This document explains how mindXagent.py performs reasoning, decision-making, and self-improvement orchestration. The reasoning process is multi-layered, combining rule-based logic, pattern recognition, memory analysis, and strategic planning to continuously improve the mindX system.
┌─────────────────────────────────────────────────────────────┐
│ MindXAgent Reasoning Pipeline │
└─────────────────────────────────────────────────────────────┘
Input (Goal/Context)
↓
- Memory Feedback Retrieval
├──→ Query MemoryAgent for relevant memories
├──→ Load improvement history
└──→ Analyze data folder state
↓
- System State Analysis
├──→ Collect performance metrics
├──→ Gather resource metrics
└──→ Assess agent knowledge base
↓
- Opportunity Identification
├──→ Pattern recognition from state
├──→ Gap analysis (expected vs actual)
└──→ Historical pattern matching
↓
- Agent Selection
├──→ Task type matching
├──→ Capability matching
└──→ Agent availability check
↓
- Strategic Planning
├──→ Blueprint generation
├──→ Campaign creation
└──→ Goal planning
↓
- Execution & Monitoring
├──→ Orchestrate agents
├──→ Monitor progress
└──→ Collect results
↓
- Result Analysis
├──→ Compare actual vs expected
├──→ Calculate variance
└──→ Identify improvement opportunities
↓
- Learning & Adaptation
├──→ Update knowledge base
├──→ Store in memory
└──→ Refine reasoning patterns
Reasoning is the process of thinking about the system, identifying problems, and deciding how to fix them. MindXAgent reasons by:
# 1. Look at the system
system_state = await mindx_agent._analyze_system_state()
Returns: {"agent_count": 15, "error_rate": 0.05, "cpu_usage": 75%}
2. Find problems
opportunities = await mindx_agent._identify_improvement_opportunities(system_state)
Returns: [{"goal": "Reduce error rate", "priority": 1}]
3. Decide what to do
prioritized = await mindx_agent._prioritize_improvements(opportunities)
Returns: Sorted list with highest priority first
4. Take action
result = await mindx_agent.orchestrate_self_improvement(prioritized[0]["goal"])
Returns: {"success": True, "improvements_made": [...]}
# MindXAgent notices high error rate
if error_rate > 0.1: # 10% error rate
# It reasons: "This is too high, I should fix it"
goal = "Reduce error rate in system operations"
# It selects agents that can help
agents = ["strategic_evolution_agent", "bdi_agent"]
# It orchestrates improvement
result = await mindx_agent.orchestrate_self_improvement(goal)
# It learns from the result
if result.success:
# Remember: "This approach worked"
# Update knowledge base
MindXAgent's reasoning involves several interconnected components:
Purpose: Use past experiences to inform current decisions
async def get_memory_feedback(self, context: str) -> MemoryContext:
"""
Retrieves relevant memories and historical data to inform reasoning.
Reasoning Process:
1. Query MemoryAgent for memories related to context
2. Load improvement history to see what worked before
3. Analyze data folder state for system health
4. Extract lessons learned from past improvements
"""
# Get memories from MemoryAgent
memories = []
if self.memory_agent:
# Search for relevant memories
agent_memories_dir = self.memory_agent.get_agent_data_directory(self.agent_id)
# In full implementation: semantic search by context
# Load improvement history
improvement_history = []
if self.improvement_history_file.exists():
with open(self.improvement_history_file, 'r') as f:
improvement_history = json.load(f)
# Analyze patterns from history
# Example: "Last 3 improvements failed, need different approach"
return MemoryContext(
memories=memories,
improvement_history=improvement_history,
lessons_learned=self._extract_lessons(improvement_history)
)
Reasoning Logic:
Purpose: Understand current system condition
async def _analyze_system_state(self) -> Dict[str, Any]:
"""
Analyzes current system state by collecting metrics from multiple sources.
Reasoning Process:
1. Count agents in knowledge base
2. Get performance metrics (error rates, latency)
3. Get resource metrics (CPU, memory usage)
4. Assess improvement history trends
"""
state = {
"timestamp": time.time(),
"agent_count": len(self.agent_knowledge),
"improvement_history_count": len(self.improvement_history)
}
# Get performance metrics
if self.performance_monitor:
metrics = self.performance_monitor.get_metrics()
state["performance_metrics"] = metrics
# Example: {"error_rate": 0.15, "avg_latency": 250}
# Get resource metrics
if self.resource_monitor:
resources = await self.resource_monitor.get_current_resources()
state["resource_metrics"] = resources
# Example: {"cpu_percent": 85, "memory_percent": 70}
return state
Reasoning Patterns:
Purpose: Find specific problems that need fixing
async def _identify_improvement_opportunities(
self,
system_state: Dict[str, Any]
) -> List[Dict[str, Any]]:
"""
Identifies improvement opportunities through pattern recognition.
Reasoning Process:
1. Check knowledge base completeness
2. Analyze performance metrics for issues
3. Check resource constraints
4. Review improvement history for patterns
"""
opportunities = []
# Rule 1: Knowledge base too small
if system_state.get("agent_count", 0) < 10:
opportunities.append({
"goal": "Expand agent knowledge base - discover more agents",
"priority": 1, # High priority
"reason": "Low agent count in knowledge base",
"impact": "high" # High impact on system capability
})
# Rule 2: High error rate
perf_metrics = system_state.get("performance_metrics", {})
if perf_metrics.get("error_rate", 0) > 0.1: # 10% threshold
opportunities.append({
"goal": "Reduce error rate in system operations",
"priority": 2, # Medium-high priority
"reason": f"Error rate is {perf_metrics.get('error_rate', 0):.2%}",
"impact": "high" # Errors affect reliability
})
# Rule 3: Resource constraints
resource_metrics = system_state.get("resource_metrics", {})
if resource_metrics.get("cpu_percent", 0) > 80:
opportunities.append({
"goal": "Optimize CPU usage - system under high load",
"priority": 2,
"reason": f"CPU usage at {resource_metrics.get('cpu_percent', 0):.1f}%",
"impact": "medium"
})
# Rule 4: Pattern from history
if len(self.improvement_history) > 0:
recent_improvements = self.improvement_history[-5:]
failed_count = sum(1 for imp in recent_improvements
if not imp.get("success", False))
if failed_count > 2: # More than 40% failure rate
opportunities.append({
"goal": "Improve improvement success rate",
"priority": 1, # High priority - meta-improvement
"reason": f"{failed_count} of last 5 improvements failed",
"impact": "high" # Affects all future improvements
})
return opportunities
Reasoning Logic:
Purpose: Choose the right agents for the task
async def select_agents_for_task(self, task: Dict[str, Any]) -> List[str]:
"""
Intelligently selects agents based on task requirements.
Reasoning Process:
1. Check task type (self_improvement, monitoring, coordination)
2. Match task type to agent categories
3. Check agent capabilities against task keywords
4. Verify agent availability
"""
selected_agents = []
task_type = task.get("type", "")
task_goal = task.get("goal", "")
# Rule-based selection by task type
if task_type == "self_improvement":
# For self-improvement, need strategic planning agents
if "strategic_evolution_agent" in self.agent_knowledge:
selected_agents.append("strategic_evolution_agent")
if "bdi_agent" in self.agent_knowledge:
selected_agents.append("bdi_agent")
if "mastermind_agent" in self.agent_knowledge:
selected_agents.append("mastermind_agent")
# Capability-based selection
for agent_id, capabilities in self.agent_capabilities.items():
if agent_id not in selected_agents:
# Extract keywords from task goal
task_keywords = task_goal.lower().split()
agent_capabilities_str = " ".join(
capabilities.primary_capabilities
).lower()
# Match keywords to capabilities
if any(keyword in agent_capabilities_str
for keyword in task_keywords if len(keyword) > 3):
selected_agents.append(agent_id)
return selected_agents
Reasoning Patterns:
Purpose: Learn from actual outcomes
async def analyze_actual_results(self, task_id: str) -> ResultAnalysis:
"""
Analyzes actual results vs expected outcomes.
Reasoning Process:
1. Get expected outcomes from task definition
2. Collect actual results from monitoring
3. Calculate variance (difference)
4. Identify what worked and what didn't
5. Generate improvement opportunities
"""
expected_outcomes = {} # From task definition
actual_results = {}
# Get actual performance
if self.performance_monitor:
perf_metrics = await self.performance_monitor.get_metrics()
actual_results["performance"] = perf_metrics
# Calculate variance
variance = {}
for key in set(list(expected_outcomes.keys()) + list(actual_results.keys())):
expected = expected_outcomes.get(key, 0)
actual = actual_results.get(key, 0)
if isinstance(expected, (int, float)) and isinstance(actual, (int, float)):
variance[key] = actual - expected
# Identify improvement opportunities from variance
improvement_opportunities = []
for key, var in variance.items():
if abs(var) > 0.1: # Significant variance threshold
if var > 0:
# Actual exceeded expectations (good!)
improvement_opportunities.append(
f"{key} exceeded expectations by {var}"
)
else:
# Actual fell short (needs improvement)
improvement_opportunities.append(
f"{key} fell short by {abs(var)}"
)
return ResultAnalysis(
task_id=task_id,
expected_outcomes=expected_outcomes,
actual_results=actual_results,
variance=variance,
improvement_opportunities=improvement_opportunities
)
Reasoning Logic:
MindXAgent can reason about itself and the system it's part of:
async def _check_identity_crisis(self) -> bool:
"""
Advanced reasoning: Self-awareness check.
MindXAgent reasons about its own state:
- "Do I know who I am?"
- "Do I have enough knowledge?"
- "Am I functioning correctly?"
"""
# Check knowledge base completeness
if not self.agent_knowledge or len(self.agent_knowledge) < 3:
# Reasoning: "I don't know enough agents - I'm in crisis"
logger.warning("Identity crisis detected - knowledge base is minimal")
await self._load_identity_from_docs() # Self-repair
return True
# Check for key agents
key_agents = ["coordinator_agent", "mastermind_agent",
"strategic_evolution_agent"]
missing_agents = [agent for agent in key_agents
if agent not in self.agent_knowledge]
if missing_agents:
# Reasoning: "I'm missing critical agents - I need to rediscover them"
logger.warning(f"Identity crisis - missing key agents: {missing_agents}")
await self._load_identity_from_docs()
return True
return False
Advanced Reasoning Aspects:
Advanced pattern recognition from improvement history:
async def _identify_improvement_opportunities(
self,
system_state: Dict[str, Any]
) -> List[Dict[str, Any]]:
"""
Advanced reasoning: Pattern recognition from historical data.
"""
opportunities = []
# Advanced: Analyze improvement history patterns
if len(self.improvement_history) > 10:
# Extract patterns
recent_improvements = self.improvement_history[-10:]
# Pattern 1: Failure clustering
failed_improvements = [imp for imp in recent_improvements
if not imp.get("success", False)]
if len(failed_improvements) > 5:
# Reasoning: "More than 50% of recent improvements failed"
# "This suggests a systemic issue with the improvement process"
opportunities.append({
"goal": "Fix improvement process - high failure rate detected",
"priority": 1,
"reason": "Pattern detected: 50%+ failure rate in recent improvements",
"pattern_type": "failure_clustering",
"confidence": 0.85
})
# Pattern 2: Agent performance correlation
agent_success_rates = {}
for imp in recent_improvements:
agents_used = imp.get("result", {}).get("agents_used", [])
success = imp.get("success", False)
for agent in agents_used:
if agent not in agent_success_rates:
agent_success_rates[agent] = {"success": 0, "total": 0}
agent_success_rates[agent]["total"] += 1
if success:
agent_success_rates[agent]["success"] += 1
# Find underperforming agents
for agent, stats in agent_success_rates.items():
success_rate = stats["success"] / stats["total"]
if success_rate < 0.5 and stats["total"] >= 3:
# Reasoning: "This agent consistently fails - need to investigate"
opportunities.append({
"goal": f"Investigate {agent} - low success rate ({success_rate:.1%})",
"priority": 2,
"reason": f"Pattern: {agent} has {success_rate:.1%} success rate",
"pattern_type": "agent_performance",
"confidence": 0.75
})
# Pattern 3: Temporal trends
# Check if error rate is increasing over time
if len(self.improvement_history) > 20:
recent_errors = sum(1 for imp in self.improvement_history[-10:]
if not imp.get("success", False))
older_errors = sum(1 for imp in self.improvement_history[-20:-10]
if not imp.get("success", False))
if recent_errors > older_errors * 1.5:
# Reasoning: "Error rate is increasing - system is degrading"
opportunities.append({
"goal": "Address system degradation - error rate increasing",
"priority": 1,
"reason": f"Trend: {recent_errors} errors in last 10 vs {older_errors} in previous 10",
"pattern_type": "temporal_trend",
"confidence": 0.80
})
return opportunities
Advanced Reasoning Features:
Advanced strategic planning through Blueprint Agent:
async def orchestrate_self_improvement(
self,
improvement_goal: str
) -> ImprovementResult:
"""
Advanced reasoning: Multi-agent strategic orchestration.
Reasoning Process:
1. Get memory context (learn from past)
2. Generate strategic blueprint (plan ahead)
3. Select appropriate agents (match capabilities)
4. Create improvement campaign (coordinate actions)
5. Execute with monitoring (track progress)
6. Analyze results (learn for next time)
"""
# Step 1: Memory-based reasoning
memory_context = await self.get_memory_feedback(improvement_goal)
# Reasoning: "What have I learned from similar goals before?"
# Step 2: Strategic blueprint generation
if self.blueprint_agent:
blueprint = await self.blueprint_agent.generate_next_evolution_blueprint()
# Reasoning: "What's the best strategic approach for this goal?"
# Blueprint contains: phases, milestones, dependencies, risks
# Step 3: Agent selection reasoning
selected_agents = await self.select_agents_for_task({
"type": "self_improvement",
"goal": improvement_goal,
"context": memory_context
})
# Reasoning: "Which agents have the capabilities needed?"
# Step 4: Campaign creation
if self.strategic_evolution_agent:
campaign = await self.strategic_evolution_agent.create_improvement_campaign(
goal_description=improvement_goal,
priority="high"
)
# Reasoning: "How should I structure this improvement?"
# Campaign contains: steps, timeline, resource requirements
# Step 5: BDI goal planning
if self.bdi_agent:
bdi_result = await self.bdi_agent.add_goal(
goal_description=improvement_goal,
priority=1
)
# Reasoning: "What are the sub-goals and dependencies?"
# BDI creates: plan, actions, beliefs, desires
# Step 6: Result analysis
result_analysis = await self.analyze_actual_results(task_id)
# Reasoning: "Did this work? What can I learn?"
# Step 7: Learning and adaptation
if result_analysis.improvement_opportunities:
# Reasoning: "I found new opportunities from this improvement"
# Store for next reasoning cycle
self.improvement_opportunities.extend(
result_analysis.improvement_opportunities
)
return ImprovementResult(...)
Advanced Reasoning Features:
Advanced continuous reasoning in autonomous mode:
async def _autonomous_improvement_loop(self):
"""
Advanced reasoning: Continuous autonomous improvement.
This implements Gödel machine reasoning:
- Continuously reasons about system state
- Identifies improvement opportunities
- Executes improvements
- Learns from results
- Improves the improvement process itself
"""
cycle_count = 0
while self.running and self.autonomous_mode:
cycle_count += 1
# Step 1: Self-awareness check
if await self._check_identity_crisis():
# Reasoning: "Am I still myself? Do I know who I am?"
logger.info("Identity restored from INDEX.md")
# Step 2: System state analysis
self._log_thinking("analyzing_system_state",
"Analyzing current system state for improvement opportunities")
system_state = await self._analyze_system_state()
# Reasoning: "What's the current state of the system?"
# Step 3: Opportunity identification
self._log_thinking("identifying_opportunities",
"Identifying improvement opportunities from system state")
improvement_opportunities = await self._identify_improvement_opportunities(
system_state
)
# Reasoning: "What problems do I see? What can be improved?"
if improvement_opportunities:
# Step 4: Prioritization reasoning
self._log_thinking("prioritizing",
f"Prioritizing {len(improvement_opportunities)} opportunities")
prioritized = await self._prioritize_improvements(improvement_opportunities)
# Reasoning: "Which improvement should I do first? Why?"
# Step 5: Action selection reasoning
if prioritized:
choices = [
{
"goal": p["goal"],
"priority": p.get("priority", "medium"),
"estimated_impact": p.get("impact", "unknown")
}
for p in prioritized[:5]
]
self._log_action_choices("improvement_selection", choices)
# Reasoning: "I have these options. I choose this one because..."
# Step 6: Execution reasoning
if prioritized:
top_priority = prioritized[0]
self._log_thinking("executing_improvement",
f"Executing improvement: {top_priority['goal']}")
# Strategic planning
if self.blueprint_agent:
blueprint = await self.blueprint_agent.generate_next_evolution_blueprint()
# Reasoning: "What's the best strategy for this improvement?"
# Execute
result = await self.orchestrate_self_improvement(top_priority['goal'])
# Reasoning: "I'm executing this improvement using these agents..."
# Learning
if result.success:
# Reasoning: "This worked! I should remember this approach"
logger.info(f"Improvement cycle {cycle_count} completed successfully")
else:
# Reasoning: "This didn't work. I should try a different approach next time"
logger.warning(f"Improvement cycle {cycle_count} had issues")
# Step 7: Wait and reflect
await asyncio.sleep(300) # 5 minutes between cycles
# Reasoning: "I'll check again in 5 minutes to see if anything changed"
Advanced Reasoning Features:
Advanced reasoning transparency:
def _log_thinking(self, step: str, thought: str,
metadata: Optional[Dict[str, Any]] = None):
"""
Logs reasoning steps for transparency and debugging.
This enables:
- Understanding how MindXAgent reasons
- Debugging reasoning failures
- Improving reasoning patterns
- UI display of thinking process
"""
thinking_entry = {
"timestamp": time.time(),
"step": step, # e.g., "analyzing_system_state"
"thought": thought, # e.g., "Analyzing current system state..."
"metadata": metadata or {} # Additional context
}
self.thinking_process.append(thinking_entry)
# Keep only last N entries (configurable)
if len(self.thinking_process) > self.max_thinking_history:
self.thinking_process = self.thinking_process[-self.max_thinking_history:]
logger.debug(f"[THINKING] {step}: {thought}")
def _log_action_choices(self, context: str, choices: List[Dict[str, Any]]):
"""
Logs action choices with reasoning.
Shows:
- What options were considered
- Which option was chosen
- Why it was chosen (priority, impact)
"""
action_entry = {
"timestamp": time.time(),
"context": context, # e.g., "improvement_selection"
"choices": choices, # All options considered
"selected": choices[0] if choices else None # Chosen option
}
self.action_choices.append(action_entry)
logger.info(f"[ACTION CHOICE] {context}: {len(choices)} options, "
f"selected: {choices[0].get('goal', 'N/A') if choices else 'None'}")
Advanced Reasoning Transparency:
Advanced reasoning combines multiple information sources:
async def receive_startup_information(self, startup_info: Dict[str, Any]):
"""
Advanced reasoning: Integrate information from multiple sources.
Combines:
- Startup agent observations
- Terminal log analysis
- Ollama connection status
- Model availability
- Error patterns
"""
self._log_thinking("receiving_startup_info",
"Received startup information from startup_agent",
startup_info)
# Reasoning about model selection
if startup_info.get("ollama_connected") and self.settings.get("autonomous_mode_enabled"):
models = startup_info.get("ollama_models", [])
# Multi-factor reasoning for model selection
strategy = self.settings.get("model_selection_strategy", "best_for_task")
if strategy == "best_for_task":
# Reasoning: "I need the best model for reasoning tasks"
from api.ollama.ollama_model_capability_tool import OllamaModelCapabilityTool
capability_tool = OllamaModelCapabilityTool(config=self.config)
best_model = capability_tool.get_best_model_for_task("reasoning")
# Reasoning: "Based on capabilities, this model is best for reasoning"
self.llm_model = best_model
elif strategy == "user_preference":
# Reasoning: "User has a preference, I should respect it"
# Load user preference from config
# Reasoning: "User prefers this model, I'll use it"
# Reasoning about startup issues
if startup_info.get("terminal_log"):
terminal_log = startup_info["terminal_log"]
errors_count = terminal_log.get("errors_count", 0)
warnings_count = terminal_log.get("warnings_count", 0)
# Reasoning: "I see errors and warnings in startup"
# "This suggests system issues that need improvement"
if errors_count > 0 or warnings_count > 0:
self._log_thinking("startup_issues_detected",
f"Startup issues: {errors_count} errors, {warnings_count} warnings")
# Create improvement opportunity
if errors_count > 0:
self.improvement_opportunities.append({
"goal": "Fix startup errors",
"priority": "high",
"source": "startup_agent",
"details": terminal_log.get("sample_errors", [])[:3],
"reasoning": "Errors detected during startup indicate system problems"
})
Advanced Reasoning Features:
if metric > threshold:
# Reasoning: "This metric exceeds acceptable threshold"
# Action: Create improvement opportunity
# Analyze historical patterns
if pattern_detected:
# Reasoning: "I've seen this pattern before"
# Action: Apply learned strategy
# Select agents based on task
agents = select_agents_for_task(task)
Reasoning: "These agents have the right capabilities"
Action: Orchestrate them together
# Execute improvement
result = execute_improvement(goal)
Analyze results
analysis = analyze_results(result)
Learn from results
update_knowledge(analysis)
Reasoning: "This worked/didn't work, I'll remember"
# Improve the improvement process
if improvement_process_needs_improvement:
# Reasoning: "My improvement process isn't working well"
# Action: Improve the improvement process itself
improve_improvement_process()
┌─────────────────────────────────────────────────────────────┐
│ Advanced Reasoning Flow │
└─────────────────────────────────────────────────────────────┘
Input: Improvement Goal
↓
┌─────────────────────────────────────┐
│ 1. Memory-Based Reasoning │
│ - Query relevant memories │
│ - Load improvement history │
│ - Extract lessons learned │
│ Reasoning: "What worked before?"│
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ 2. System State Analysis │
│ - Collect performance metrics │
│ - Gather resource metrics │
│ - Assess agent knowledge │
│ Reasoning: "What's the state?" │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ 3. Pattern Recognition │
│ - Identify improvement patterns │
│ - Detect failure patterns │
│ - Recognize trends │
│ Reasoning: "What patterns do I │
│ see?" │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ 4. Opportunity Identification │
│ - Apply threshold rules │
│ - Match patterns to opportunities│
│ - Assign priorities │
│ Reasoning: "What should I fix?" │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ 5. Strategic Planning │
│ - Generate blueprint │
│ - Create campaign │
│ - Plan with BDI │
│ Reasoning: "How should I fix it?" │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ 6. Agent Selection │
│ - Match task to agent types │
│ - Match capabilities │
│ - Verify availability │
│ Reasoning: "Who can help?" │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ 7. Execution │
│ - Orchestrate agents │
│ - Monitor progress │
│ - Collect results │
│ Reasoning: "Executing plan..." │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ 8. Result Analysis │
│ - Compare actual vs expected │
│ - Calculate variance │
│ - Identify new opportunities │
│ Reasoning: "Did it work?" │
└─────────────────────────────────────┘
↓
┌─────────────────────────────────────┐
│ 9. Learning & Adaptation │
│ - Update knowledge base │
│ - Store in memory │
│ - Refine reasoning patterns │
│ Reasoning: "What did I learn?" │
└─────────────────────────────────────┘
↓
Output: Improved System + Learned Patterns
# Simple reasoning: If error rate is high, fix it
error_rate = 0.15 # 15%
if error_rate > 0.1: # Threshold: 10%
goal = "Reduce error rate"
await mindx_agent.orchestrate_self_improvement(goal)
# Medium reasoning: Analyze patterns from history
recent_improvements = improvement_history[-10:]
failed_count = sum(1 for imp in recent_improvements
if not imp.get("success", False))
if failed_count > 5: # More than 50% failed
# Reasoning: "Pattern detected - improvement process is broken"
goal = "Fix improvement process - high failure rate"
await mindx_agent.orchestrate_self_improvement(goal)
# Advanced reasoning: Combine multiple factors
async def advanced_reasoning_example():
# 1. Get memory context
memory_context = await mindx_agent.get_memory_feedback("system improvement")
# 2. Analyze system state
system_state = await mindx_agent._analyze_system_state()
# 3. Identify opportunities with pattern recognition
opportunities = await mindx_agent._identify_improvement_opportunities(
system_state
)
# 4. Prioritize with multi-factor analysis
prioritized = await mindx_agent._prioritize_improvements(opportunities)
# 5. Select agents based on capabilities
selected_agents = await mindx_agent.select_agents_for_task({
"type": "self_improvement",
"goal": prioritized[0]["goal"],
"context": memory_context
})
# 6. Generate strategic blueprint
blueprint = await blueprint_agent.generate_next_evolution_blueprint()
# 7. Execute with monitoring
result = await mindx_agent.orchestrate_self_improvement(
prioritized[0]["goal"]
)
# 8. Analyze results and learn
analysis = await mindx_agent.analyze_actual_results(result.task_id)
# 9. Update knowledge base
if analysis.improvement_opportunities:
# Reasoning: "I found new opportunities from this improvement"
mindx_agent.improvement_opportunities.extend(
analysis.improvement_opportunities
)
MindXAgent's reasoning is a sophisticated multi-layered process that combines:
The reasoning process is transparent (via thinking logs), adaptive (learns from results), and recursive (improves itself), making MindXAgent a true Gödel machine that can reason about and improve the system it's part of.