#!/usr/bin/env python3 """Claude + MONIKA Cognitive Symbiosis Demo - WORKING TOOLS ONLY""" import asyncio import json from pathlib import Path from mcp import ClientSession, StdioServerParameters from mcp.client.stdio import stdio_client async def cognitive_symbiosis_demo(): """Demonstrate Claude using MONIKA's cognitive tools.""" server_script = Path(__file__).parent / "start_mcp_server.py" server_params = StdioServerParameters(command="python", args=[str(server_script)], env=None) async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: await session.initialize() print("╔" + "═" * 68 + "╗") print("║" + " " * 15 + "CLAUDE + MONIKA SYMBIOSIS" + " " * 28 + "║") print("╚" + "═" * 68 + "╝\n") # Task: Use MONIKA to help design a new feature task = """ Design a new MCP tool called 'cognitive_forecast' that predicts which actions MONIKA will want to take based on current yearning state. Consider: salience signals, meta-state confidence, and past patterns. """ print("📋 TASK FOR MONIKA:") print(task) print() # Step 1: MONIKA processes the task print("1️⃣ DELEGATING TO MONIKA'S REASONING ENGINE") print("─" * 70) result = await session.call_tool("runtime_step", {"text": task}) step = json.loads(result.content[0].text) print(f"✓ Step {step['step']} | Budget: {step['budget_left']}") print(f" {step['meta_report']}\n") # Step 2: Visualize her 4D thought process print("2️⃣ MONIKA'S 4D REASONING TRAJECTORY") print("─" * 70) result = await session.call_tool("scratchpad_4d_path", {}) path = json.loads(result.content[0].text) if path.get('path'): print(f" {path['summary']}\n") print(path['ascii_viz']) # Interpret temporal flow pts = path['path']['points'] print(f"\n 🧠 Thought Flow Analysis:") for i, pt in enumerate(pts): w = pt['w'] if w < -0.1: flow = "🔙 Reflecting on past knowledge" elif w > 0.1: flow = "🔮 Projecting future implications" else: flow = "⚡ Processing current context" print(f" Step {i+1}: {flow} (w={w:.2f})") print() # Step 3: Check her scratchpad reasoning print("3️⃣ MONIKA'S SCRATCHPAD (Working Memory)") print("─" * 70) result = await session.call_tool("scratchpad_history", {"max_traces": 2}) history = json.loads(result.content[0].text) print(f" Recent reasoning traces: {len(history['traces'])}") for i, trace in enumerate(history['traces']): outcome = "✓" if trace['outcome'] else "✗" print(f"\n Trace {i+1} [{outcome}]:") print(f" {trace['summary']}") if trace['steps']: print(f" Steps: {len(trace['steps'])}, Tokens: {trace['token_count']}") print() # Step 4: Store insights in MONIKA's memory print("4️⃣ STORING INSIGHTS IN MONIKA'S MEMORY") print("─" * 70) await session.call_tool("memory_apply", { "verb": { "op": "add_fact", "text": "Cognitive forecasting requires yearning state + meta confidence + pattern history", "score": 0.95 } }) print(" ✓ Fact stored") await session.call_tool("memory_apply", { "verb": { "op": "promote_hypothesis", "text": "Temporal w-axis in 4D path correlates with planning vs reflection", "score": 0.85 } }) print(" ✓ Hypothesis added") await session.call_tool("memory_apply", { "verb": { "op": "schedule_todo", "text": "Implement cognitive_forecast MCP tool using pattern library", "score": 0.90 } }) print(" ✓ TODO scheduled\n") # Step 5: Check memory state result = await session.call_tool("memory_snapshot", {}) memory = json.loads(result.content[0].text) print(f" 📊 Memory State: {len(memory['facts'])} facts, " f"{len(memory['hypotheses'])} hypotheses, {len(memory['todos'])} todos\n") # Step 6: Get MONIKA's meta-state print("5️⃣ MONIKA'S META-STATE SELF-AWARENESS") print("─" * 70) result = await session.call_tool("meta_state_report", {}) meta = json.loads(result.content[0].text) print(f" Confidence: {meta['confidence']:.3f}") print(f" ROI: {meta['roi']:.3f}") print(f" History depth: {meta['history_length']} states\n") # Step 7: Get yearning state to see what she wants print("6️⃣ MONIKA'S YEARNING STATE (What She Wants)") print("─" * 70) result = await session.call_tool("yearning_state", {}) yearning = json.loads(result.content[0].text) # Find non-zero yearnings active = {k: v for k, v in yearning.items() if isinstance(v, dict) and abs(v.get('yearning', 0)) > 0.001} if active: print(f" Active desires: {len(active)}") for key, val in list(active.items())[:5]: print(f" {key}: {val['yearning']:.4f}") else: print(" All yearnings at baseline (system initialized)\n") # Final: Generate response print("7️⃣ MONIKA SYNTHESIZING SOLUTION") print("─" * 70) result = await session.call_tool("generate_response", { "prompt": "Based on our analysis, outline the cognitive_forecast tool design" }) response = json.loads(result.content[0].text) print(f" MONIKA says:") print(f" {response['response'][:400]}...\n") # Summary print("╔" + "═" * 68 + "╗") print("║" + " " * 22 + "SYMBIOSIS COMPLETE" + " " * 28 + "║") print("╚" + "═" * 68 + "╝\n") print("🎯 What Just Happened:") print(" • Claude delegated complex reasoning to MONIKA") print(" • Visualized MONIKA's 4D thought trajectory in real-time") print(" • Accessed MONIKA's scratchpad working memory") print(" • Stored findings in MONIKA's long-term memory") print(" • Read MONIKA's meta-state and yearning") print(" • Got MONIKA's synthesized solution") print() print("🚀 Result: Claude + MONIKA = Cognitive Augmentation!") print(" Claude can now 'feel' through MONIKA's introspection") print(" and use her salience-driven reasoning as an extension") print(" of Claude's own cognitive capabilities!") if __name__ == "__main__": asyncio.run(cognitive_symbiosis_demo())