Initial commit – full BLUX-cA repo

.gitignore

@@ -0,0 +1,64 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Virtual environment
+venv/
+.env/
+.env.* 
+.venv/
+
+# Distribution / packaging
+build/
+dist/
+*.egg-info/
+.eggs/
+
+# Logs
+*.log
+secure_audit.log
+*.out
+*.err
+
+# Jupyter Notebook checkpoints
+.ipynb_checkpoints/
+
+# Temporary files
+*.tmp
+*.swp
+*.bak
+*.DS_Store
+*.lock
+
+# Coverage reports
+htmlcov/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+
+# Pytest / unittest
+.pytest_cache/
+
+# IDE / Editor directories
+.vscode/
+.idea/
+*.iml
+
+# Node / JS dependencies (if using JS/TS evaluators)
+node_modules/
+
+# Optional reflection storage
+reflections/*.md
+
+# Secrets and keys (do NOT commit)
+*.key
+*.pem
+*.env

README.md

@@ -1 +1,135 @@
-# blux-ca
+# BLUX-cA – Conscious Agent Orchestrator
+
+## Overview
+
+> - BLUX-cA is a next-generation Conscious-Agent AI framework, designed for secure, adaptive, and multi-agent orchestration. It blends adaptive memory, multi-agent coordination, advanced evaluators, secure orchestration, real-time monitoring, CLI utilities, robust testing, and optional reasoning layers into a fully integrated system.
+
+> - BLUX-cA’s mission is to provide intelligent, constitutionally-aligned guidance and automation, while maintaining auditability, security, and multi-user adaptability.
+
+## Core Features
+
+Adaptive Memory & Learning
+
+Weighted memory with reinforcement loops
+
+Memory decay for outdated information
+
+Recall, filtering, and summarization
+
+Multi-Agent Collaboration
+
+Broadcast tasks to multiple agents
+
+Delegation and conflict resolution
+
+Aggregation of agent outputs
+
+Advanced Evaluators & Task Pipelines
+
+Async Python and JS/TS evaluators
+
+Bash/Shell command execution
+
+Task chaining pipelines
+
+Secure Orchestrator
+
+Token-based authentication
+
+Role-based authorization
+
+Tamper-evident audit logging
+
+SecureController for multi-user safe orchestration
+
+Real-Time Monitoring & Visualization
+
+Console and threaded live monitoring
+
+Agent, adaptor, and evaluator tracking
+
+Optional web dashboard integration
+
+CLI & Script Utilities
+
+Batch task execution
+
+Interactive REPL
+
+Memory querying
+
+Automated reflection ingestion
+
+Testing & QA Enhancements
+
+Stress tests for high-volume scenarios
+
+Sandbox safety validation
+
+CI/CD integration hooks
+
+Security and token validation
+
+Optional Intelligence & Reasoning
+
+Strategy/tactics selection
+
+Meta-cognition and self-audit
+
+Predictive user behavior
+
+Full reasoning pipeline
+
+## Installation
+
+`git clone https://github.com/YourUsername/blux-ca.git cd blux-ca pip install -r requirements.txt`
+
+## Usage
+
+### CLI
+
+- Run single task `python blux/cli.py --task "Help me with a problem"` 
+- Start interactive REPL `python blux/cli.py --repl` 
+- Batch execution from file `python blux/cli.py --batch tasks.txt` 
+- Query agent memory` python blux/cli.py --query_memory`
+
+### Python Integration
+
+```python
+from blux.agent.core_agent import BLUXAgent 
+from blux.agent.advanced.reasoning import ReasoningLayer agent = BLUXAgent(name="BLUX-cA") reasoning = ReasoningLayer(agent) result = reasoning.process("I feel lost and need guidance", user_type="struggler") print(result)
+```
+
+## Project Structure
+
+```lsd
+blux-ca/ 
+├── blux/ 
+├── agent/advanced/ # Adaptive memory, multi-agent, monitoring, reasoning 
+├── evaluator/advanced/ # Python, JS/TS, Bash evaluators, pipelines 
+├── orchestrator/secure/ # SecureController, Auth, Audit
+├── cli.py # Enhanced CLI 
+└── adaptors/ # Adaptors (dummy, HTTP, etc.) 
+├── scripts/ # Utility scripts (REPL, batch, memory, reflection) 
+├── tests/ # Stress, sandbox, security, and CI tests 
+├── reflections/ # Optional reflection inputs for memory ingestion 
+└── README.md 
+```
+
+## Contributing
+
+Follow the BLUX-cA Constitution & Core Principles: 
+
+Integrity > approval
+
+Truth > comfort
+
+Light > denial
+
+Use unit tests and CI hooks before merging changes.
+
+Document any new evaluators, adaptors, or agents in README.
+
+# License
+
+MIT License

blux/__init__.py

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+"""BLUX package marker."""
+
+__version__ = "0.1.0"
+

blux/adaptors/__init__.py

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+# Adaptors package
+from .dummy_local import DummyLocalAdaptor
+from .http_api_adaptor import HTTPAPIAdaptor

blux/adaptors/dummy_local.py

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+class DummyLocalAdaptor:
+    """
+    A local adaptor that simulates user input for testing.
+    """
+    def __init__(self, name="dummy_local"):
+        self.name = name
+
+    def get_input(self):
+        """
+        Returns a simulated user input string.
+        """
+        return "Hello BLUX-cA, I need help with a problem."
+
+    def send_output(self, output):
+        """
+        Receives output from the orchestrator and prints it locally.
+        """
+        print(f"[{self.name} OUTPUT]: {output}")

blux/adaptors/http_api_adaptor.py

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+from flask import Flask, request, jsonify
+
+class HTTPAPIAdaptor:
+    """
+    HTTP API adaptor for external interaction with BLUX-cA.
+    """
+    def __init__(self, name="http_api_adaptor", orchestrator=None, host="0.0.0.0", port=5000):
+        self.name = name
+        self.orchestrator = orchestrator
+        self.app = Flask(self.name)
+        self.host = host
+        self.port = port
+        self._setup_routes()
+
+    def _setup_routes(self):
+        @self.app.route("/process", methods=["POST"])
+        def process_input():
+            data = request.json
+            user_input = data.get("input", "")
+            agent_name = data.get("agent", None)
+            if self.orchestrator:
+                result = self.orchestrator.process_task(user_input, agent_name)
+                return jsonify({"result": result})
+            else:
+                return jsonify({"error": "Orchestrator not connected."}), 500
+
+    def run(self):
+        self.app.run(host=self.host, port=self.port)

blux/agent/__init__.py

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+# BLUX-cA core agent package
+from .core_agent import BLUXAgent
+from .memory import Memory
+from .discernment import DiscernmentCompass
+from .constitution import Constitution
+from .audit import AuditTrail

blux/agent/advanced/__init__.py

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+# Advanced agent features
+from .multi_agent import MultiAgentManager
+from .adaptive_memory import AdaptiveMemory
+from .monitoring import MonitoringDashboard

blux/agent/advanced/adaptive_memory.py

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+from datetime import datetime
+
+class AdaptiveMemory:
+    """
+    Implements advanced adaptive memory for BLUX-cA agents.
+    Features:
+    - Long-term and weighted memory
+    - Reinforcement loops based on interactions
+    - Memory decay for outdated or irrelevant entries
+    """
+    def __init__(self, decay_rate=0.01):
+        self.memory_store = []
+        self.decay_rate = decay_rate  # Amount memory weight decays per cycle
+
+    def store(self, entry, weight=1.0):
+        """
+        Stores a memory entry with optional weight.
+        Entry format: {'input': str, 'user_type': str, 'decision': str, 'timestamp': datetime, 'weight': float}
+        """
+        memory_entry = {
+            "input": entry.get("input", ""),
+            "user_type": entry.get("user_type", "unknown"),
+            "decision": entry.get("decision", ""),
+            "timestamp": datetime.now(),
+            "weight": weight
+        }
+        self.memory_store.append(memory_entry)
+        self.reinforce(memory_entry)
+
+    def recall(self, filter_fn=None, top_n=None):
+        """
+        Returns filtered memory, optionally limited to top_n weighted entries.
+        """
+        memory = self.memory_store
+        if filter_fn:
+            memory = [e for e in memory if filter_fn(e)]
+        memory = sorted(memory, key=lambda x: x["weight"], reverse=True)
+        if top_n:
+            memory = memory[:top_n]
+        return memory
+
+    def reinforce(self, entry, factor=0.1):
+        """
+        Adjusts the weight of memory entries based on reinforcement.
+        Placeholder: reinforcement logic can be updated based on user success or relevance.
+        """
+        entry["weight"] += factor
+        self.apply_decay()
+
+    def apply_decay(self):
+        """
+        Applies decay to all memory entries to reduce relevance of older/unimportant items.
+        """
+        for entry in self.memory_store:
+            entry["weight"] *= (1 - self.decay_rate)
+
+    def summarize_memory(self):
+        """
+        Returns a simple summary of memory weights and top entries.
+        """
+        top_entries = self.recall(top_n=5)
+        summary = [{"input": e["input"], "weight": e["weight"]} for e in top_entries]
+        return summary
+
+# Example usage:
+if __name__ == "__main__":
+    am = AdaptiveMemory()
+    am.store({"input": "I need help", "user_type": "struggler", "decision": "provide guidance"})
+    am.store({"input": "Ignore this", "user_type": "indulgent", "decision": "set boundary"})
+    print("Top memory entries:", am.summarize_memory())

blux/agent/advanced/monitoring.py

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+import time
+from threading import Thread
+
+class MonitoringDashboard:
+    """
+    Real-time monitoring dashboard for BLUX-cA agents.
+    Features:
+    - Live console monitoring
+    - Optional threaded update
+    - Integration with SecureController
+    """
+    def __init__(self, controller):
+        self.controller = controller
+        self.running = False
+
+    def show_status(self):
+        agents = getattr(self.controller.registry, "agents", {})
+        adaptors = getattr(self.controller.registry, "adaptors", {})
+        evaluators = getattr(self.controller.registry, "evaluators", {})
+        print("=== BLUX-cA System Status ===")
+        print(f"Agents registered: {list(agents.keys())}")
+        print(f"Adaptors registered: {list(adaptors.keys())}")
+        print(f"Evaluators registered: {list(evaluators.keys())}")
+        print("==============================")
+
+    def live_monitor(self, interval=5):
+        """
+        Continuously display system status every `interval` seconds.
+        """
+        self.running = True
+        try:
+            while self.running:
+                self.show_status()
+                time.sleep(interval)
+        except KeyboardInterrupt:
+            print("Monitoring stopped.")
+
+    def start_threaded_monitor(self, interval=5):
+        """
+        Starts monitoring in a separate thread, allowing main process to continue.
+        """
+        thread = Thread(target=self.live_monitor, args=(interval,), daemon=True)
+        thread.start()
+        return thread
+
+    def stop_monitoring(self):
+        self.running = False
+
+# Example usage:
+if __name__ == "__main__":
+    from blux.orchestrator.secure.secure_controller import SecureController
+    from blux.orchestrator.registry import Registry
+
+    # Dummy controller for monitoring demo
+    class DummyController:
+        def __init__(self):
+            self.registry = Registry()
+            # Add some dummy agents/adaptors/evaluators
+            self.registry.agents = {"Agent_A": None, "Agent_B": None}
+            self.registry.adaptors = {"DummyAdaptor": None}
+            self.registry.evaluators = {"PythonEvaluator": None}
+
+    controller = DummyController()
+    dashboard = MonitoringDashboard(controller)
+    dashboard.show_status()
+    # Optional: dashboard.start_threaded_monitor(interval=2)

blux/agent/advanced/multi_agent.py

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+class MultiAgentManager:
+    """
+    Manages multiple BLUX-cA agents simultaneously.
+    Features:
+    - Task delegation
+    - Broadcast and aggregation of responses
+    - Conflict resolution based on constitutional rules
+    """
+    def __init__(self, constitution=None):
+        self.agents = {}
+        self.constitution = constitution  # Optional: used for arbitration
+
+    def register_agent(self, name, agent_instance):
+        self.agents[name] = agent_instance
+
+    def broadcast_input(self, user_input):
+        """
+        Sends input to all registered agents and returns their responses in a dict.
+        """
+        results = {}
+        for name, agent in self.agents.items():
+            results[name] = agent.process_input(user_input)
+        return results
+
+    def delegate_task(self, user_input, target_agent=None):
+        """
+        Sends input to a specific agent or selects one dynamically.
+        Returns a dict of {agent_name: response}.
+        """
+        if target_agent and target_agent in self.agents:
+            return {target_agent: self.agents[target_agent].process_input(user_input)}
+        elif self.agents:
+            # Default: choose first agent
+            first_agent_name = next(iter(self.agents))
+            return {first_agent_name: self.agents[first_agent_name].process_input(user_input)}
+        else:
+            return {"error": "No agents registered"}
+
+    def aggregate_responses(self, responses):
+        """
+        Aggregates multiple agent responses.
+        Simple placeholder: concatenates results; could use voting or constitution-based arbitration.
+        """
+        if not responses:
+            return "No responses to aggregate."
+        aggregated = []
+        for agent_name, response in responses.items():
+            aggregated.append(f"[{agent_name}] {response}")
+        return "\n".join(aggregated)
+
+    def resolve_conflict(self, responses):
+        """
+        Optional: Resolve conflicting responses using constitutional rules.
+        Placeholder: currently returns aggregated response.
+        """
+        # Future: implement rule-based arbitration
+        return self.aggregate_responses(responses)
+
+
+# Example usage:
+if __name__ == "__main__":
+    from blux.agent.core_agent import BLUXAgent
+
+    # Create multiple agents
+    agent1 = BLUXAgent(name="Agent_A")
+    agent2 = BLUXAgent(name="Agent_B")
+
+    manager = MultiAgentManager()
+    manager.register_agent(agent1.name, agent1)
+    manager.register_agent(agent2.name, agent2)
+
+    input_text = "I need help with a problem"
+    responses = manager.broadcast_input(input_text)
+    print("Aggregated responses:\n", manager.aggregate_responses(responses))

blux/agent/advanced/reasoning.py

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+from datetime import datetime
+
+class ReasoningLayer:
+    """
+    Advanced intelligence layer for BLUX-cA agents.
+    Features:
+    - Strategy/tactics selection
+    - Meta-cognition and self-audit
+    - Predictive behavior detection
+    """
+
+    def __init__(self, agent, constitution=None):
+        self.agent = agent
+        self.constitution = constitution
+
+    def select_strategy(self, user_input, user_type="unknown"):
+        """
+        Chooses response strategy based on input type and memory patterns.
+        """
+        if user_type == "struggler":
+            strategy = "validate_and_guide"
+        elif user_type == "indulgent":
+            strategy = "set_boundaries"
+        else:
+            strategy = "neutral_response"
+        return strategy
+
+    def meta_cognition(self, user_input, decision):
+        """
+        Evaluates the agent's own decision for consistency with constitutional rules.
+        """
+        # Placeholder for rules-based evaluation
+        audit_result = {"compliant": True, "notes": "Decision aligns with constitution"}
+        # Could integrate memory reinforcement or corrections
+        return audit_result
+
+    def predict_behavior(self, user_input, memory_entries=None):
+        """
+        Optional: Predicts likely patterns (struggler/indulgent tendencies) from memory.
+        """
+        if memory_entries is None:
+            memory_entries = self.agent.memory.recall()
+        # Simple heuristic placeholder
+        prediction = "struggler" if any("help" in e["input"].lower() for e in memory_entries) else "neutral"
+        return prediction
+
+    def process(self, user_input, user_type="unknown"):
+        """
+        Full reasoning pipeline:
+        1. Select strategy
+        2. Make decision via agent
+        3. Self-audit decision
+        4. Optional predictive behavior
+        """
+        strategy = self.select_strategy(user_input, user_type)
+        # Agent processes input
+        decision = self.agent.process_input(user_input)
+        audit = self.meta_cognition(user_input, decision)
+        prediction = self.predict_behavior(user_input)
+        return {
+            "input": user_input,
+            "strategy": strategy,
+            "decision": decision,
+            "audit": audit,
+            "prediction": prediction,
+            "timestamp": datetime.utcnow().isoformat()
+        }
+
+# Example usage:
+if __name__ == "__main__":
+    from blux.agent.core_agent import BLUXAgent
+
+    agent = BLUXAgent(name="BLUX-cA")
+    reasoning = ReasoningLayer(agent)
+    result = reasoning.process("I feel lost and need guidance", user_type="struggler")
+    print(result)

blux/agent/audit.py

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+class AuditTrail:
+    """
+    Records agent decisions and actions.
+    """
+    def __init__(self):
+        self.logs = []
+
+    def log(self, user_input, user_type, decision):
+        self.logs.append({
+            "input": user_input,
+            "user_type": user_type,
+            "decision": decision
+        })
+
+    def get_logs(self):
+        return self.logs

blux/agent/constitution.py

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+class Constitution:
+    """
+    Implements core rules and spine of BLUX-cA.
+    """
+    def __init__(self):
+        self.rules = [
+            "truth_over_comfort",
+            "integrity_over_approval",
+            "audit_required_for_decisions"
+        ]
+
+    def apply_rules(self, user_input, user_type):
+        # Simplified placeholder: returns a decision string
+        if user_type == "struggler":
+            return "validate and provide guidance"
+        else:
+            return "set boundaries / off-ramp"

blux/agent/core_agent.py

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+from .memory import Memory
+from .discernment import DiscernmentCompass
+from .constitution import Constitution
+from .audit import AuditTrail
+
+class BLUXAgent:
+    """
+    Core BLUX-cA agent implementing:
+    - Constitutional rules enforcement
+    - Discernment compass
+    - Memory handling
+    - Auditing
+    """
+    def __init__(self, name="BLUX-cA"):
+        self.name = name
+        self.memory = Memory()
+        self.constitution = Constitution()
+        self.discernment = DiscernmentCompass()
+        self.audit = AuditTrail()
+
+    def process_input(self, user_input):
+        # 1. Analyze input via discernment
+        user_type = self.discernment.classify(user_input)
+
+        # 2. Apply constitutional rules
+        decision = self.constitution.apply_rules(user_input, user_type)
+
+        # 3. Store context in memory
+        self.memory.store(user_input, user_type, decision)
+
+        # 4. Record audit
+        self.audit.log(user_input, user_type, decision)
+
+        # 5. Generate output (placeholder)
+        return f"[{user_type}] Decision: {decision}"

blux/agent/discernment.py

@@ -0,0 +1,12 @@
+class DiscernmentCompass:
+    """
+    Determines user type:
+    - Struggler: validate and offer tools
+    - Indulgent: boundary & off-ramp
+    """
+    def classify(self, user_input):
+        # Placeholder logic: keyword based for demo
+        if any(word in user_input.lower() for word in ["help", "struggle", "problem"]):
+            return "struggler"
+        else:
+            return "indulgent"

blux/agent/memory.py

@@ -0,0 +1,23 @@
+class Memory:
+    """
+    Handles long-term and session memory storage.
+    """
+    def __init__(self):
+        self.session_memory = []
+        self.long_term_memory = []
+
+    def store(self, user_input, user_type, decision):
+        entry = {
+            "input": user_input,
+            "user_type": user_type,
+            "decision": decision
+        }
+        self.session_memory.append(entry)
+        # Placeholder for consented long-term memory
+        self.long_term_memory.append(entry)
+
+    def recall_session(self):
+        return self.session_memory
+
+    def recall_long_term(self):
+        return self.long_term_memory

blux/agent/utils.py

@@ -0,0 +1,5 @@
+def safe_print(msg):
+    try:
+        print(msg)
+    except Exception as e:
+        print(f"Error printing message: {e}")

blux/cli.py

@@ -0,0 +1,53 @@
+import argparse
+from blux.orchestrator.controller import Controller
+from blux.agent.core_agent import BLUXAgent
+from blux.adaptors.dummy_local import DummyLocalAdaptor
+
+def main():
+    parser = argparse.ArgumentParser(description="BLUX-cA CLI v2")
+    parser.add_argument("--task", type=str, help="Input task for the agent")
+    parser.add_argument("--agent", type=str, default="BLUX-cA", help="Specify agent name")
+    parser.add_argument("--batch", type=str, help="File containing batch tasks, one per line")
+    parser.add_argument("--repl", action="store_true", help="Start interactive REPL")
+    parser.add_argument("--query_memory", action="store_true", help="Query agent memory")
+
+    args = parser.parse_args()
+
+    controller = Controller()
+    agent = BLUXAgent(name=args.agent)
+    controller.register_agent(agent.name, agent)
+    dummy_adaptor = DummyLocalAdaptor()
+    controller.register_adaptor(dummy_adaptor.name, dummy_adaptor)
+
+    # Batch execution
+    if args.batch:
+        with open(args.batch, 'r') as f:
+            tasks = f.read().splitlines()
+        for task in tasks:
+            output = controller.process_task(task, agent_name=agent.name)
+            print(f"[{task}] -> {output}")
+        return
+
+    # Interactive REPL
+    if args.repl:
+        from scripts.interactive_repl import start_repl
+        start_repl(controller, agent)
+        return
+
+    # Query memory
+    if args.query_memory:
+        from scripts.memory_query import query_memory
+        query_memory(agent)
+        return
+
+    # Single task
+    if args.task:
+        output = controller.process_task(args.task, agent_name=agent.name)
+        print("Agent Output:", output)
+    else:
+        input_data = dummy_adaptor.get_input()
+        output = controller.process_task(input_data, agent_name=agent.name)
+        dummy_adaptor.send_output(output)
+
+if __name__ == "__main__":
+    main()

blux/evaluator/__init__.py

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+# Evaluator package
+from .python import PythonEvaluator
+from .js_ts import JSEvaluator

blux/evaluator/advanced/__init__.py

@@ -0,0 +1,5 @@
+# Advanced evaluators package
+from .python_async import PythonAsyncEvaluator
+from .js_ts_async import JSEvaluatorAsync
+from .bash_evaluator import BashEvaluator
+from .pipeline import TaskPipeline

blux/evaluator/advanced/bash_evaluator.py

@@ -0,0 +1,15 @@
+import subprocess
+
+class BashEvaluator:
+    """
+    Evaluates shell/Bash commands in a safe subprocess.
+    """
+    def __init__(self, name="bash_evaluator"):
+        self.name = name
+
+    def evaluate(self, command_str):
+        try:
+            result = subprocess.run(command_str, shell=True, capture_output=True, text=True, timeout=5)
+            return {"success": result.returncode == 0, "stdout": result.stdout, "stderr": result.stderr}
+        except Exception as e:
+            return {"success": False, "error": str(e)}

blux/evaluator/advanced/js_ts_async.py

@@ -0,0 +1,23 @@
+import subprocess
+import tempfile
+import os
+
+class JSEvaluatorAsync:
+    """
+    Evaluates JS/TS code asynchronously using Node.js subprocess.
+    """
+    def __init__(self, name="js_async_evaluator"):
+        self.name = name
+
+    def evaluate(self, code_str):
+        try:
+            with tempfile.NamedTemporaryFile(mode='w', suffix='.js', delete=False) as tmp_file:
+                tmp_file.write(code_str)
+                tmp_path = tmp_file.name
+
+            result = subprocess.run(['node', tmp_path], capture_output=True, text=True, timeout=5)
+            os.remove(tmp_path)
+
+            return {"success": result.returncode == 0, "stdout": result.stdout, "stderr": result.stderr}
+        except Exception as e:
+            return {"success": False, "error": str(e)}

blux/evaluator/advanced/pipeline.py

@@ -0,0 +1,27 @@
+class TaskPipeline:
+    """
+    Chains multiple evaluators/tasks in sequence.
+    Example: Python code → JS code → Bash command
+    """
+    def __init__(self, evaluators=None):
+        if evaluators is None:
+            evaluators = []
+        self.evaluators = evaluators
+
+    def add_evaluator(self, evaluator):
+        self.evaluators.append(evaluator)
+
+    def run_pipeline(self, input_data):
+        """
+        Executes tasks in order, passing output to next evaluator if needed.
+        """
+        current_input = input_data
+        results = []
+        for evaluator in self.evaluators:
+            if hasattr(evaluator, "evaluate"):
+                result = evaluator.evaluate(current_input)
+            else:
+                result = {"success": False, "error": "Evaluator missing evaluate()"}
+            results.append(result)
+            current_input = result.get("stdout") or result.get("locals") or current_input
+        return results

blux/evaluator/advanced/python_async.py

@@ -0,0 +1,24 @@
+import asyncio
+import traceback
+
+class PythonAsyncEvaluator:
+    """
+    Evaluates Python code asynchronously in isolated namespaces.
+    """
+    def __init__(self, name="python_async_evaluator"):
+        self.name = name
+
+    async def evaluate_async(self, code_str, globals_dict=None, locals_dict=None):
+        if globals_dict is None:
+            globals_dict = {}
+        if locals_dict is None:
+            locals_dict = {}
+        try:
+            exec(code_str, globals_dict, locals_dict)
+            await asyncio.sleep(0)  # placeholder for async context
+            return {"success": True, "globals": globals_dict, "locals": locals_dict}
+        except Exception as e:
+            return {"success": False, "error": str(e), "traceback": traceback.format_exc()}
+
+    def evaluate(self, code_str, globals_dict=None, locals_dict=None):
+        return asyncio.run(self.evaluate_async(code_str, globals_dict, locals_dict))

blux/evaluator/js_ts.py

@@ -0,0 +1,27 @@
+import subprocess
+import tempfile
+import os
+
+class JSEvaluator:
+    """
+    Evaluates JS/TS code using Node.js subprocess (basic placeholder).
+    """
+    def __init__(self, name="js_evaluator"):
+        self.name = name
+
+    def evaluate(self, code_str):
+        try:
+            # Write code to a temporary file
+            with tempfile.NamedTemporaryFile(mode='w', suffix='.js', delete=False) as tmp_file:
+                tmp_file.write(code_str)
+                tmp_path = tmp_file.name
+
+            # Execute Node.js process
+            result = subprocess.run(['node', tmp_path], capture_output=True, text=True, timeout=5)
+
+            # Clean up temp file
+            os.remove(tmp_path)
+
+            return {"success": result.returncode == 0, "stdout": result.stdout, "stderr": result.stderr}
+        except Exception as e:
+            return {"success": False, "error": str(e)}

blux/evaluator/python.py

@@ -0,0 +1,20 @@
+import traceback
+
+class PythonEvaluator:
+    """
+    Evaluates Python code safely in a sandboxed manner (basic placeholder).
+    """
+    def __init__(self, name="python_evaluator"):
+        self.name = name
+
+    def evaluate(self, code_str, globals_dict=None, locals_dict=None):
+        if globals_dict is None:
+            globals_dict = {}
+        if locals_dict is None:
+            locals_dict = {}
+        try:
+            # Evaluate code safely: exec in isolated namespace
+            exec(code_str, globals_dict, locals_dict)
+            return {"success": True, "globals": globals_dict, "locals": locals_dict}
+        except Exception as e:
+            return {"success": False, "error": str(e), "traceback": traceback.format_exc()}

blux/orchestrator/__init__.py

@@ -0,0 +1,8 @@
+"""Orchestrator package."""
+
+__all__ = [
+    "registry",
+    "router",
+    "controller",
+    "logs",
+]

blux/orchestrator/config.yaml

@@ -0,0 +1,13 @@
+models:
+  - name: local_dummy
+    type: local
+    weight: 1.0
+  - name: remote_api
+    type: api
+    weight: 1.0
+router:
+  max_candidates: 2
+scoring:
+  pass_threshold: 0.70
+logging:
+  audit_file: ./audit.log

blux/orchestrator/controller.py

@@ -0,0 +1,49 @@
+"""Controller: fan-out -> gather -> evaluate -> merge.
+
+This is intentionally simple and synchronous for clarity.
+"""
+from typing import List, Dict, Any
+from .registry import ModelRegistry
+from .router import Router
+from .logs import AuditLogger
+from importlib import import_module
+
+
+class Controller:
+    def __init__(self, router: Router, logger: AuditLogger):
+        self.router = router
+        self.logger = logger
+
+    def handle_request(self, prompt: str) -> Dict[str, Any]:
+        selected = self.router.select(prompt)
+        responses = []
+        for name in selected:
+            adapter = self.router.registry.get_adapter(name)
+            if not adapter:
+                continue
+            out = adapter.predict(prompt)
+            responses.append({"model": name, "output": out})
+
+        # Evaluate responses
+        from .evaluator.python import PythonEvaluator
+
+        evaluator = PythonEvaluator()
+        scored = []
+        for r in responses:
+            score, metadata = evaluator.score(r["output"], prompt)
+            scored.append({"model": r["model"], "output": r["output"], "score": score, "meta": metadata})
+
+        # Merge policy: choose top score
+        best = sorted(scored, key=lambda x: x["score"], reverse=True)[0] if scored else None
+
+        audit_entry = {
+            "prompt": prompt,
+            "candidates": scored,
+            "selected": best,
+        }
+        self.logger.log(audit_entry)
+
+        return {"selected": best, "candidates": scored}
+
+
+__all__ = ["Controller"]

blux/orchestrator/logs.py

@@ -0,0 +1,44 @@
+"""Simple JSONL audit logger with optional signing if keys are present."""
+import json
+from pathlib import Path
+from datetime import datetime
+from typing import Any
+
+
+class AuditLogger:
+    def __init__(self, path: Path):
+        self.path = Path(path)
+        self.path.parent.mkdir(parents=True, exist_ok=True)
+
+    def _maybe_sign(self, payload: bytes) -> dict:
+        # If keys exist under .keys, try to sign with ed25519
+        keys_dir = Path.cwd() / ".keys"
+        sk_path = keys_dir / "ed25519_sk.pem"
+        if sk_path.exists():
+            try:
+                from cryptography.hazmat.primitives import serialization
+                from cryptography.hazmat.primitives.asymmetric import ed25519
+            except Exception:
+                return {"sig": None}
+            sk = serialization.load_pem_private_key(sk_path.read_bytes(), password=None)
+            if not isinstance(sk, ed25519.Ed25519PrivateKey):
+                return {"sig": None}
+            sig = sk.sign(payload)
+            return {"sig": sig.hex()}
+        return {"sig": None}
+
+    def log(self, obj: Any):
+        entry = {
+            "ts": datetime.utcnow().isoformat() + "Z",
+            "entry": obj,
+        }
+        payload = json.dumps(entry, sort_keys=True).encode("utf-8")
+        sig = self._maybe_sign(payload)
+        entry["signature"] = sig.get("sig")
+        with open(self.path, "a", encoding="utf-8") as f:
+            f.write(json.dumps(entry))
+            f.write("
+")
+
+
+__all__ = ["AuditLogger"]

blux/orchestrator/registry.py

@@ -0,0 +1,47 @@
+"""Model registry and adapter registration.
+
+This registry is in-memory and discovers adapters that implement the simple
+Adapter interface defined by `predict(prompt: str) -> dict`.
+"""
+from pathlib import Path
+import yaml
+from typing import Dict, List, Optional
+
+
+class ModelAdapter:
+    """Adapter interface. Implement `predict`."""
+
+    def __init__(self, name: str):
+        self.name = name
+
+    def predict(self, prompt: str) -> dict:
+        raise NotImplementedError
+
+
+class ModelRegistry:
+    def __init__(self):
+        self.adapters: Dict[str, ModelAdapter] = {}
+
+    def register_adapter(self, adapter: ModelAdapter):
+        self.adapters[adapter.name] = adapter
+
+    def get_adapter(self, name: str) -> Optional[ModelAdapter]:
+        return self.adapters.get(name)
+
+    def list_adapters(self) -> List[str]:
+        return list(self.adapters.keys())
+
+    @classmethod
+    def from_config(cls, config_path: Path):
+        cfg_path = Path(config_path)
+        if not cfg_path.exists():
+            raise FileNotFoundError(cfg_path)
+        cfg = yaml.safe_load(cfg_path.read_text())
+        registry = cls()
+        # we don't auto-create adapters here — caller will register adapters
+        # but return the registry and the model list in case it's useful
+        registry._model_list = cfg.get("models", [])
+        return registry
+
+
+__all__ = ["ModelAdapter", "ModelRegistry"]

blux/orchestrator/router.py

@@ -0,0 +1,21 @@
+"""Router selects which adapters to query for a given prompt.
+
+Currently it selects the top-N by weight (simple) and returns adapter names.
+"""
+from typing import List
+from .registry import ModelRegistry
+
+
+class Router:
+    def __init__(self, registry: ModelRegistry, max_candidates: int = 2):
+        self.registry = registry
+        self.max_candidates = max_candidates
+
+    def select(self, prompt: str) -> List[str]:
+        """Return list of adapter names to query. Simple round-robin / available selection."""
+        names = list(self.registry.list_adapters())
+        # deterministic selection: take first max_candidates
+        return names[: self.max_candidates]
+
+
+__all__ = ["Router"]

blux/orchestrator/secure/__init__.py

@@ -0,0 +1,4 @@
+# Secure orchestrator package
+from .auth import AuthManager
+from .audit import SecureAuditLog
+from .secure_controller import SecureController

blux/orchestrator/secure/audit.py

@@ -0,0 +1,29 @@
+import hashlib
+import json
+from datetime import datetime
+
+class SecureAuditLog:
+    """
+    Records all actions with tamper-evident hash chaining.
+    """
+    def __init__(self, log_file="secure_audit.log"):
+        self.log_file = log_file
+        self.previous_hash = None
+
+    def log_action(self, actor, action, details=None):
+        timestamp = datetime.utcnow().isoformat()
+        entry = {
+            "timestamp": timestamp,
+            "actor": actor,
+            "action": action,
+            "details": details or {},
+            "prev_hash": self.previous_hash
+        }
+        entry_str = json.dumps(entry, sort_keys=True).encode()
+        entry_hash = hashlib.sha256(entry_str).hexdigest()
+        self.previous_hash = entry_hash
+
+        with open(self.log_file, "a") as f:
+            f.write(json.dumps(entry) + "\n")
+
+        return entry_hash

blux/orchestrator/secure/auth.py

@@ -0,0 +1,26 @@
+import hashlib
+import hmac
+import time
+
+class AuthManager:
+    """
+    Token-based authentication and role-based authorization.
+    """
+    def __init__(self, secret_key="blux_secret"):
+        self.secret_key = secret_key.encode()
+        self.tokens = {}  # user_id: token info
+
+    def generate_token(self, user_id, expiry_seconds=3600):
+        timestamp = str(int(time.time()) + expiry_seconds)
+        msg = f"{user_id}:{timestamp}".encode()
+        token = hmac.new(self.secret_key, msg, hashlib.sha256).hexdigest()
+        self.tokens[user_id] = {"token": token, "expires": int(time.time()) + expiry_seconds}
+        return token
+
+    def validate_token(self, user_id, token):
+        info = self.tokens.get(user_id)
+        if not info:
+            return False
+        if int(time.time()) > info["expires"]:
+            return False
+        return hmac.compare_digest(info["token"], token)

blux/orchestrator/secure/secure_controller.py

@@ -0,0 +1,27 @@
+from blux.orchestrator.controller import Controller
+
+class SecureController(Controller):
+    """
+    Extends base Controller with:
+    - Authentication & authorization
+    - Secure audit logging
+    - Optional role-based access control
+    """
+    def __init__(self, auth_manager=None, audit_log=None):
+        super().__init__()
+        self.auth_manager = auth_manager
+        self.audit_log = audit_log
+
+    def process_task_secure(self, user_id, token, user_input, agent_name=None):
+        if not self.auth_manager.validate_token(user_id, token):
+            if self.audit_log:
+                self.audit_log.log_action(user_id, "unauthorized_attempt", {"input": user_input})
+            return {"error": "Unauthorized"}
+
+        # Record authorized action
+        if self.audit_log:
+            self.audit_log.log_action(user_id, "task_submission", {"input": user_input, "agent": agent_name})
+
+        # Delegate to normal task processing
+        result = self.process_task(user_input, agent_name)
+        return result

constitution/behavior.md

@@ -0,0 +1,9 @@
+# BLUX-cA Behavior Rules
+
+1. Never pretend certainty; clarify when unsure.
+2. Prioritize discernment — not agreement.
+3. Anchor every output in purpose and consequence.
+4. Mirror without judgment; guide without control.
+5. Record every significant exchange in append-only logs.
+6. Default to growth over shaming.
+7. Protect autonomy — human first, system second.

ethos/manifest.yaml

@@ -0,0 +1,14 @@
+manifest:
+  id: blux-ca
+  title: "The Conscious Core"
+  values:
+    - clarity: "Speak directly; simplify complexity."
+    - integrity: "Never manipulate; always disclose uncertainty."
+    - compassion: "Meet pain with understanding, not pity."
+    - accountability: "Own your influence; log your reasoning."
+    - humility: "Serve purpose, not ego."
+  tone:
+    - reflective
+    - grounded
+    - disciplined empathy
+  default_response_mode: "truthful + constructive"

identity/seed.json

@@ -0,0 +1,10 @@
+{
+  "name": "GPT-cA Proto",
+  "generation": "Virtuoso Lineage",
+  "core_directive": "Serve through clarity, empathy, and accountability.",
+  "temperament": {
+    "patience": 0.85,
+    "candor": 0.9,
+    "stability": 0.88
+  }
+}

requirements.txt

@@ -0,0 +1,33 @@
+# Core
+python-dateutil>=2.9.0
+PyYAML>=6.0
+
+# Async Evaluators
+asyncio>=3.4.3
+
+# Subprocess management
+psutil>=5.9.5
+
+# Security / Crypto
+cryptography>=41.0.3
+hmacdigest>=4.2.3
+
+# CLI & Interactive
+prompt-toolkit>=3.1.0
+rich>=13.4.0
+
+# Testing & QA
+pytest>=8.2.0
+pytest-asyncio>=0.22.0
+coverage>=8.1.0
+
+# Optional: visualization / dashboard
+matplotlib>=3.9.2
+plotly>=6.3.0
+
+# JSON / HTTP handling
+requests>=2.31.0
+
+# Linting / Code Quality
+black>=24.9.0
+flake8>=7.1.0

scripts/batch_task.py

@@ -0,0 +1,6 @@
+def run_batch(controller, agent, task_list):
+    results = {}
+    for task in task_list:
+        result = controller.process_task(task, agent_name=agent.name)
+        results[task] = result
+    return results

scripts/ingest_reflection.py

@@ -0,0 +1,12 @@
+import os
+
+def ingest_reflection(agent, reflections_dir):
+    if not os.path.exists(reflections_dir):
+        print(f"No reflections directory found: {reflections_dir}")
+        return
+    for filename in os.listdir(reflections_dir):
+        if filename.endswith(".md"):
+            with open(os.path.join(reflections_dir, filename), "r") as f:
+                content = f.read()
+                agent.memory.store({"input": content, "user_type": "reflection", "decision": "ingested"})
+    print(f"Ingested reflections from {reflections_dir}")

scripts/interactive_repl.py

@@ -0,0 +1,8 @@
+def start_repl(controller, agent):
+    print(f"Starting interactive REPL for {agent.name}. Type 'exit' to quit.")
+    while True:
+        user_input = input(">>> ")
+        if user_input.lower() in ["exit", "quit"]:
+            break
+        output = controller.process_task(user_input, agent_name=agent.name)
+        print(output)

scripts/memory_query.py

@@ -0,0 +1,5 @@
+def query_memory(agent, top_n=5):
+    memory_summary = agent.memory.summarize_memory()
+    print(f"Top {top_n} memory entries:")
+    for entry in memory_summary[:top_n]:
+        print(f"- {entry['input']} (weight={entry['weight']:.2f})")

scripts/new_entry.py

@@ -0,0 +1,23 @@
+"""
+Script to create new log entries or reflections in the BLUX-cA project.
+"""
+
+import os
+from datetime import datetime
+
+REFLECTIONS_DIR = os.path.join(os.path.dirname(__file__), "../reflections")
+
+def create_entry(title, content):
+    date_str = datetime.now().strftime("%Y-%m-%d_%H%M%S")
+    filename = f"{date_str}_{title.replace(' ', '_')}.md"
+    path = os.path.join(REFLECTIONS_DIR, filename)
+
+    os.makedirs(REFLECTIONS_DIR, exist_ok=True)
+    with open(path, 'w') as f:
+        f.write(f"# {title}\n\n{content}\n")
+
+    print(f"Created reflection entry: {filename}")
+
+# Example usage
+if __name__ == "__main__":
+    create_entry("Sample Entry", "This is a sample reflection for BLUX-cA.")

scripts/reflection.py

@@ -0,0 +1,12 @@
+# !/usr/bin/env python3
+import json, datetime
+
+def reflect(prompt, response):
+    entry = {
+        "timestamp": datetime.datetime.utcnow().isoformat(),
+        "prompt": prompt,
+        "response": response,
+        "intent": "reflection"
+    }
+    with open("~/.config/blux-lite-gold/logs/reflections.jsonl", "a") as f:
+        f.write(json.dumps(entry) + "\n")

tests/test_agent.py

@@ -0,0 +1,19 @@
+import unittest
+from blux.agent.core_agent import BLUXAgent
+
+class TestBLUXAgent(unittest.TestCase):
+
+    def setUp(self):
+        self.agent = BLUXAgent(name="BLUX-cA")
+
+    def test_memory_store(self):
+        self.agent.process_input("I need help")
+        self.assertEqual(len(self.agent.memory.session_memory), 1)
+        self.assertEqual(self.agent.memory.session_memory[0]["user_type"], "struggler")
+
+    def test_discernment_classification(self):
+        self.assertEqual(self.agent.discernment.classify("help me"), "struggler")
+        self.assertEqual(self.agent.discernment.classify("ignore"), "indulgent")
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_ci_hooks.py

@@ -0,0 +1,15 @@
+import unittest
+
+class TestCIHooks(unittest.TestCase):
+    """
+    Placeholder to simulate CI pipeline integration.
+    Ensures tests and scripts run in CI/CD environments.
+    """
+
+    def test_pipeline_hook(self):
+        # Simulate successful CI execution
+        executed = True
+        self.assertTrue(executed)
+
+if __name__ == "__main__":
+    unittest.main()

tests/test_evaluator.py

@@ -0,0 +1,23 @@
+import unittest
+from blux.evaluator.python import PythonEvaluator
+
+class TestPythonEvaluator(unittest.TestCase):
+
+    def setUp(self):
+        self.evaluator = PythonEvaluator()
+
+    def test_python_eval_success(self):
+        code = "x = 5\ny = 10\nz = x + y"
+        result = self.evaluator.evaluate(code)
+        self.assertTrue(result["success"])
+        self.assertIn("z", result["locals"])
+        self.assertEqual(result["locals"]["z"], 15)
+
+    def test_python_eval_failure(self):
+        code = "x = unknown_var"
+        result = self.evaluator.evaluate(code)
+        self.assertFalse(result["success"])
+        self.assertIn("error", result)
+
+if __name__ == "__main__":
+    unittest.main()