return points
+
+
+def _parse_tooling_md(md: str, source_file: str) -> List[MemoryPoint]:
+    md = md.strip()
+    if not md:
+        return []
+    return [_mk(
+        type_="tool", title="Tooling-Stack (VM)",
+        content=md, category="infrastruktur",
+        migration_key=f"{source_file}/tooling-full",
+    )]
+
+
+# ─── Helper ─────────────────────────────────────────────────────────
+
+def _mk(
+    type_: str,
+    title: str,
+    content: str,
+    migration_key: str,
+    pinned: bool = True,
+    category: str = "",
+) -> MemoryPoint:
+    p = MemoryPoint(
+        id="",
+        type=type_,
+        title=title,
+        content=content.strip(),
+        pinned=pinned,
+        category=category,
+        source="import",
+        tags=[],
+    )
+    # migration_key wird ueber Payload-Index angesprochen — in to_payload manuell anhaengen
+    setattr(p, "_migration_key", migration_key)
+    return p
+
+
+# ─── Eintrittspunkt ─────────────────────────────────────────────────
+
+def run_migration(
+    import_dir: Path,
+    store: VectorStore,
+    embedder: Embedder,
+) -> dict:
+    """Liest alle .md-Dateien aus import_dir, parst sie, schreibt in DB.
+
+    Idempotent: vorhandene Punkte mit gleicher migration_key werden geloescht
+    und neu geschrieben.
+
+    Returns: {"created": int, "updated": int, "skipped": int, "files": [...]}
+    """
+    if not import_dir.exists():
+        return {"created": 0, "updated": 0, "skipped": 0, "files": [], "error": f"{import_dir} nicht gefunden"}
+
+    parsers = {
+        "AGENT.md": _parse_agent_md,
+        "BOOTSTRAP.md": _parse_agent_md,  # gleicher Parser, ggf. ueberlappende Eintraege
+        "USER.md": _parse_user_md,
+        "USER.md.example": _parse_user_md,
+        "TOOLING.md": _parse_tooling_md,
+        "TOOLING.md.example": _parse_tooling_md,
+    }
+
+    # USER.md hat Vorrang vor USER.md.example
+    file_priority = ["AGENT.md", "BOOTSTRAP.md", "USER.md", "USER.md.example",
+                     "TOOLING.md", "TOOLING.md.example"]
+    seen_kinds: set[str] = set()  # "USER" / "TOOLING" — nur einmal
+
+    points: List[MemoryPoint] = []
+    processed_files: List[str] = []
+
+    for fname in file_priority:
+        fp = import_dir / fname
+        if not fp.exists():
+            continue
+        kind = fname.split(".")[0]  # "AGENT", "BOOTSTRAP", "USER", "TOOLING"
+        # USER.md.example nur wenn USER.md fehlt
+        if kind in ("USER", "TOOLING") and kind in seen_kinds:
+            continue
+        seen_kinds.add(kind)
+        parser = parsers.get(fname)
+        if not parser:
+            continue
+        try:
+            md = fp.read_text(encoding="utf-8")
+            file_points = parser(md, fname)
+            points.extend(file_points)
+            processed_files.append(f"{fname} ({len(file_points)})")
+            logger.info("Migration: %s → %d Punkte", fname, len(file_points))
+        except Exception as exc:
+            logger.exception("Migration: %s fehlgeschlagen", fname)
+            processed_files.append(f"{fname} (FEHLER: {exc})")
+
+    if not points:
+        return {"created": 0, "updated": 0, "skipped": 0, "files": processed_files}
+
+    # Erst alte Migration-Punkte mit gleicher migration_key loeschen
+    migration_keys = [getattr(p, "_migration_key", None) for p in points]
+    migration_keys = [k for k in migration_keys if k]
+    if migration_keys:
+        store.client.delete(
+            collection_name=COLLECTION,
+            points_selector=qm.FilterSelector(filter=qm.Filter(must=[
+                qm.FieldCondition(key="migration_key", match=qm.MatchAny(any=migration_keys))
+            ])),
+        )
+        logger.info("Migration: %d alte Punkte mit gleicher migration_key entfernt", len(migration_keys))
+
+    # Embed in Batches
+    texts = [p.content for p in points]
+    vectors = embedder.embed_batch(texts)
+
+    created = 0
+    for p, vec in zip(points, vectors):
+        payload = p.to_payload()
+        mkey = getattr(p, "_migration_key", None)
+        if mkey:
+            payload["migration_key"] = mkey
+        from datetime import datetime, timezone
+        import uuid as _uuid
+        pid = str(_uuid.uuid4())
+        now = datetime.now(timezone.utc).isoformat()
+        payload["created_at"] = now
+        payload["updated_at"] = now
+        store.client.upsert(
+            collection_name=COLLECTION,
+            points=[qm.PointStruct(id=pid, vector=vec, payload=payload)],
+        )
+        created += 1
+
+    return {
+        "created": created,
+        "files": processed_files,
+        "import_dir": str(import_dir),
+    }

aria-brain/prompts.py

@@ -0,0 +1,131 @@
+"""
+System-Prompt-Bau aus Memory-Punkten.
+
+Strategie:
+  1. Alle pinned Punkte (Hot Memory) — gruppiert nach Type — in den
+     System-Prompt schreiben. IMMER drin.
+  2. Top-K semantisch aehnliche Punkte (Cold Memory) zur aktuellen
+     User-Nachricht — als "Moeglicherweise relevant" eingehaengt.
+  3. Aktive Skills als kompakte Liste (nur Name + Description) — damit
+     ARIA weiss was sie hat.
+
+Phase B Punkt 1: nur Hot-Memory-Bau, Skills + Cold-Search kommen
+mit dem Conversation-Loop in spaeteren Phasen.
+"""
+
+from __future__ import annotations
+
+from typing import List
+
+from memory import MemoryPoint
+
+TYPE_HEADINGS = {
+    "identity": "## Wer du bist",
+    "rule": "## Sicherheitsregeln & Prinzipien",
+    "preference": "## Benutzer-Praeferenzen",
+    "tool": "## Tool-Freigaben",
+    "skill": "## Deine Skills",
+}
+
+
+def build_hot_memory_section(pinned: List[MemoryPoint]) -> str:
+    """Baue den 'IMMER-im-Prompt'-Block aus pinned Punkten."""
+    grouped: dict[str, List[MemoryPoint]] = {}
+    for p in pinned:
+        grouped.setdefault(p.type, []).append(p)
+
+    parts: List[str] = []
+    # Sortier-Reihenfolge: identity → rule → preference → tool → skill → Rest
+    order = ["identity", "rule", "preference", "tool", "skill"]
+    for t in order:
+        items = grouped.pop(t, [])
+        if not items:
+            continue
+        parts.append(TYPE_HEADINGS.get(t, f"## {t}"))
+        for p in items:
+            parts.append(f"### {p.title}")
+            parts.append(p.content.strip())
+            parts.append("")
+
+    # uebrige Types (falls jemand was anderes als pinned markiert)
+    for t, items in grouped.items():
+        parts.append(f"## {t}")
+        for p in items:
+            parts.append(f"### {p.title}")
+            parts.append(p.content.strip())
+            parts.append("")
+
+    return "\n".join(parts).strip()
+
+
+def build_cold_memory_section(matches: List[MemoryPoint]) -> str:
+    """Baue 'Moeglicherweise relevant'-Block aus Search-Treffern."""
+    if not matches:
+        return ""
+    lines = ["## Moeglicherweise relevant (aus Gedaechtnis)"]
+    for p in matches:
+        score = f" [score={p.score:.2f}]" if p.score is not None else ""
+        lines.append(f"- **{p.title}**{score}")
+        lines.append(f"  {p.content.strip()}")
+    return "\n".join(lines)
+
+
+def build_skills_section(skills: List[dict]) -> str:
+    """Listet alle Skills (aktiv + deaktiviert) damit ARIA weiss was es gibt
+    und keine doppelt baut. Plus klare Schwelle wann ein Skill sich lohnt."""
+    lines = ["## Deine Skills"]
+    if skills:
+        for s in skills:
+            active = s.get("active", True)
+            marker = "" if active else " [DEAKTIVIERT — kann nicht aufgerufen werden]"
+            lines.append(f"- **{s.get('name', '?')}**{marker} — {s.get('description', '(ohne Beschreibung)')}")
+        lines.append("")
+        lines.append("Wenn ein vorhandener Skill zur Aufgabe passt: nutze ihn via Tool-Call.")
+    else:
+        lines.append("(noch keine Skills vorhanden)")
+
+    lines.append("")
+    lines.append("### Wann lohnt sich ein neuer Skill?")
+    lines.append("")
+    lines.append("**Skills sind IMMER Python** — eigene venv pro Skill mit den noetigen "
+                 "pip-Paketen. Kein apt im Skill, kein systemweiter Install. Python deckt "
+                 "in der Regel alles ab (yt-dlp, requests, pypdf, pillow, openpyxl, "
+                 "static-ffmpeg, beautifulsoup4, …). Falls etwas WIRKLICH nur via apt geht: "
+                 "Stefan fragen ob es ins Brain-Dockerfile soll.")
+    lines.append("")
+    lines.append("**Harte Regel — IMMER Skill anlegen wenn:** die Loesung erfordert eine "
+                 "pip-Library. Begruendung: Brain-Container hat keinen persistenten State "
+                 "ausser /data/skills/. Ohne Skill wuerde der Install bei jedem "
+                 "Container-Restart wiederholt.")
+    lines.append("")
+    lines.append("**Sonst — Skill nur wenn alle vier zutreffen:**")
+    lines.append("")
+    lines.append("1. **Wiederkehrend** — die Aufgabe wird realistisch nochmal gestellt. "
+                 "Einmal-Faelle (\"wie spaet ist es jetzt\") kein Skill.")
+    lines.append("2. **Nicht-trivial** — mehrere Schritte. Ein einzelner Shell-Befehl "
+                 "(`date`, `hostname`, `ls`) ist KEIN Skill — das macht Bash direkt.")
+    lines.append("3. **Parametrisierbar** — der Skill nimmt Eingaben (URL, Datei, Suchbegriff) "
+                 "und gibt ein nuetzliches Ergebnis zurueck.")
+    lines.append("4. **Wiederverwendbar als ganzes** — Stefan wuerde es zukuenftig per Name "
+                 "ansprechen (\"mach mir den YouTube zu MP3\") statt jedes Mal zu erklaeren.")
+    lines.append("")
+    lines.append("Wenn nichts installiert werden muss UND nicht alle vier zutreffen: einfach "
+                 "die Aufgabe loesen ohne Skill anzulegen. Stefan kann jederzeit sagen "
+                 "'bau daraus einen Skill'.")
+    return "\n".join(lines)
+
+
+def build_system_prompt(
+    pinned: List[MemoryPoint],
+    cold: List[MemoryPoint] | None = None,
+    skills: List[dict] | None = None,
+) -> str:
+    """Kompletter System-Prompt: Hot + Cold + Skills."""
+    parts = [build_hot_memory_section(pinned)]
+    if skills:
+        parts.append("")
+        parts.append(build_skills_section(skills))
+    if cold:
+        parts.append("")
+        parts.append(build_cold_memory_section(cold))
+    return "\n".join(parts).strip()

aria-brain/proxy_client.py

@@ -0,0 +1,125 @@
+"""
+Claude-Aufruf ueber den lokalen Proxy.
+
+Der Proxy (claude-max-api-proxy) bietet eine OpenAI-kompatible API
+unter http://proxy:3456/v1/chat/completions. Wir nutzen non-streaming
+mit einem laengeren Timeout — Claude Code spawnt pro Anfrage einen
+neuen CLI-Prozess (Cold-Start), das dauert.
+"""
+
+from __future__ import annotations
+
+import logging
+import os
+from typing import List, Optional
+
+import httpx
+from pydantic import BaseModel
+
+logger = logging.getLogger(__name__)
+
+DEFAULT_MODEL = os.environ.get("BRAIN_MODEL", "claude-sonnet-4")
+PROXY_URL = os.environ.get("PROXY_URL", "http://proxy:3456")
+PROXY_TIMEOUT_SEC = float(os.environ.get("PROXY_TIMEOUT_SEC", "300"))
+
+
+class Message(BaseModel):
+    role: str  # "system" | "user" | "assistant" | "tool"
+    content: Optional[str] = None
+    tool_calls: Optional[list] = None
+    tool_call_id: Optional[str] = None
+    name: Optional[str] = None  # nur fuer role=tool
+
+
+class ProxyResult(BaseModel):
+    content: str = ""
+    tool_calls: list = []  # je: {"id", "name", "arguments" (dict)}
+    finish_reason: str = ""
+
+
+class ProxyClient:
+    def __init__(self, base_url: str = PROXY_URL, model: str = DEFAULT_MODEL):
+        self.base_url = base_url.rstrip("/")
+        self.model = model
+        # Persistente Client-Connection — vermeidet TCP-Handshake bei jedem Call
+        self._client = httpx.Client(timeout=PROXY_TIMEOUT_SEC)
+
+    def chat(self, messages: List[Message], model: Optional[str] = None) -> str:
+        """Convenience: einfacher Chat ohne Tools. Gibt nur den Reply-String zurueck."""
+        result = self.chat_full(messages, tools=None, model=model)
+        if not result.content:
+            raise RuntimeError("Proxy lieferte leeren content")
+        return result.content
+
+    def chat_full(
+        self,
+        messages: List[Message],
+        tools: Optional[list] = None,
+        model: Optional[str] = None,
+    ) -> ProxyResult:
+        """Full chat — kann Tool-Calls liefern (wenn tools mitgegeben).
+
+        tools-Format ist OpenAI-Style:
+          [{"type":"function","function":{"name":..,"description":..,"parameters":{...}}}, ...]
+        """
+        url = f"{self.base_url}/v1/chat/completions"
+        # Pydantic-Dumps mit exclude_none damit role=tool ohne tool_calls geht
+        payload = {
+            "model": model or self.model,
+            "messages": [m.model_dump(exclude_none=True) for m in messages],
+        }
+        if tools:
+            payload["tools"] = tools
+        logger.info("Proxy → %s (%d Messages, %d tools, model=%s)",
+                    url, len(messages), len(tools or []), payload["model"])
+        try:
+            r = self._client.post(url, json=payload)
+        except httpx.RequestError as exc:
+            raise RuntimeError(f"Proxy unreachable: {exc}") from exc
+        if r.status_code != 200:
+            raise RuntimeError(f"Proxy HTTP {r.status_code}: {r.text[:300]}")
+        try:
+            data = r.json()
+        except Exception as exc:
+            raise RuntimeError(f"Proxy invalid JSON: {exc}") from exc
+
+        choices = data.get("choices") or []
+        if not choices:
+            raise RuntimeError(f"Proxy ohne choices: {str(data)[:300]}")
+
+        msg = choices[0].get("message") or {}
+        finish_reason = choices[0].get("finish_reason", "")
+
+        content = msg.get("content") or ""
+        if isinstance(content, list):
+            content = "".join(
+                part.get("text", "") for part in content if isinstance(part, dict) and part.get("type") == "text"
+            )
+
+        tool_calls_raw = msg.get("tool_calls") or []
+        tool_calls = []
+        import json as _json
+        for tc in tool_calls_raw:
+            fn = tc.get("function") or {}
+            args_raw = fn.get("arguments", "{}")
+            args: dict
+            if isinstance(args_raw, dict):
+                args = args_raw
+            else:
+                try:
+                    args = _json.loads(args_raw)
+                except Exception:
+                    args = {"_raw": args_raw}
+            tool_calls.append({
+                "id": tc.get("id", ""),
+                "name": fn.get("name", ""),
+                "arguments": args,
+            })
+
+        return ProxyResult(content=content or "", tool_calls=tool_calls, finish_reason=finish_reason)
+
+    def close(self):
+        try:
+            self._client.close()
+        except Exception:
+            pass

aria-brain/requirements.txt

@@ -0,0 +1,14 @@
+fastapi==0.115.0
+uvicorn[standard]==0.32.0
+pydantic==2.9.2
+httpx==0.27.2
+websockets==13.1
+
+# Vector-DB
+qdrant-client==1.12.1
+
+# Embeddings (laeuft auf CPU, ~120MB Modell)
+sentence-transformers==3.2.1
+
+# Utility
+python-multipart==0.0.12

aria-brain/skills.py

@@ -0,0 +1,373 @@
+"""
+Skill-Manager — Filesystem-Layer fuer ARIAs Faehigkeiten.
+
+Layout:
+  /data/skills/<name>/
+    skill.json   - Manifest
+    README.md    - Beschreibung (vom Stil her: was, wann, wie aufrufen)
+    run.sh       - Entry-Point (sh, python -m, was auch immer)
+    requirements.txt        - optional, fuer local-venv
+    venv/        - automatisch erzeugt bei local-venv
+    bin/         - statische Binaries (fuer local-bin)
+    logs/        - <ts>.json Run-Logs (append-only pro Run)
+
+Manifest (skill.json):
+  {
+    "name": "youtube2mp3",
+    "description": "Konvertiert YouTube-Video-URL zu MP3",
+    "execution": "local-venv" | "local-bin" | "bash",
+    "entry": "run.sh",
+    "args": [{"name": "url", "required": true}, ...],
+    "requires": {"pip": [...], "binaries": [...]},
+    "active": true,
+    "created_at": "ISO",
+    "updated_at": "ISO",
+    "last_used": null | "ISO",
+    "use_count": 0,
+    "version": "1.0",
+    "author": "aria" | "stefan"
+  }
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+import re
+import shutil
+import subprocess
+import time
+import uuid
+from datetime import datetime, timezone
+from pathlib import Path
+from typing import Optional
+
+logger = logging.getLogger(__name__)
+
+SKILLS_DIR = Path(os.environ.get("SKILLS_DIR", "/data/skills"))
+SHARED_UPLOADS = Path("/shared/uploads")
+
+VALID_EXECUTIONS = {"local-venv", "local-bin", "bash"}
+NAME_RE = re.compile(r"^[a-zA-Z0-9_-]{2,60}$")
+
+
+def _now() -> str:
+    return datetime.now(timezone.utc).isoformat()
+
+
+def _safe_name(name: str) -> str:
+    if not isinstance(name, str) or not NAME_RE.match(name):
+        raise ValueError(f"Ungültiger Skill-Name: {name!r}")
+    return name
+
+
+def _skill_dir(name: str) -> Path:
+    return SKILLS_DIR / _safe_name(name)
+
+
+# ─── Listing ────────────────────────────────────────────────────────
+
+def list_skills(active_only: bool = False) -> list[dict]:
+    out: list[dict] = []
+    if not SKILLS_DIR.exists():
+        return out
+    for entry in sorted(SKILLS_DIR.iterdir()):
+        if not entry.is_dir():
+            continue
+        manifest = read_manifest(entry.name)
+        if manifest is None:
+            continue
+        if active_only and not manifest.get("active", True):
+            continue
+        out.append(manifest)
+    return out
+
+
+def read_manifest(name: str) -> Optional[dict]:
+    try:
+        path = _skill_dir(name) / "skill.json"
+        if not path.exists():
+            return None
+        return json.loads(path.read_text(encoding="utf-8"))
+    except Exception as exc:
+        logger.warning("Manifest lesen %s: %s", name, exc)
+        return None
+
+
+def write_manifest(name: str, manifest: dict) -> None:
+    d = _skill_dir(name)
+    d.mkdir(parents=True, exist_ok=True)
+    manifest["updated_at"] = _now()
+    (d / "skill.json").write_text(json.dumps(manifest, indent=2, ensure_ascii=False), encoding="utf-8")
+
+
+def read_readme(name: str) -> str:
+    path = _skill_dir(name) / "README.md"
+    return path.read_text(encoding="utf-8") if path.exists() else ""
+
+
+# ─── Create / Update / Delete ────────────────────────────────────────
+
+def create_skill(
+    name: str,
+    description: str,
+    execution: str,
+    entry_code: str,
+    readme: str = "",
+    args: Optional[list] = None,
+    requires: Optional[dict] = None,
+    pip_packages: Optional[list[str]] = None,
+    author: str = "aria",
+) -> dict:
+    """Legt einen neuen Skill an. Wirft ValueError bei ungueltigen Inputs.
+
+    entry_code wird je nach execution in run.sh oder run.py geschrieben.
+    Bei local-venv wird sofort eine venv erzeugt + pip_packages installiert.
+    """
+    name = _safe_name(name)
+    if execution not in VALID_EXECUTIONS:
+        raise ValueError(f"execution muss eines von {VALID_EXECUTIONS} sein")
+    d = _skill_dir(name)
+    if d.exists():
+        raise ValueError(f"Skill '{name}' existiert bereits — erst loeschen oder updaten")
+
+    d.mkdir(parents=True)
+    (d / "logs").mkdir()
+
+    # Entry-File: run.sh oder run.py
+    if execution == "local-venv":
+        entry_path = d / "run.py"
+        entry_path.write_text(entry_code, encoding="utf-8")
+        entry_name = "run.py"
+        (d / "requirements.txt").write_text("\n".join(pip_packages or []) + "\n", encoding="utf-8")
+    else:
+        entry_path = d / "run.sh"
+        # Shebang ergaenzen wenn nicht da
+        content = entry_code if entry_code.startswith("#!") else "#!/usr/bin/env bash\nset -euo pipefail\n" + entry_code
+        entry_path.write_text(content, encoding="utf-8")
+        entry_path.chmod(0o755)
+        entry_name = "run.sh"
+
+    # README
+    (d / "README.md").write_text(readme or f"# {name}\n\n{description}\n", encoding="utf-8")
+
+    manifest = {
+        "name": name,
+        "description": description,
+        "execution": execution,
+        "entry": entry_name,
+        "args": args or [],
+        "requires": requires or {},
+        "active": True,
+        "created_at": _now(),
+        "updated_at": _now(),
+        "last_used": None,
+        "use_count": 0,
+        "version": "1.0",
+        "author": author,
+    }
+    write_manifest(name, manifest)
+
+    # venv aufbauen bei local-venv
+    if execution == "local-venv":
+        try:
+            _setup_venv(d, pip_packages or [])
+        except Exception as exc:
+            # venv-Aufbau fehlgeschlagen → Skill steht trotzdem im Repo, aber inaktiv
+            manifest["active"] = False
+            manifest["setup_error"] = str(exc)[:500]
+            write_manifest(name, manifest)
+            logger.warning("Skill %s: venv-Setup fehlgeschlagen → deaktiviert: %s", name, exc)
+
+    logger.info("Skill erstellt: %s (%s)", name, execution)
+    return manifest
+
+
+def _setup_venv(skill_dir: Path, pip_packages: list[str]) -> None:
+    venv = skill_dir / "venv"
+    logger.info("venv erstellen: %s", venv)
+    subprocess.run(["python", "-m", "venv", str(venv)], check=True, timeout=120)
+    pip = venv / "bin" / "pip"
+    if pip_packages:
+        subprocess.run([str(pip), "install", "--no-cache-dir", *pip_packages], check=True, timeout=600)
+
+
+def update_skill(name: str, patch: dict) -> dict:
+    manifest = read_manifest(name)
+    if manifest is None:
+        raise ValueError(f"Skill '{name}' nicht gefunden")
+    allowed = {"description", "args", "requires", "active", "version", "entry"}
+    for k, v in patch.items():
+        if k in allowed:
+            manifest[k] = v
+    write_manifest(name, manifest)
+    return manifest
+
+
+def delete_skill(name: str) -> None:
+    d = _skill_dir(name)
+    if not d.exists():
+        raise ValueError(f"Skill '{name}' nicht gefunden")
+    shutil.rmtree(d)
+    logger.info("Skill geloescht: %s", name)
+
+
+# ─── Run ────────────────────────────────────────────────────────────
+
+def run_skill(name: str, args: Optional[dict] = None, timeout_sec: int = 300) -> dict:
+    """Fuehrt einen Skill aus. Args werden als ENV-Vars uebergeben
+    (Praefix ARG_, z.B. ARG_URL fuer args["url"]).
+
+    Returns: {ok, exit_code, stdout, stderr, duration_sec, log_path}
+    """
+    manifest = read_manifest(name)
+    if manifest is None:
+        raise ValueError(f"Skill '{name}' nicht gefunden")
+    if not manifest.get("active", True):
+        raise ValueError(f"Skill '{name}' ist deaktiviert")
+
+    d = _skill_dir(name)
+    entry = manifest.get("entry", "run.sh")
+    exec_mode = manifest.get("execution", "bash")
+
+    env = os.environ.copy()
+    # Skill-Args als ENV-Vars
+    for k, v in (args or {}).items():
+        if not re.match(r"^[a-zA-Z][a-zA-Z0-9_]*$", k):
+            continue
+        env[f"ARG_{k.upper()}"] = str(v)
+    env["SKILL_DIR"] = str(d)
+    env["SHARED_UPLOADS"] = str(SHARED_UPLOADS)
+
+    # Command bauen
+    if exec_mode == "local-venv":
+        python = d / "venv" / "bin" / "python"
+        cmd = [str(python), str(d / entry)]
+    elif exec_mode == "local-bin":
+        # Skill bringt seine bin/ mit — wir prepended sie an den PATH
+        env["PATH"] = f"{d / 'bin'}:{env.get('PATH', '')}"
+        cmd = [str(d / entry)]
+    else:  # bash
+        cmd = [str(d / entry)]
+
+    log_id = f"{int(time.time())}-{uuid.uuid4().hex[:8]}"
+    log_path = d / "logs" / f"{log_id}.json"
+
+    t0 = time.time()
+    try:
+        proc = subprocess.run(
+            cmd, env=env, cwd=str(d),
+            capture_output=True, text=True, timeout=timeout_sec,
+        )
+        out_text = proc.stdout
+        err_text = proc.stderr
+        exit_code = proc.returncode
+        timed_out = False
+    except subprocess.TimeoutExpired as exc:
+        out_text = exc.stdout or ""
+        err_text = (exc.stderr or "") + f"\n[TIMEOUT {timeout_sec}s]"
+        exit_code = -1
+        timed_out = True
+    duration = time.time() - t0
+
+    # Log schreiben (gekuerzt damit es nicht explodiert)
+    record = {
+        "ts": _now(),
+        "args": args or {},
+        "exit_code": exit_code,
+        "duration_sec": round(duration, 2),
+        "stdout": (out_text or "")[:8000],
+        "stderr": (err_text or "")[:8000],
+        "timed_out": timed_out,
+    }
+    try:
+        log_path.write_text(json.dumps(record, indent=2, ensure_ascii=False), encoding="utf-8")
+    except Exception:
+        pass
+
+    # Stats updaten
+    manifest["last_used"] = _now()
+    manifest["use_count"] = int(manifest.get("use_count", 0)) + 1
+    write_manifest(name, manifest)
+
+    record["ok"] = exit_code == 0
+    record["log_path"] = str(log_path)
+    return record
+
+
+def list_logs(name: str, limit: int = 50) -> list[dict]:
+    d = _skill_dir(name) / "logs"
+    if not d.exists():
+        return []
+    files = sorted(d.glob("*.json"), reverse=True)[:limit]
+    out: list[dict] = []
+    for f in files:
+        try:
+            data = json.loads(f.read_text(encoding="utf-8"))
+            data["log_id"] = f.stem
+            out.append(data)
+        except Exception:
+            continue
+    return out
+
+
+# ─── Export / Import ────────────────────────────────────────────────
+
+def export_skill(name: str) -> bytes:
+    """Packt einen Skill als tar.gz und gibt die Bytes zurueck.
+    venv und logs werden ausgeschlossen (werden beim Import neu gebaut)."""
+    import io
+    import tarfile
+    d = _skill_dir(name)
+    if not d.exists():
+        raise ValueError(f"Skill '{name}' nicht gefunden")
+    buf = io.BytesIO()
+    with tarfile.open(fileobj=buf, mode="w:gz") as tar:
+        for path in d.iterdir():
+            if path.name in ("venv", "logs", "__pycache__"):
+                continue
+            tar.add(path, arcname=f"{name}/{path.name}")
+    return buf.getvalue()
+
+
+def import_skill(tar_bytes: bytes, overwrite: bool = False) -> dict:
+    """Importiert einen Skill aus tar.gz. Liefert das Manifest zurueck."""
+    import io
+    import tarfile
+    SKILLS_DIR.mkdir(parents=True, exist_ok=True)
+    with tarfile.open(fileobj=io.BytesIO(tar_bytes), mode="r:gz") as tar:
+        # Erst Root-Name finden (= Skill-Name)
+        members = tar.getmembers()
+        if not members:
+            raise ValueError("Leeres Archiv")
+        root = members[0].name.split("/", 1)[0]
+        name = _safe_name(root)
+        d = _skill_dir(name)
+        if d.exists():
+            if not overwrite:
+                raise ValueError(f"Skill '{name}' existiert bereits — overwrite=true setzen")
+            shutil.rmtree(d)
+        # Extrahieren — Path-Traversal verhindern
+        for m in members:
+            target = SKILLS_DIR / m.name
+            if not str(target.resolve()).startswith(str(SKILLS_DIR.resolve())):
+                raise ValueError(f"Unsicherer Pfad im Archiv: {m.name}")
+        tar.extractall(SKILLS_DIR)
+    # logs-Verzeichnis anlegen falls fehlte
+    (d / "logs").mkdir(exist_ok=True)
+    # venv neu bauen falls local-venv
+    manifest = read_manifest(name) or {}
+    if manifest.get("execution") == "local-venv":
+        req_file = d / "requirements.txt"
+        pip_packages: list[str] = []
+        if req_file.exists():
+            pip_packages = [l.strip() for l in req_file.read_text().splitlines() if l.strip() and not l.startswith("#")]
+        try:
+            _setup_venv(d, pip_packages)
+        except Exception as exc:
+            logger.warning("Skill-Import %s: venv-Setup fehlgeschlagen: %s", name, exc)
+            manifest["active"] = False
+            manifest["setup_error"] = str(exc)[:500]
+            write_manifest(name, manifest)
+    return manifest

bridge/aria_bridge.py

@@ -536,14 +536,9 @@ class ARIABridge:
         # sprengt die argv-Liste beim Claude-Subprocess-Spawn (E2BIG). Bei
         # COMPACT_AFTER erreicht → Sessions reset + Container restart.
         # Counter ueberlebt Bridge-Restart nicht (frischer Zaehler beim Start ok).
-        self._user_message_count: int = 0
+        # _user_message_count + _compact_after entfallen — Auto-Compact war
-        # Aus runtime.json gelesen (Diagnostic → Einstellungen → Compact-Schwelle)
+        # aria-core-spezifisch (E2BIG-Schutz). Der neue Brain-Loop kennt
-        # Default 140, 0 = deaktiviert
+        # diese Begrenzung nicht.
-        try:
-            rt = json.loads(Path("/shared/config/runtime.json").read_text()) if Path("/shared/config/runtime.json").exists() else {}
-            self._compact_after = int(rt.get("compactAfterMessages", 140))
-        except Exception:
-            self._compact_after = 140
         # Pending Files: wenn die App ein Bild + Text gleichzeitig schickt, kommen
         # zwei separate RVS-Events ('file' und 'chat') — wir buffern die Files
         # kurz und mergen sie mit dem nachfolgenden Chat-Text zu einer einzigen
@@ -1176,73 +1171,108 @@ class ARIABridge:
         await self.send_to_core(text, source="app-file+chat")
         return True
 
-    async def _trigger_session_reset(self) -> None:
-        """Sessions loeschen + Container restart via Diagnostic HTTP-API."""
-        try:
-            req = urllib.request.Request(
-                "http://localhost:3001/api/aria-session-reset",
-                data=b"{}",
-                method="POST",
-                headers={"Content-Type": "application/json"},
-            )
-            def _do_reset():
-                try:
-                    with urllib.request.urlopen(req, timeout=45) as resp:
-                        return resp.status
-                except Exception as e:
-                    return f"err:{e}"
-            result = await asyncio.get_event_loop().run_in_executor(None, _do_reset)
-            logger.info("[core] Session-Reset Result: %s", result)
-        except Exception as e:
-            logger.warning("[core] Session-Reset Trigger fehlgeschlagen: %s", e)
-
     async def send_to_core(self, text: str, source: str = "bridge") -> None:
-        """Sendet Text an aria-core (OpenClaw chat.send Protokoll)."""
+        """Sendet Text an aria-brain (HTTP /chat) und broadcastet die Antwort.
-        if self.ws_core is None:
-            logger.error("[core] Nicht verbunden — Nachricht verworfen: '%s'", text[:60])
-            return
 
-        # Auto-Compact: bei zu vielen User-Messages laeuft argv beim Subprocess-
+        Nicht-Streaming: wir warten bis Brain fertig ist, dann pushen wir
-        # Spawn ueber (E2BIG). Vor send pruefen, ggf. Sessions resetten.
+        die komplette Reply via RVS an alle Clients (App + Diagnostic).
-        if source.startswith("app") and self._compact_after > 0:
+        TTS wird vom Bridge-Code separat angestossen (gleiche Logik wie
-            self._user_message_count += 1
+        vorher mit aria-core).
-            if self._user_message_count >= self._compact_after:
+        """
-                logger.warning("[core] Auto-Compact: %d Messages erreicht — Session-Reset",
+        brain_url = os.environ.get("BRAIN_URL", "http://aria-brain:8080")
-                               self._user_message_count)
+        url = f"{brain_url}/chat"
-                self._user_message_count = 0
+        payload = json.dumps({"message": text, "source": source}).encode("utf-8")
-                # Reset triggern via Diagnostic (asynchron, blockiert send nicht)
+        logger.info("[brain] chat ← %s '%s'", source, text[:80])
-                asyncio.create_task(self._trigger_session_reset())
-                # User informieren — der naechste Request kommt erst nach Restart durch
-                await self._send_to_rvs({
-                    "type": "chat",
-                    "payload": {
-                        "text": f"[Compact] Konversation war lang ({self._compact_after} Nachrichten) — Session wurde geleert, ARIA startet frisch. Deine letzte Nachricht bitte gleich nochmal senden.",
-                        "sender": "aria",
-                    },
-                    "timestamp": int(asyncio.get_event_loop().time() * 1000),
-                })
-                return
 
-        # Aktive Session vom Diagnostic holen
+        # agent_activity broadcasten (App + Diagnostic "ARIA denkt..." Indicator)
-        self._fetch_active_session()
+        await self._send_to_rvs({
-
+            "type": "agent_activity",
-        req_id = self._next_req_id()
+            "payload": {"activity": "thinking"},
-        message = json.dumps({
+            "timestamp": int(asyncio.get_event_loop().time() * 1000),
-            "type": "req",
-            "id": req_id,
-            "method": "chat.send",
-            "params": {
-                "sessionKey": self._session_key,
-                "message": text,
-                "idempotencyKey": str(uuid.uuid4()),
-            },
         })
 
+        def _do_call():
+            try:
+                req = urllib.request.Request(
+                    url, data=payload, method="POST",
+                    headers={"Content-Type": "application/json"},
+                )
+                # Cold-Start kann lange dauern, 5min Timeout
+                with urllib.request.urlopen(req, timeout=300) as resp:
+                    return resp.status, resp.read().decode("utf-8", errors="ignore")
+            except Exception as exc:
+                return None, str(exc)
+
+        status, body = await asyncio.get_event_loop().run_in_executor(None, _do_call)
+        if status != 200:
+            logger.error("[brain] /chat fehlgeschlagen: status=%s body=%s", status, body[:200])
+            await self._send_to_rvs({
+                "type": "agent_activity",
+                "payload": {"activity": "idle"},
+                "timestamp": int(asyncio.get_event_loop().time() * 1000),
+            })
+            await self._send_to_rvs({
+                "type": "chat",
+                "payload": {
+                    "text": f"[Brain-Fehler] {body[:200] or 'unbekannt'}",
+                    "sender": "aria",
+                },
+                "timestamp": int(asyncio.get_event_loop().time() * 1000),
+            })
+            return
+
         try:
-            await self.ws_core.send(message)
+            data = json.loads(body)
-            logger.info("[core] chat.send (%s, id=%s): '%s'", source, req_id, text[:80])
         except Exception:
-            logger.exception("[core] Sendefehler")
+            logger.error("[brain] /chat lieferte ungueltiges JSON: %s", body[:200])
+            await self._send_to_rvs({
+                "type": "agent_activity",
+                "payload": {"activity": "idle"},
+                "timestamp": int(asyncio.get_event_loop().time() * 1000),
+            })
+            return
+
+        reply = (data.get("reply") or "").strip()
+        if not reply:
+            logger.warning("[brain] /chat: leerer Reply")
+            await self._send_to_rvs({
+                "type": "agent_activity",
+                "payload": {"activity": "idle"},
+                "timestamp": int(asyncio.get_event_loop().time() * 1000),
+            })
+            return
+
+        # Side-Channel-Events VOR der Chat-Bubble broadcasten (z.B. skill_created)
+        # damit sie in der UI vor der Reply auftauchen
+        for event in data.get("events", []) or []:
+            etype = event.get("type")
+            if etype == "skill_created":
+                await self._send_to_rvs({
+                    "type": "skill_created",
+                    "payload": event.get("skill", {}),
+                    "timestamp": int(asyncio.get_event_loop().time() * 1000),
+                })
+                logger.info("[brain] ARIA hat einen Skill erstellt: %s",
+                            event.get("skill", {}).get("name"))
+
+        # _process_core_response uebernimmt alles weitere:
+        # File-Marker extrahieren + broadcasten, NO_REPLY-Check, Chat-
+        # Broadcast an RVS, TTS, agent_activity idle. Wir geben das
+        # raw payload mit dem reply rein damit Mode/voice-Metadata
+        # passend behandelt wird (hier minimal, weil Brain noch keine
+        # metadata mitschickt).
+        try:
+            await self._process_core_response(reply, {})
+        except Exception:
+            logger.exception("[brain] _process_core_response Fehler")
+            await self._send_to_rvs({
+                "type": "agent_activity",
+                "payload": {"activity": "idle"},
+                "timestamp": int(asyncio.get_event_loop().time() * 1000),
+            })
+
+        if data.get("distilling"):
+            logger.info("[brain] Destillat laeuft im Hintergrund")
 
     # ── RVS Verbindung (App-Relay) ──────────────────────────
 
@@ -1627,21 +1657,67 @@ class ARIABridge:
             except Exception as e:
                 logger.warning("[rvs] file_saved konnte nicht an App gesendet werden: %s", e)
 
-        elif msg_type == "aria_session_reset":
+        elif msg_type == "file_list_request":
-            # Manueller Compact-Trigger: Sessions weg + Restart
+            # App fragt die Liste aller /shared/uploads/-Dateien an.
-            logger.warning("[rvs] aria_session_reset Request von App")
+            logger.info("[rvs] file_list_request von App")
-            self._user_message_count = 0
-            asyncio.create_task(self._trigger_session_reset())
-            return
-
-        elif msg_type == "aria_restart":
-            # App-Button "ARIA hart neu starten" → docker restart aria-core
-            # via Diagnostic (der hat den Docker-Socket gemountet).
-            logger.warning("[rvs] aria_restart Request von App — harter Container-Restart")
             try:
                 req = urllib.request.Request(
-                    "http://localhost:3001/api/aria-restart",
+                    "http://localhost:3001/api/files-list",
-                    data=b"{}",
+                    method="GET",
+                )
+                def _do_list():
+                    try:
+                        with urllib.request.urlopen(req, timeout=10) as resp:
+                            return json.loads(resp.read().decode("utf-8", errors="ignore"))
+                    except Exception as e:
+                        return {"ok": False, "error": str(e)}
+                d = await asyncio.get_event_loop().run_in_executor(None, _do_list)
+                await self._send_to_rvs({
+                    "type": "file_list_response",
+                    "payload": d,
+                    "timestamp": int(asyncio.get_event_loop().time() * 1000),
+                })
+            except Exception as e:
+                logger.warning("[rvs] file_list_request: %s", e)
+            return
+
+        elif msg_type == "file_delete_request":
+            # App will eine Datei loeschen — leite an Diagnostic.
+            p = payload.get("path", "")
+            logger.warning("[rvs] file_delete_request von App: %s", p)
+            try:
+                body_bytes = json.dumps({"path": p}).encode("utf-8")
+                req = urllib.request.Request(
+                    "http://localhost:3001/api/files-delete",
+                    data=body_bytes,
+                    method="POST",
+                    headers={"Content-Type": "application/json"},
+                )
+                def _do_delete():
+                    try:
+                        with urllib.request.urlopen(req, timeout=10) as resp:
+                            return resp.status, resp.read().decode("utf-8", errors="ignore")
+                    except Exception as e:
+                        return None, str(e)
+                status, body = await asyncio.get_event_loop().run_in_executor(None, _do_delete)
+                logger.info("[rvs] file_delete_request %s: status=%s", p, status)
+                # Diagnostic broadcastet file_deleted via sendToRVS_raw — kommt
+                # ueber den persistenten WS-Path zur App. Wir bestaetigen
+                # zusaetzlich, damit der Caller sicher ist dass es durch ist.
+            except Exception as e:
+                logger.warning("[rvs] file_delete_request: %s", e)
+            return
+
+        elif msg_type == "container_restart":
+            # App-Button "Container neu" — leitet generisch an Diagnostic
+            # weiter. Whitelist ist im Diagnostic-Server.
+            name = payload.get("name", "")
+            logger.warning("[rvs] container_restart Request von App: %s", name)
+            try:
+                body_bytes = json.dumps({"name": name}).encode("utf-8")
+                req = urllib.request.Request(
+                    "http://localhost:3001/api/container-restart",
+                    data=body_bytes,
                     method="POST",
                     headers={"Content-Type": "application/json"},
                 )
@@ -1652,49 +1728,19 @@ class ARIABridge:
                     except Exception as e:
                         return None, str(e)
                 status, body = await asyncio.get_event_loop().run_in_executor(None, _do_restart)
-                logger.info("[rvs] aria_restart Result: status=%s", status)
+                logger.info("[rvs] container_restart %s Result: status=%s", name, status)
-                # Note: bei erfolgreichem Restart ist die RVS-Verbindung sehr
-                # wahrscheinlich kurz weg (aria-bridge ist im service:aria-Network).
-                # Die Antwort kommt evtl. nicht mehr durch — egal.
-            except Exception as e:
-                logger.warning("[rvs] aria_restart Weiterleitung fehlgeschlagen: %s", e)
-            return
-
-        elif msg_type == "doctor_fix":
-            # App-Button "ARIA reparieren" → openclaw doctor --fix anstossen.
-            # Bridge erreicht aria-core nicht via docker (kein docker-socket
-            # gemountet), aber der Diagnostic-Server hat den Socket. HTTP-Call
-            # an http://localhost:3001/api/doctor-fix.
-            logger.info("[rvs] doctor_fix Request von App — leite an Diagnostic weiter")
-            try:
-                req = urllib.request.Request(
-                    "http://localhost:3001/api/doctor-fix",
-                    data=b"{}",
-                    method="POST",
-                    headers={"Content-Type": "application/json"},
-                )
-                # Blocking call ist OK weil openclaw doctor schnell durchlaeuft.
-                # In Executor laufen lassen damit der asyncio-Loop nicht blockt.
-                def _do_fix():
-                    try:
-                        with urllib.request.urlopen(req, timeout=30) as resp:
-                            return resp.status, resp.read().decode("utf-8", errors="ignore")
-                    except Exception as e:
-                        return None, str(e)
-                status, body = await asyncio.get_event_loop().run_in_executor(None, _do_fix)
                 ok = status == 200
-                logger.info("[rvs] doctor_fix Result: status=%s ok=%s", status, ok)
                 await self._send_to_rvs({
                     "type": "chat",
                     "payload": {
-                        "text": "[Reparatur] ARIA wurde durchgecheckt — sollte wieder antworten." if ok
+                        "text": f"[Container] {name} neu gestartet." if ok
-                                else f"[Reparatur] Fehlgeschlagen: {body[:200]}",
+                                else f"[Container] Restart {name} fehlgeschlagen: {body[:200]}",
                         "sender": "aria",
                     },
                     "timestamp": int(asyncio.get_event_loop().time() * 1000),
                 })
             except Exception as e:
-                logger.warning("[rvs] doctor_fix Weiterleitung fehlgeschlagen: %s", e)
+                logger.warning("[rvs] container_restart Weiterleitung fehlgeschlagen: %s", e)
             return
 
         elif msg_type == "file_request":
@@ -2122,7 +2168,8 @@ class ARIABridge:
         self.running = True
 
         tasks = [
-            asyncio.create_task(self.connect_to_core()),
+            # connect_to_core entfaellt — Bridge ruft jetzt aria-brain ueber
+            # HTTP (siehe send_to_core). Keine persistente WS-Verbindung mehr.
             asyncio.create_task(self.connect_to_rvs()),
         ]
 

docker-compose.yml

@@ -28,35 +28,40 @@ services:
     networks:
       - aria-net
 
-  # ─── OpenClaw (ARIA Gehirn) ─────────────────────────────
+  # ─── Qdrant (Vector-DB fuer ARIAs Gedaechtnis) ────────
-  aria:
+  # Storage liegt im Repo-Bind-Mount aria-data/brain/qdrant.
-    image: ghcr.io/openclaw/openclaw:latest
+  # Damit Backup/Export/Import komplett ueber das Filesystem gehen.
-    container_name: aria-core
+  qdrant:
-    hostname: aria-wohnung
+    image: qdrant/qdrant:latest
-    privileged: true                               # ARIAs Wohnung — sie hat die Schlüssel
+    container_name: aria-qdrant
+    volumes:
+      - ./aria-data/brain/qdrant:/qdrant/storage
+    restart: unless-stopped
+    networks:
+      - aria-net
+
+  # ─── ARIA Brain (Agent + Memory) ─────────────────────────
+  # Loest das alte aria-core (OpenClaw) ab. Vector-DB-basiertes
+  # Memory, eigener Agent-Loop, SSH zur aria-wohnung-VM.
+  brain:
+    build: ./aria-brain
+    container_name: aria-brain
+    hostname: aria-wohnung-brain                    # damit ssh known_hosts stabil bleibt
     extra_hosts:
       - "host.docker.internal:host-gateway"        # Zugriff auf die VM via SSH
     depends_on:
+      - qdrant
       - proxy
-    ports:
-      - "3001:3001"                                # Diagnostic Web-UI (laeuft im shared network)
     environment:
-      - CANVAS_HOST=127.0.0.1
+      - QDRANT_HOST=aria-qdrant
-      - OPENCLAW_GATEWAY_TOKEN=${ARIA_AUTH_TOKEN}
+      - QDRANT_PORT=6333
-      - DEFAULT_MODEL=proxy/claude-sonnet-4
+      - PROXY_URL=http://proxy:3456
-      - RATE_LIMIT_PER_USER=30
+      - ARIA_AUTH_TOKEN=${ARIA_AUTH_TOKEN:-}
-      - DISPLAY=:0
     volumes:
-      # PHASE A — OpenClaw laeuft noch, aber System-Prompt-Files sind nach
+      - ./aria-data/brain/data:/data                # Memory-Cache + Skills + Models (bind-mount fuer Export)
-      # aria-data/brain-import/ gewandert und werden vom OpenClaw nicht mehr
+      - ./aria-data/brain-import:/import:ro         # Quell-MDs fuer den initialen Memory-Import (read-only)
-      # gelesen. ARIA antwortet bis zum Abriss ohne ihre Persoenlichkeit —
+      - ./aria-data/ssh:/root/.ssh                  # SSH-Keys fuer aria-wohnung (geteilt mit Proxy)
-      # einfach "raw Claude" durch den Proxy.
+      - aria-shared:/shared                         # gleicher Austausch-Speicher wie Bridge
-      - openclaw-config:/home/node/.openclaw       # bleibt — enthaelt Memory + Sessions fuer den Import-Schritt
-      - claude-config:/home/node/.claude
-      - ./aria-data/ssh:/home/node/.ssh
-      - /tmp/.X11-unix:/tmp/.X11-unix
-      - /var/run/docker.sock:/var/run/docker.sock
-      - aria-shared:/shared
     restart: unless-stopped
     networks:
       - aria-net
@@ -66,10 +71,13 @@ services:
     build: ./bridge
     container_name: aria-bridge
     depends_on:
-      - aria
+      - brain
-    network_mode: "service:aria"                   # Teilt Netzwerk mit aria-core → localhost:18789
+    networks:
+      - aria-net
+    ports:
+      - "3001:3001"                                # Diagnostic Web-UI (Diagnostic teilt Netzwerk mit Bridge)
     volumes:
-      - aria-shared:/shared                        # Shared Volume fuer Datei-Austausch (Bridge <> Core)
+      - aria-shared:/shared                        # Shared Volume fuer Datei-Austausch
       # Audio-Zugriff
       - /run/user/1000/pulse:/run/user/1000/pulse
       - /dev/snd:/dev/snd
@@ -77,6 +85,7 @@ services:
       - /dev/snd
     environment:
       - PULSE_SERVER=unix:/run/user/1000/pulse/native
+      - BRAIN_URL=http://aria-brain:8080
       - ARIA_AUTH_TOKEN=${ARIA_AUTH_TOKEN:-}
       - RVS_HOST=${RVS_HOST:-}
       - RVS_PORT=${RVS_PORT:-443}
@@ -86,19 +95,23 @@ services:
     restart: unless-stopped
 
   # ─── Diagnostic (Selbstcheck-UI und Einstellungen) ────
+  # Teilt Netzwerk mit Bridge, damit der Diagnostic-Server die
+  # Bridge auf localhost erreichen kann.
   diagnostic:
     build: ./diagnostic
     container_name: aria-diagnostic
     depends_on:
-      - aria
+      - bridge
-    network_mode: "service:aria"                   # Teilt Netzwerk mit aria-core → localhost:18789
+    network_mode: "service:bridge"
     volumes:
-      - /var/run/docker.sock:/var/run/docker.sock:ro
+      - /var/run/docker.sock:/var/run/docker.sock      # Container Restart + Brain-Export/Import
       - ./aria-data/config/diag-state:/data           # Persistenter State (aktive Session etc.)
-      - aria-shared:/shared                            # Shared Volume (Uploads + Config)
+      - aria-shared:/shared                            # Shared Volume (Uploads + Config + Voices)
+      - ./aria-data/brain:/brain                       # Brain-Export/Import (tar.gz aus Bind-Mount)
     environment:
       - ARIA_AUTH_TOKEN=${ARIA_AUTH_TOKEN:-}
       - PROXY_URL=http://proxy:3456
+      - BRAIN_URL=http://aria-brain:8080
       - RVS_HOST=${RVS_HOST:-}
       - RVS_PORT=${RVS_PORT:-443}
       - RVS_TLS=${RVS_TLS:-true}
@@ -107,9 +120,7 @@ services:
     restart: unless-stopped
 
 volumes:
-  openclaw-config:                                 # Persistiert ~/.openclaw (Model, Auth, Sessions)
+  aria-shared:                                     # Datei-Austausch zwischen Bridge / Brain / Diagnostic
-  claude-config:                                   # Persistiert ~/.claude (Permissions, Settings)
-  aria-shared:                                     # Datei-Austausch zwischen Bridge und Core
 
 networks:
   aria-net: