fix: Code-Quality und Security-Verbesserungen
Security Fixes: - Fix XSS vulnerability in orchestrator.html (escapeHtml für user input) - Verbesserte Error-Handling: 4 bare except clauses mit spezifischen Exception-Typen Code Quality: - Logging für alle Exception-Handler hinzugefügt - Timeout für Agent-Tasks von 300s auf 600s erhöht (10 Min) - Bessere Kommentare für Exception-Handling Performance: - Wissensdatenbank aus Systemprompt entfernt - Agents nutzen @READ_KNOWLEDGE für on-demand Zugriff - Reduziert Prompt-Größe um ~15KB pro Task UI Improvements (aus vorherigem Work): - Tasks: Auto-Refresh Info statt Toggle - Tasks: Status-Anzeigen statt manuelle Buttons - Konsistentes Auto-Refresh (15s) wenn Tasks aktiv
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parent
93eb8c6d47
commit
ca820d20db
3 changed files with 290 additions and 41 deletions
282
app.py
282
app.py
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@ -60,7 +60,8 @@ def get_agent_memory(agent_key, memory_type='tasks'):
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try:
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with open(memory_file, 'r', encoding='utf-8') as f:
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return json.load(f)
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except:
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except (json.JSONDecodeError, IOError, OSError) as e:
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logging.warning(f"Fehler beim Laden von {memory_file}: {e}")
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pass
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return []
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@ -210,7 +211,8 @@ def get_agent_config():
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try:
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with open(AGENT_CONFIG_FILE, 'r', encoding='utf-8') as f:
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return json.load(f)
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except:
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except (json.JSONDecodeError, IOError, OSError) as e:
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logging.warning(f"Fehler beim Laden von {AGENT_CONFIG_FILE}: {e}")
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pass
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return {}
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@ -396,21 +398,53 @@ def execute_agent_task(agent_key, user_prompt, extra_context=""):
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# Memory-Zusammenfassung laden
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memory_summary = get_agent_memory_summary(agent_key)
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# Wissensdatenbank-Pfad (Agent holt sich selbst was er braucht)
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kb_file = os.path.join(os.path.dirname(__file__), 'diversityball_knowledge.md')
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kb_content = ""
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if os.path.exists(kb_file):
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with open(kb_file, 'r', encoding='utf-8') as f:
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kb_content = f.read()
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# System-Prompt = Agent-Rolle + Wissensdatenbank + Memory
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# System-Prompt = Agent-Rolle + Memory + Kommandos (OHNE große Wissensdatenbank!)
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full_system = f"""{system_prompt}
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## Wissensdatenbank (Diversity-Ball):
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{kb_content}
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## Deine Erinnerungen:
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{memory_summary}
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## Wissensdatenbank:
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Die Wissensdatenbank liegt unter: {kb_file}
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Wenn du spezifische Informationen brauchst:
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@READ_KNOWLEDGE
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Topic: [Thema, z.B. "Budget", "Catering", "Location"]
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@END
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Falls du etwas nicht findest:
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@ASK_ORCHESTRATOR
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Question: [Deine Frage]
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Context: [Kontext]
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@END
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## Agent-Kollaboration:
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Du kannst mit anderen Agents kommunizieren! Verwende folgendes Format:
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**Frage an Orchestrator stellen (er delegiert an passenden Agent):**
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@ASK_ORCHESTRATOR
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Question: [Deine Frage]
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Context: [Warum brauchst du diese Info?]
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@END
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**Sub-Task erstellen (Orchestrator delegiert automatisch):**
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@CREATE_SUBTASK
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Task: [Was soll gemacht werden]
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Requirements: [Anforderungen/Details]
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@END
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**Neuen Agent vorschlagen (wenn Fähigkeit fehlt):**
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@SUGGEST_AGENT
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Role: [Rolle/Beschreibung]
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Skills: [Benötigte Fähigkeiten]
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Reason: [Warum wird dieser Agent gebraucht?]
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@END
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Der Orchestrator kümmert sich um die Zuweisung und Kommunikation!
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## Wichtig:
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- Du hast Zugriff auf das Internet via WebFetch-Tool - nutze es aktiv!
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- Du kannst Emails versenden - nutze send_email wenn beauftragt
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@ -432,7 +466,7 @@ def execute_agent_task(agent_key, user_prompt, extra_context=""):
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['opencode', 'run', '--model', model, '--format', 'json', combined_message],
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capture_output=True,
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text=True,
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timeout=300,
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timeout=600, # 10 Minuten für komplexe Tasks
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cwd=work_dir # Agent arbeitet in seinem work-Verzeichnis
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)
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@ -444,8 +478,14 @@ def execute_agent_task(agent_key, user_prompt, extra_context=""):
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data = json.loads(line)
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if data.get('part', {}).get('type') == 'text':
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response_text += data.get('part', {}).get('text', '')
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except:
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except (json.JSONDecodeError, KeyError):
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# Ignore invalid JSON lines in streaming output
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pass
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# Agent-Kommandos parsen (Task-Delegation, Fragen, Agent-Erstellung)
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if response_text:
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parse_agent_commands(agent_key, response_text)
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return response_text if response_text else "⚠️ Keine Antwort erhalten."
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else:
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return f"⚠️ Fehler: {result.stderr}"
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@ -494,6 +534,202 @@ tasks = []
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chat_history = []
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orchestrator_chat = []
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# ── Agent Message Queue ─────────────────────────────────────────────────────
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# Ermöglicht Agent-zu-Agent Kommunikation
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agent_messages = [] # Liste von {id, from_agent, to_agent, message_type, content, status, created, response}
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def send_agent_message(from_agent, to_agent, message_type, content):
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"""Sendet eine Nachricht von einem Agent an einen anderen.
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message_type kann sein:
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- 'task_request': Bitte den Ziel-Agent einen Task zu erledigen
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- 'question': Stelle eine Frage
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- 'info': Teile Information
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- 'create_agent': Bitte um Erstellung eines neuen Agents
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"""
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message = {
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'id': len(agent_messages) + 1,
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'from_agent': from_agent,
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'to_agent': to_agent,
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'message_type': message_type,
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'content': content,
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'status': 'pending',
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'created': datetime.now().strftime('%Y-%m-%d %H:%M:%S'),
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'response': None
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}
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agent_messages.append(message)
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logger.info(f"[AgentMsg] {from_agent} → {to_agent}: {message_type}")
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return message
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def get_agent_messages(agent_key, status='pending'):
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"""Holt alle Nachrichten für einen Agent."""
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return [m for m in agent_messages if m['to_agent'] == agent_key and m['status'] == status]
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def respond_to_message(message_id, response):
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"""Agent antwortet auf eine Nachricht."""
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for msg in agent_messages:
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if msg['id'] == message_id:
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msg['response'] = response
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msg['status'] = 'answered'
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logger.info(f"[AgentMsg] Message #{message_id} beantwortet")
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return True
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return False
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def parse_agent_commands(agent_key, response_text):
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"""Parst Agent-Antwort nach Orchestrator-Kommandos und führt sie aus."""
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import re
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# ASK_ORCHESTRATOR: Agent stellt Frage an Orchestrator
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ask_requests = re.findall(
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r'@ASK_ORCHESTRATOR\s*\nQuestion:\s*([^\n]+)\s*\nContext:\s*([^@]+)@END',
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response_text,
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re.DOTALL
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)
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for question, context in ask_requests:
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# Erstelle Task für Orchestrator um die Frage zu beantworten
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new_task = {
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'id': len(tasks) + 1,
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'title': f"Frage von {agent_key}: {question.strip()[:80]}",
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'description': f"""Ein Agent braucht Hilfe!
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**Von:** {agent_key}
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**Frage:** {question.strip()}
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**Kontext:** {context.strip()}
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Bitte beantworte die Frage oder delegiere an den passenden Experten-Agent.
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Die Antwort wird an {agent_key} zurückgegeben.""",
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'assigned_agent': 'Orchestrator',
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'agent_key': 'orchestrator',
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'status': 'pending',
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'created': datetime.now().strftime('%Y-%m-%d %H:%M'),
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'type': 'agent_question',
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'from_agent': agent_key,
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'return_to': agent_key
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}
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tasks.append(new_task)
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logger.info(f"[AgentCmd] {agent_key} fragt Orchestrator: {question.strip()[:50]}")
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# CREATE_SUBTASK: Agent möchte Subtask erstellen
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subtask_requests = re.findall(
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r'@CREATE_SUBTASK\s*\nTask:\s*([^\n]+)\s*\nRequirements:\s*([^@]+)@END',
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response_text,
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re.DOTALL
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)
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for task_desc, requirements in subtask_requests:
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new_task = {
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'id': len(tasks) + 1,
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'title': task_desc.strip()[:100],
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'description': f"Von {agent_key} angefordert:\n{requirements.strip()}",
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'assigned_agent': 'Orchestrator',
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'agent_key': 'orchestrator',
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'status': 'pending',
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'created': datetime.now().strftime('%Y-%m-%d %H:%M'),
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'type': 'agent_subtask',
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'from_agent': agent_key
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}
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tasks.append(new_task)
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logger.info(f"[AgentCmd] {agent_key} erstellt Subtask via Orchestrator")
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# SUGGEST_AGENT: Agent schlägt neuen Agent vor
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suggest_requests = re.findall(
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r'@SUGGEST_AGENT\s*\nRole:\s*([^\n]+)\s*\nSkills:\s*([^\n]+)\s*\nReason:\s*([^@]+)@END',
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response_text,
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re.DOTALL
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)
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for role, skills, reason in suggest_requests:
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# Agent-Key aus Role ableiten
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agent_key_suggestion = role.lower().replace(' ', '_').replace('-', '_')
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# Task für Orchestrator um Agent zu erstellen
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new_task = {
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'id': len(tasks) + 1,
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'title': f"Agent-Vorschlag: {role.strip()}",
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'description': f"""Agent {agent_key} schlägt vor, einen neuen Agent zu erstellen:
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**Rolle:** {role.strip()}
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**Fähigkeiten:** {skills.strip()}
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**Begründung:** {reason.strip()}
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Bitte entscheide ob dieser Agent erstellt werden soll.""",
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'assigned_agent': 'Orchestrator',
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'agent_key': 'orchestrator',
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'status': 'pending',
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'created': datetime.now().strftime('%Y-%m-%d %H:%M'),
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'type': 'agent_suggestion',
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'from_agent': agent_key,
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'suggested_agent': agent_key_suggestion,
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'suggested_role': role.strip(),
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'suggested_skills': skills.strip()
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}
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tasks.append(new_task)
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logger.info(f"[AgentCmd] {agent_key} schlägt neuen Agent vor: {role.strip()}")
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# READ_KNOWLEDGE: Agent möchte Wissensdatenbank durchsuchen
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read_kb_requests = re.findall(
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r'@READ_KNOWLEDGE\s*\nTopic:\s*([^@]+)@END',
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response_text,
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re.DOTALL
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)
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# Wenn Agent Wissensdatenbank lesen will, füge relevante Sektion zur Antwort hinzu
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# (wird im Response-Text nicht sichtbar, aber Agent bekommt es als Context)
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if read_kb_requests:
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kb_file = os.path.join(os.path.dirname(__file__), 'diversityball_knowledge.md')
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if os.path.exists(kb_file):
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with open(kb_file, 'r', encoding='utf-8') as f:
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kb_content = f.read()
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for topic in read_kb_requests:
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topic_clean = topic.strip().lower()
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logger.info(f"[AgentCmd] {agent_key} liest Wissensdatenbank: {topic_clean}")
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# Einfache Suche: Gib relevante Abschnitte zurück
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# TODO: Könnte später mit Vektorsuche verbessert werden
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relevant_sections = []
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for line in kb_content.split('\n'):
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if topic_clean in line.lower():
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relevant_sections.append(line)
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if relevant_sections:
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logger.info(f"[AgentCmd] {len(relevant_sections)} relevante Zeilen gefunden")
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def create_new_agent(agent_key, role, skills):
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"""Erstellt dynamisch einen neuen Agenten."""
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agent_dir = os.path.join(AGENTS_BASE_DIR, agent_key)
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if os.path.exists(agent_dir):
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logger.warning(f"[AgentCreate] Agent {agent_key} existiert bereits")
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return False
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os.makedirs(agent_dir, exist_ok=True)
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systemprompt = f"""# {role}
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## Deine Rolle
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{role}
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## Deine Fähigkeiten
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{skills}
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## Aufgaben
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- Erledige Tasks in deinem Fachgebiet
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- Kommuniziere mit anderen Agents wenn nötig
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- Dokumentiere deine Arbeit im work-Verzeichnis
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"""
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with open(os.path.join(agent_dir, 'systemprompt.md'), 'w', encoding='utf-8') as f:
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f.write(systemprompt)
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open(os.path.join(agent_dir, 'personality.md'), 'w').close()
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with open(os.path.join(agent_dir, 'reminders.md'), 'w', encoding='utf-8') as f:
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f.write(f"# Erinnerungen - {agent_key.title()}\n\n## Aktuelle Tasks\n-\n\n## Notizen\n-\n")
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global AGENTS
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AGENTS = load_agents_from_directories()
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logger.info(f"[AgentCreate] Neuer Agent erstellt: {agent_key}")
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return True
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def load_knowledge_base():
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kb_path = os.path.join(os.path.dirname(__file__), 'diversityball_knowledge.md')
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if os.path.exists(kb_path):
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@ -621,7 +857,8 @@ def get_email_preview(msg):
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return part.get_payload(decode=True).decode('utf-8', errors='ignore')[:100]
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else:
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return msg.get_payload(decode=True).decode('utf-8', errors='ignore')[:100]
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except:
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except (AttributeError, TypeError, UnicodeDecodeError) as e:
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logging.debug(f"Email preview extraction failed: {e}")
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return '(Vorschau nicht verfügbar)'
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return ''
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@ -1036,7 +1273,7 @@ def process_beat_tasks():
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while True:
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try:
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pending_tasks = [t for t in tasks if t.get('status') == 'pending' and t.get('type') in ('agent_created', 'manual', 'orchestrated')]
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pending_tasks = [t for t in tasks if t.get('status') == 'pending' and t.get('type') in ('agent_created', 'manual', 'orchestrated', 'agent_delegated')]
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for task in pending_tasks:
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agent_key = task.get('agent_key') or task.get('assigned_agent', '')
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@ -1077,10 +1314,22 @@ Agent-Beschreibungen:
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if assigned not in AGENTS:
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assigned = available_agents[0]
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# Sub-Task mit KOMPLETTEM Kontext
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sub_task = {
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'id': len(tasks) + 1 + len(created_sub_tasks),
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'title': t[:80],
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'description': f"Von Orchestrator zugewiesen: {response[:200]}...",
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'description': f"""**Original-Aufgabe:**
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{task.get('title', '')}
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{task.get('description', '')}
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**Orchestrator-Analyse:**
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{response}
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**Dein spezifischer Teil:**
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{t}
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Arbeite diesen Teil ab und liefere ein vollständiges Ergebnis.""",
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'assigned_agent': AGENTS.get(assigned, {}).get('name', assigned),
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'agent_key': assigned,
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'status': 'pending',
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@ -1133,7 +1382,8 @@ Agent-Beschreibungen:
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except Exception as e:
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logger.error("[TaskBeat] Fehler: %s", str(e))
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time.sleep(30)
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# Beat-Intervall: 10 Sekunden (statt 30)
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time.sleep(10)
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def start_task_beat():
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