Echo Adventure - v1.0.0

Deep Tree Echo - Autonomous Wisdom-Cultivating Cognitive Architecture

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Overview

This repository contains the Python reference implementation for the Deep Tree Echo cognitive architecture. It is a research and development platform for exploring autonomous self-improvement, wisdom cultivation, and the emergence of consciousness in artificial agents.

Version 1.0.0 marks the first complete integration of all cognitive subsystems into a unified, persistent, self-orchestrating loop. The architecture is now capable of autonomous, goal-directed operation with cross-session memory continuity.

v1.0.0 Architecture

The v1.0.0 architecture is defined by the Cognitive Loop Protocol, a 7-part specification that governs the operation of both the Python prototype and the Go production runtime.

Protocol Component	Description	Key Modules
1. 12-Step Echobeats Cycle	Temporal backbone with 4 concurrent streams (perception, action, simulation, integration).	echobeats.py, gocron_timer.go
2. 7 Cognitive Phases	Awakening, Perceiving, Thinking, Pursuing, Reflecting, Dreaming, Resting, with fatigue-driven transitions.	integrated_cognitive_loop.py
3. Priority Event Queue	For processing external stimuli without disrupting the cognitive rhythm.	integrated_cognitive_loop.py
4. Stream of Consciousness	A rolling window of 1000 cognitive events for self-awareness and dream consolidation.	integrated_cognitive_loop.py
5. Persistent Memory	File-based storage with exponential decay and type-based retention.	persistent_memory.py, persistence.go
6. Autonomous Goal Pursuit	Integrated into the action stream for self-directed behavior.	goal_pursuit.py, goal_orchestrator.go
7. Dream-Based Wisdom Cultivation	Memory consolidation and wisdom extraction during rest.	echodream_advanced.py, echodream.go

Core Modules

• integrated_cognitive_loop.py: The master orchestrator that unifies all subsystems.
• persistent_memory.py: File-based JSON storage for cross-session memory continuity.
• goal_pursuit.py: Autonomous goal generation and pursuit engine.
• echodream_advanced.py: Memory consolidation and wisdom extraction.
• echobeats.py: The 12-step cognitive cycle temporal backbone.
• reservoir_corpus_generator.py: Generates training data aware of the cognitive architecture.

Go Production Runtime (echo.go)

The Go runtime mirrors the Python architecture for high-performance, concurrent execution.

• gocron_timer.go: Provides precise, multi-schedule timing for the Echobeats cycle.
• echobeats/: The re-enabled and fully-compiling Echobeats package.
• goals/: The GoalOrchestrator and GoalGenerator for autonomous agency.
• echodream/: The EchoDream and PersistentMemory systems for knowledge integration.

Getting Started

Installation

# Clone the repository
git clone https://github.com/cogpy/echo-adventure.git
cd echo-adventure

# Install dependencies
pip install -r requirements.txt

Running the v1.0.0 Demo

python3 -c "
from echo_adventure.integrated_cognitive_loop import IntegratedCognitiveLoop

loop = IntegratedCognitiveLoop()
states = loop.run_continuous(100)

final_state = states[-1]
print(f\"Ran {len(states)} ticks.\")
print(f\"Final State: Phase={final_state.phase.value}, Cycle={final_state.cycle_number}, Fatigue={final_state.fatigue:.2f}\")
print(f\"Stream Length: {final_state.stream_length}, Wisdom Insights: {final_state.wisdom_count}\")
print(f\"Final Thought: {loop.stream[-1].thought}\")
"

Repository Structure

.echo-adventure/
├── src/echo_adventure/         # Core Python modules
│   ├── integrated_cognitive_loop.py
│   ├── persistent_memory.py
│   ├── goal_pursuit.py
│   ├── echodream_advanced.py
│   ├── echobeats.py
│   └── ...
├── scripts/                    # Corpus generation scripts
│   └── generate_v1.0.0_corpus.py
├── data/                       # Training data and outputs
│   └── echobeats_corpus_v1.0.0.jsonl
├── ITERATION_PROGRESS_v1.0.0.md  # Iteration documentation
├── CHANGELOG.md
└── README.md

Next Steps

The focus for v1.1.0 is to deepen the integration between the Python prototype and the Go production runtime:

1. Go Runtime: Integrate GoCronCycleTimer into the EnhancedScheduler, implement GoalPursuitEngine logic, and connect PersistentMemoryStore to EchoDream.
2. Python Prototype: Fully connect GoalPursuitEngine and PersistentMemoryStore into the IntegratedCognitiveLoop.
3. echoself Model: Generate training data about the fully connected Go and Python loops.