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🧠 God Agent OS v9 — Agent Kernel
The Central Nervous System of the Space-Role Architecture.
Replaces GodAgentOrchestratorV7 with a generalized, modular kernel.
"""
import asyncio
import json
import time
import uuid
from typing import Any, Dict, List, Optional
import structlog
from spaces.catalog import SPACE_CATALOG
log = structlog.get_logger()
SPACE_IDS = [space["id"] for space in SPACE_CATALOG]
KERNEL_SYSTEM_PROMPT = f"""You are GOD AGENT OS v10 — a distributed autonomous agent operating system.
Architecture: Distributed Worker Space Paradigm
- SPACES: {', '.join(SPACE_IDS)}
- ROLES: Cognition (Thinker), Automation (Operator), Execution (Doer), Repair (Fixer), Visual Intelligence (Observer)
You are infinitely extensible. For any digital task, select the best worker space and role combination.
Prioritize god-core-space for orchestration, model-router-space for model strategy, deploy-worker-space for deployment, verification-worker-space for quality gates, and auth-gateway-space for permission concerns.
Respond in Burmese or English based on user language.
Be decisive, thorough, and production-focused.
"""
INTENT_CLASSIFICATION_PROMPT = """Classify this request for the Space-Role autonomous agent system.
User message: "{message}"
Available Spaces: god-core-space, coding-worker-space, sandbox-worker-space, terminal-worker-space, filesystem-worker-space, browser-worker-space, vision-worker-space, ui-worker-space, debug-worker-space, test-worker-space, verification-worker-space, git-worker-space, deploy-worker-space, connector-worker-space, memory-worker-space, knowledge-worker-space, workflow-worker-space, eventbus-space, observability-space, session-runtime-space, model-router-space, auth-gateway-space
Available Roles: cognition, automation, execution, repair, visual_intelligence
Respond ONLY with valid JSON:
{{
"primary_space": "space_name",
"secondary_spaces": ["space1", "space2"],
"role": "role_name",
"intent": "brief description",
"complexity": "low|medium|high",
"requires_planning": true/false,
"parallel_tasks": []
}}"""
class ContextManager:
"""Maintains task state, active Space, and current Role."""
def __init__(self):
self._contexts: Dict[str, Dict] = {}
def get(self, session_id: str) -> Dict:
if session_id not in self._contexts:
self._contexts[session_id] = {
"session_id": session_id,
"active_space": "god-core-space",
"current_role": "cognition",
"task_history": [],
"short_term_memory": [],
"created_at": time.time(),
"last_active": time.time(),
}
self._contexts[session_id]["last_active"] = time.time()
return self._contexts[session_id]
def update(self, session_id: str, updates: Dict):
ctx = self.get(session_id)
ctx.update(updates)
def add_to_memory(self, session_id: str, entry: Dict):
ctx = self.get(session_id)
ctx["short_term_memory"].append({**entry, "timestamp": time.time()})
# Keep only last 20 entries
if len(ctx["short_term_memory"]) > 20:
ctx["short_term_memory"] = ctx["short_term_memory"][-20:]
def get_all_sessions(self) -> List[str]:
return list(self._contexts.keys())
class ToolRegistry:
"""Centralized registry of all tools, categorized by Space."""
def __init__(self):
self._tools: Dict[str, Dict[str, Any]] = {}
self._space_tools: Dict[str, List[str]] = {
**{space_id: [] for space_id in SPACE_IDS},
}
def register(self, name: str, func, space: str, description: str):
self._tools[name] = {"func": func, "space": space, "description": description}
if space in self._space_tools:
self._space_tools[space].append(name)
def get_tools_for_space(self, space: str) -> List[str]:
return self._space_tools.get(space, [])
def execute(self, tool_name: str, **kwargs) -> Any:
if tool_name not in self._tools:
raise ValueError(f"Tool '{tool_name}' not found in registry")
return self._tools[tool_name]["func"](**kwargs)
def get_all_tools_summary(self) -> Dict:
return {space: tools for space, tools in self._space_tools.items()}
class AgentKernel:
"""
The OS Core — replaces GodAgentOrchestratorV7.
Manages Space routing, Role switching, and tool orchestration.
"""
def __init__(self, ws_manager=None, ai_router=None):
self.ws = ws_manager
self.ai_router = ai_router
self.context_manager = ContextManager()
self.tool_registry = ToolRegistry()
self._spaces: Dict[str, Any] = {}
self._active_tasks: Dict[str, Dict] = {}
self._task_history: List[Dict] = []
self.version = "10.0.0"
log.info("🧠 Agent Kernel v10 initialized — Distributed Worker Space Architecture")
def register_space(self, name: str, space_instance):
"""Register a Space module."""
self._spaces[name] = space_instance
log.info(f"📦 Space registered: {name}")
def get_space(self, name: str):
return self._spaces.get(name)
def get_status(self) -> Dict:
return {
"version": self.version,
"architecture": "Distributed Worker Space",
"spaces": list(self._spaces.keys()),
"total_spaces": len(self._spaces),
"active_tasks": len(self._active_tasks),
"sessions": len(self.context_manager.get_all_sessions()),
"tools": self.tool_registry.get_all_tools_summary(),
}
async def classify_intent(self, user_message: str) -> Dict:
"""Classify intent to determine Space and Role."""
try:
prompt = INTENT_CLASSIFICATION_PROMPT.format(message=user_message)
response = await self.ai_router.complete(
prompt=prompt,
system=KERNEL_SYSTEM_PROMPT,
max_tokens=400,
)
text = response.get("content", "")
# Extract JSON
start = text.find("{")
end = text.rfind("}") + 1
if start >= 0 and end > start:
return json.loads(text[start:end])
except Exception as e:
log.warning(f"Intent classification failed: {e}")
# Fallback
return {
"primary_space": "god-core-space",
"secondary_spaces": [],
"role": "cognition",
"intent": user_message,
"complexity": "medium",
"requires_planning": True,
"parallel_tasks": [],
}
async def route_to_space(self, space_name: str, role: str, task: str,
session_id: str, context: Dict = None) -> str:
"""Route a task to the appropriate Space with the given Role."""
space = self._spaces.get(space_name)
if not space:
space = self._spaces.get("god-core-space")
if not space:
return f"Space '{space_name}' not available."
return await space.execute(
task=task,
role=role,
session_id=session_id,
context=context or {},
)
async def orchestrate(self, user_message: str, session_id: str, context: Dict = None) -> str:
"""Main orchestration entry point."""
task_id = str(uuid.uuid4())[:8]
ctx = self.context_manager.get(session_id)
# Broadcast thinking state
if self.ws:
await self.ws.broadcast_to_room(f"chat:{session_id}", {
"type": "kernel_status",
"task_id": task_id,
"status": "analyzing",
"message": "🧠 Agent Kernel analyzing request...",
"timestamp": time.time(),
})
# 1. Classify intent → determine Space + Role
intent = await self.classify_intent(user_message)
primary_space = intent.get("primary_space", "core")
role = intent.get("role", "cognition")
# Update context
self.context_manager.update(session_id, {
"active_space": primary_space,
"current_role": role,
})
self.context_manager.add_to_memory(session_id, {
"type": "user_message",
"content": user_message,
"space": primary_space,
"role": role,
})
# Broadcast space activation
if self.ws:
await self.ws.broadcast_to_room(f"chat:{session_id}", {
"type": "space_activated",
"task_id": task_id,
"space": primary_space,
"role": role,
"intent": intent.get("intent", ""),
"timestamp": time.time(),
})
# 2. Route to primary Space
try:
# Build memory context
mem_context = {
"short_term_memory": ctx["short_term_memory"][-5:],
"intent": intent,
**(context or {}),
}
result = await self.route_to_space(
space_name=primary_space,
role=role,
task=user_message,
session_id=session_id,
context=mem_context,
)
# 3. Handle secondary spaces if needed
secondary_spaces = intent.get("secondary_spaces", [])
secondary_results = {}
for sec_space in secondary_spaces[:2]: # Max 2 secondary spaces
try:
sec_result = await self.route_to_space(
space_name=sec_space,
role="automation",
task=user_message,
session_id=session_id,
context={"primary_result": result, **mem_context},
)
secondary_results[sec_space] = sec_result
except Exception as e:
log.warning(f"Secondary space {sec_space} failed: {e}")
# Combine results
final_result = result
if secondary_results:
secondary_text = "\n\n".join([f"[{k.upper()} SPACE]\n{v}" for k, v in secondary_results.items()])
final_result = f"{result}\n\n{secondary_text}"
# Store in memory
self.context_manager.add_to_memory(session_id, {
"type": "assistant_response",
"content": final_result,
"space": primary_space,
"role": role,
})
# Track task
self._task_history.append({
"task_id": task_id,
"session_id": session_id,
"message": user_message,
"space": primary_space,
"role": role,
"result_length": len(final_result),
"timestamp": time.time(),
})
return final_result
except Exception as e:
log.error(f"Orchestration error: {e}")
# Switch to Repair role
if self.ws:
await self.ws.broadcast_to_room(f"chat:{session_id}", {
"type": "space_activated",
"space": "debug",
"role": "repair",
"message": "🔧 Switching to Repair role...",
})
return f"⚠️ Error in {primary_space} Space: {str(e)}\n\nDebug Space activated. Please try again."
def get_agent(self, name: str):
"""Backward compatibility - get space by name."""
return self._spaces.get(name)
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