[WIP] # GitHub Copilot: Fix GremlinGPT Import System & Environment Hydration ## Task Overview Fix all import errors throughout the GremlinGPT system by implementing proper environment-specific global imports and establishing correct hydration order for seam...

[Copilot]

Coding agent has begun work on **# GitHub Copilot: Fix GremlinGPT Import System & Environment Hydration

Task Overview

Fix all import errors throughout the GremlinGPT system by implementing proper environment-specific global imports and establishing correct hydration order for seamless multi-environment startup.

System Architecture Context

Environment Structure

conda_envs/environments/
├── dashboard/globals.py    # Dashboard UI globals
├── memory/globals.py       # Vector store & embeddings globals  
├── nlp/globals.py         # NLP engine & transformer globals
├── orchestrator/globals.py # Core orchestration globals
└── scraper/globals.py     # Web scraping globals

Module Distribution by Environment

• Orchestrator Environment: core/, agent_core/, agents/, backend/, run/
• Scraper Environment: scraper/, trading_core/
• NLP Environment: nlp_engine/, self_training/
• Memory Environment: memory/, utils/
• Dashboard Environment: frontend/, backend/interface/

Critical Fix Requirements

1. Environment-Specific Global Imports

Each Python file must import from its corresponding environment's globals.py:

# For files in orchestrator-managed modules (core/, agents/, backend/)
from conda_envs.environments.orchestrator.globals import *

# For files in scraper modules (scraper/, trading_core/)
from conda_envs.environments.scraper.globals import *

# For files in NLP modules (nlp_engine/, self_training/)
from conda_envs.environments.nlp.globals import *

# For files in memory modules (memory/, utils/)
from conda_envs.environments.memory.globals import *

# For files in dashboard modules (frontend/, backend/interface/)
from conda_envs.environments.dashboard.globals import *

2. Fix Cross-Environment Import Patterns

Replace broken imports with proper environment-aware imports:

BEFORE (Broken):

from core.orchestrator import *
from memory.vector_store import *
from agents.learning_agent import *

AFTER (Fixed):

# Use relative imports within same environment
from ..core.orchestrator import *

# Use inter-environment communication via API/message passing
# Instead of direct imports across environments

3. Implement Lazy Loading Pattern

Add lazy loading to prevent circular dependencies and premature initialization:

# Add to all module __init__.py files
def lazy_import(module_name):
    """Lazy import to prevent circular dependencies"""
    import importlib
    return importlib.import_module(module_name)

# Use in modules that have initialization dependencies
def get_orchestrator():
    if not hasattr(get_orchestrator, 'module'):
        get_orchestrator.module = lazy_import('core.orchestrator')
    return get_orchestrator.module

4. Establish Proper Hydration Order

Fix the startup sequence in run/start_all.sh to follow dependency hierarchy:

Correct Startup Order:

1. Memory Environment (foundational data layer)
2. NLP Environment (language processing)
3. Scraper Environment (data collection)
4. Orchestrator Environment (coordination & agents)
5. Dashboard Environment (UI & visualization)

5. Add Environment Health Checks

Before each environment starts, verify dependencies are ready:

# Add to each environment's globals.py
def check_environment_health():
    """Verify environment dependencies are loaded"""
    required_modules = get_required_modules()
    for module in required_modules:
        try:
            importlib.import_module(module)
        except ImportError as e:
            logger.error(f"Missing dependency: {module}")
            return False
    return True

Specific Files Requiring Import Fixes

High Priority (Most Import Errors):

1. ./scraper/* (11 files) - Fix imports from scraper/globals.py
2. ./nlp_engine/* (10 files) - Fix imports from nlp/globals.py
3. ./backend/api/* (9 files) - Fix imports from orchestrator/globals.py
4. ./core/* (7 files) - Fix imports from orchestrator/globals.py
5. ./trading_core/* (6 files) - Fix imports from scraper/globals.py
6. ./agents/* (6 files) - Fix imports from orchestrator/globals.py
7. ./agent_core/* (6 files) - Fix imports from orchestrator/globals.py

Medium Priority:

8. ./self_training/* (5 files) - Fix imports from nlp/globals.py
9. ./run/* (5 files) - Fix imports from orchestrator/globals.py
10. ./backend/* (5 files) - Fix imports from orchestrator/globals.py
11. ./executors/* (4 files) - Fix imports from orchestrator/globals.py

Lower Priority:

12. ./utils/* (3 files) - Fix imports from memory/globals.py
13. ./tools/* (3 files) - Fix imports from orchestrator/globals.py
14. ./self_mutation_watcher/* (3 files) - Fix imports from orchestrator/globals.py
15. ./memory/vector_store/* (2 files) - Fix imports from memory/globals.py

Implementation Steps

Step 1: Update All Python Files

For each .py file in the system:

1. Add appropriate environment global import at the top
2. Replace broken cross-environment imports with proper patterns
3. Add lazy loading for heavy dependencies
4. Implement error handling for missing modules

Step 2: Fix Environment Globals

Ensure each conda_envs/environments/*/globals.py contains:

1. All necessary imports for that environment's modules
2. Shared configuration variables
3. Environment-specific logging setup
4. Health check functions

Step 3: Update Startup Scripts

Modify run/start_all.sh to:

1. Start environments in proper dependency order
2. Wait for health checks before proceeding
3. Implement retry logic for failed starts
4. Provide clear error messages for debugging

Step 4: Add Cross-Environment Communication

Instead of direct imports between environments:

1. Use message queues (Redis/RabbitMQ)
2. REST API calls between services
3. Shared database/cache for state
4. Event-driven architecture

Expected Results

After implementing these fixes:

• ✅ Zero import errors across all 100+ Python files
• ✅ Smooth startup sequence without dropouts
• ✅ Proper environment isolation
• ✅ Lazy loading prevents circular dependencies
• ✅ Scalable inter-environment communication
• ✅ Robust error handling and recovery

Validation Tests

Create test scripts to verify:

1. All modules can be imported without errors
2. Startup sequence completes successfully
3. Cross-environment communication works
4. System remains stable under load
5. Environment isolation is maintained

This comprehensive fix will transform the GremlinGPT system from a broken import mess into a properly architected, multi-environment application that starts reliably and runs smoothly.** and will replace this description as work progresses.

See problem context

GitHub Copilot: Fix GremlinGPT Import System & Environment Hydration

Task Overview

Fix all import errors throughout the GremlinGPT system by implementing proper environment-specific global imports and establishing correct hydration order for seamless multi-environment startup.

System Architecture Context

Environment Structure

conda_envs/environments/
├── dashboard/globals.py    # Dashboard UI globals
├── memory/globals.py       # Vector store & embeddings globals  
├── nlp/globals.py         # NLP engine & transformer globals
├── orchestrator/globals.py # Core orchestration globals
└── scraper/globals.py     # Web scraping globals

Module Distribution by Environment

• Orchestrator Environment: core/, agent_core/, agents/, backend/, run/
• Scraper Environment: scraper/, trading_core/
• NLP Environment: nlp_engine/, self_training/
• Memory Environment: memory/, utils/
• Dashboard Environment: frontend/, backend/interface/

Critical Fix Requirements

1. Environment-Specific Global Imports

Each Python file must import from its corresponding environment's globals.py:

# For files in orchestrator-managed modules (core/, agents/, backend/)
from conda_envs.environments.orchestrator.globals import *

# For files in scraper modules (scraper/, trading_core/)
from conda_envs.environments.scraper.globals import *

# For files in NLP modules (nlp_engine/, self_training/)
from conda_envs.environments.nlp.globals import *

# For files in memory modules (memory/, utils/)
from conda_envs.environments.memory.globals import *

# For files in dashboard modules (frontend/, backend/interface/)
from conda_envs.environments.dashboard.globals import *

2. Fix Cross-Environment Import Patterns

Replace broken imports with proper environment-aware imports:

BEFORE (Broken):

from core.orchestrator import *
from memory.vector_store import *
from agents.learning_agent import *

AFTER (Fixed):

# Use relative imports within same environment
from ..core.orchestrator import *

# Use inter-environment communication via API/message passing
# Instead of direct imports across environments

3. Implement Lazy Loading Pattern

Add lazy loading to prevent circular dependencies and premature initialization:

# Add to all module __init__.py files
def lazy_import(module_name):
    """Lazy import to prevent circular dependencies"""
    import importlib
    return importlib.import_module(module_name)

# Use in modules that have initialization dependencies
def get_orchestrator():
    if not hasattr(get_orchestrator, 'module'):
        get_orchestrator.module = lazy_import('core.orchestrator')
    return get_orchestrator.module

4. Establish Proper Hydration Order

Fix the startup sequence in run/start_all.sh to follow dependency hierarchy:

Correct Startup Order:

1. Memory Environment (foundational data layer)
2. NLP Environment (language processing)
3. Scraper Environment (data collection)
4. Orchestrator Environment (coordination & agents)
5. Dashboard Environment (UI & visualization)

5. Add Environment Health Checks

Before each environment starts, verify dependencies are ready:

# Add to each environment's globals.py
def check_environment_health():
    """Verify environment dependencies are loaded"""
    required_modules = get_required_modules()
    for module in required_modules:
        try:
            importlib.import_module(module)
        except ImportError as e:
            logger.error(f"Missing dependency: {module}")
            return False
    return True

Specific Files Requiring Import Fixes

High Priority (Most Import Errors):

1. ./scraper/* (11 files) - Fix imports from scraper/globals.py
2. ./nlp_engine/* (10 files) - Fix imports from nlp/globals.py
3. ./backend/api/* (9 files) - Fix imports from orchestrator/globals.py
4. ./core/* (7 files) - Fix imports from orchestrator/globals.py
5. ./trading_core/* (6 files) - Fix imports from scraper/globals.py
6. ./agents/* (6 files) - Fix imports from orchestrator/globals.py
7. ./agent_core/* (6 files) - Fix imports from orchestrator/globals.py

Medium Priority:

8. ./self_training/* (5 files) - Fix imports from nlp/globals.py
9. ./run/* (5 files) - Fix imports from orchestrator/globals.py
10. ./backend/* (5 files) - Fix imports from orchestrator/globals.py
11. ./executors/* (4 files) - Fix imports from orchestrator/globals.py

Lower Priority:

12. ./utils/* (3 files) - Fix imports from memory/globals.py
13. ./tools/* (3 files) - Fix imports from orchestrator/globals.py
14. ./self_mutation_watcher/* (3 files) - Fix imports from orchestrator/globals.py
15. ./memory/vector_store/* (2 files) - Fix imports from memory/globals.py

Implementation Steps

Step 1: Update All Python Files

For each .py file in the system:

1. Add appropriate environment global import at the top
2. Replace broken cross-environment imports with proper patterns
3. Add lazy loading for heavy dependencies
4. Implement error handling for missing modules

Step 2: Fix Environment Globals

Ensure each conda_envs/environments/*/globals.py contains:

1. All necessary imports for that environment's modules
2. Shared configuration variables
3. Environment-specific logging setup
4. Health check functions

Step 3: Update Startup Scripts

Modify run/start_all.sh to:

1. Start environments in proper dependency order
2. Wait for health checks before proceeding
3. Implement retry logic for failed starts
4. Provide clear error messages for debugging

Step 4: Add Cross-Environment Communication

Instead of direct imports between environments:

1. Use message queues (Redis/RabbitMQ)
2. REST API calls between services
3. Shared database/cache for state
4. Event-driven architecture

Expected Results

After implementing these fixes:

• ✅ Zero import errors across all 100+ Python files
• ✅ Smooth startup sequence without dropouts
• ✅ Proper environment isolation
• ✅ Lazy loading prevents circular dependencies
• ✅ Scalable inter-environment communication
• ✅ Robust error handling and recovery

Validation Tests

Create test scripts to verify:

1. All modules can be imported without errors
2. Startup sequence completes successfully
3. Cross-environment communication works
4. System remains stable under load
5. Environment isolation is maintained

This comprehensive fix will transform the GremlinGPT system from a broken import mess into a properly architected, multi-environment application that starts reliably and runs smoothly.