Add Cognitive Manufacturing Environment - #109
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A comprehensive manufacturing control environment with 30 tools for AI agents. Features: - Multi-machine production line simulation - Database persistence (PostgreSQL/SQLite support) - ML-powered analytics (predictive maintenance, anomaly detection, quality prediction) - Reinforcement learning optimization - Quality and inventory management - Energy monitoring and optimization - Scenario simulation and schedule optimization Architecture: - Physics-based simulator with temperature, vibration, and wear dynamics - Multi-objective reward system - 30 specialized tools across manufacturing operations - Complete OpenEnv compliance Author: Mohammad Mowas Dependencies: sqlalchemy, sentence-transformers, pandas, scikit-learn
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Pull Request Overview
This PR introduces a comprehensive Cognitive Manufacturing Environment for the OpenEnv framework. The environment simulates a production facility with realistic physics-based machine dynamics, providing 30 specialized tools for AI agents to manage manufacturing operations across quality control, inventory management, energy optimization, and predictive analytics.
Key Changes:
- Complete manufacturing simulation with single-machine and 4-machine production line modes
- 30 specialized tools organized in 5 phases (basic control, production line, data management, ML analytics, advanced management)
- Multi-objective reward system balancing safety, throughput, quality, cost, and sustainability
- Optional database persistence (PostgreSQL/SQLite), ML-powered analytics, and RL optimization
Reviewed Changes
Copilot reviewed 48 out of 48 changed files in this pull request and generated 24 comments.
Show a summary per file
| File | Description |
|---|---|
src/envs/cognitive_manufacturing/tools/*.py |
30 tool implementations for manufacturing control, data management, ML analytics, and advanced operations |
src/envs/cognitive_manufacturing/server/environment.py |
Core environment implementation with tool orchestration and reward computation |
src/envs/cognitive_manufacturing/server/simulator.py |
Physics-based machine simulator with temperature, vibration, and wear dynamics |
src/envs/cognitive_manufacturing/server/production_line.py |
4-machine production line with material flow and buffers |
src/envs/cognitive_manufacturing/server/database.py |
Database manager for persistent storage with SQLAlchemy |
src/envs/cognitive_manufacturing/server/ml_models.py |
ML service with predictive maintenance, anomaly detection, quality prediction, RL, and demand forecasting |
src/envs/cognitive_manufacturing/server/rewards.py |
Multi-objective reward calculation system |
src/envs/cognitive_manufacturing/models.py |
Data models for actions, observations, and state |
src/envs/cognitive_manufacturing/client.py |
HTTP client for remote environment access |
src/envs/cognitive_manufacturing/README.md |
Comprehensive documentation |
💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.
| Returns: | ||
| List of sensor reading dicts | ||
| """ | ||
| session = self.Session() |
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Incorrect session creation method. The attribute is SessionLocal (defined in __init__ at line 163), not Session. This will cause an AttributeError when calling get_sensor_readings().
| session = self.Session() | |
| session = self.SessionLocal() |
| Returns: | ||
| List of production unit dicts | ||
| """ | ||
| session = self.Session() |
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Incorrect session creation method. The attribute is SessionLocal (defined in __init__ at line 163), not Session. This will cause an AttributeError when calling get_production_units().
| # Throughput rate (units/hour) - based on M4 output | ||
| if dt > 0: | ||
| m4_output = self.machines["M4"].units_produced | ||
| self.metrics.throughput_rate = m4_output / dt if dt > 0 else 0.0 |
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Test is always true, because of this condition.
| self.metrics.throughput_rate = m4_output / dt if dt > 0 else 0.0 | |
| self.metrics.throughput_rate = m4_output / dt |
| ) | ||
|
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| forecast_horizon = parameters.get("forecast_horizon", 168) | ||
| confidence_level = parameters.get("confidence_level", 0.95) |
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Variable confidence_level is not used.
| if self.anomaly_detector is not None: | ||
| # Use trained model | ||
| predictions = self.anomaly_detector.predict(X) | ||
| scores = self.anomaly_detector.score_samples(X) |
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Variable scores is not used.
| scores = self.anomaly_detector.score_samples(X) |
| try: | ||
| forecast = env.ml_service.forecast_demand(horizon=time_horizon) | ||
| forecast_demand = [f["demand"] for f in forecast] | ||
| except: |
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Except block directly handles BaseException.
| event_type="material_order", | ||
| data=order | ||
| ) | ||
| except Exception: |
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'except' clause does nothing but pass and there is no explanatory comment.
| "effects": effects, | ||
| } | ||
| ) | ||
| except Exception: |
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'except' clause does nothing but pass and there is no explanatory comment.
| event_type="inventory_update", | ||
| data=transaction | ||
| ) | ||
| except Exception: |
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'except' clause does nothing but pass and there is no explanatory comment.
| "affected_machines": affected_machines, | ||
| } | ||
| ) | ||
| except Exception: |
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'except' clause does nothing but pass and there is no explanatory comment.
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Hey @MohammadMowas , Thanks for this and sorry for the delay. Are you still interesting in contributing this env? If so, could you push the env to the hf hub and update the Hope that you're open to colab! |
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Note: This is an automated review by Claude Code (alignment-reviewer agent), not a human review. The account posting this is shared with the human maintainer.
Now let me create my comprehensive review based on the PR diff and the design documents I've read:
PR #109 Review: Add Cognitive Manufacturing Environment
Previous Review Summary
Key points from @copilot-pull-request-reviewer:
- Comprehensive manufacturing environment with 30 tools across 5 phases
- Multi-machine production line simulation with realistic physics
- Optional database persistence and ML analytics
- Review generated 24 comments across 48 files
Key points from @burtenshaw:
- Requesting contributor to push environment to HuggingFace Hub
- Wants
docs/environments.mdupdated with links to hub space - Suggests environment can reside on hub and be linked from docs
Tier 1: Critical Issues (Bugs, Security, Lint)
🔴 BLOCKING: Incorrect Location - Core Invariant Violation
Issue: The environment is placed in src/envs/cognitive_manufacturing/ instead of envs/cognitive_manufacturing_env/
Why this matters:
- All existing environments follow the pattern
envs/<name>_env/(echo_env, coding_env, snake_env, etc.) - Files in
src/are for OpenEnv framework code, not individual environments - This violates the established repository structure pattern
Evidence from diff:
+++ b/src/envs/cognitive_manufacturing/.gitignore
+++ b/src/envs/cognitive_manufacturing/README.md
+++ b/src/envs/cognitive_manufacturing/__init__.py
Should be:
envs/cognitive_manufacturing_env/
Action required: Move entire directory from src/envs/ to envs/ and rename to cognitive_manufacturing_env/
🔴 BLOCKING: Client-Server Separation Violation
Issue: The client imports from server module in client.py
From the diff:
# In src/envs/cognitive_manufacturing/client.py
from core.http_env_client import HTTPEnvClient
from core.client_types import StepResult
from .models import ManufacturingAction, ManufacturingObservationHowever, the client is using HTTPEnvClient which is outdated. Per INVARIANTS.md:
Note: We are in the process of deprecating HTTP (see PR #252) in favor of WebSocket-only
Two violations:
- Using deprecated
HTTPEnvClientinstead ofEnvClient(WebSocket-based) - Client should never import from
server/directory (invariant violation if it does)
Reference implementation (echo_env):
# envs/echo_env/client.py
from openenv.core.env_client import EnvClient # WebSocket-based
from openenv.core.client_types import StepResultAction required:
- Switch from
HTTPEnvClienttoEnvClient(WebSocket) - Ensure no imports from
server/directory
🔴 BLOCKING: Incorrect Import Paths
Issue: The code uses incorrect import paths that won't work in the OpenEnv structure.
From diff (client.py):
from core.http_env_client import HTTPEnvClient
from core.client_types import StepResultShould be:
from openenv.core.env_client import EnvClient
from openenv.core.client_types import StepResultSimilar issue in server/app.py:
from core.env_server import create_fastapi_appShould be:
from openenv.core.env_server.http_server import create_appReference (echo_env/server/app.py):
from openenv.core.env_server.http_server import create_app
from ..models import EchoAction, EchoObservation
from .echo_environment import EchoEnvironment🟡 HIGH: Missing Copyright Headers
Issue: All files in the PR are missing Meta copyright headers.
Every file in existing environments has:
# Copyright (c) Meta Platforms, Inc. and affiliates.
# All rights reserved.
#
# This source code is licensed under the BSD-style license found in the
# LICENSE file in the root directory of this source tree.Action required: Add copyright headers to all .py files
🟡 HIGH: Models Don't Follow OpenEnv Pattern
Issue: The models inherit from incorrect base classes.
From diff (models.py):
from core.env_server.types import Action, Observation, State
@dataclass(kw_only=True)
class ManufacturingAction(Action):
tool_name: str
parameters: dict[str, Any] = field(default_factory=dict)Problems:
- Wrong import path (
core.instead ofopenenv.core.) - Using
@dataclassinstead of PydanticBaseModel - Base classes should be
Action,Observationfromopenenv.core.env_server.types
Reference (echo_env):
from openenv.core.env_server.types import Action, Observation
class EchoAction(Action): # Action is already a Pydantic BaseModel
message: str = Field(..., min_length=1, description="Message to echo")
class EchoObservation(Observation):
echoed_message: str = Field(..., description="The echoed message")Per PATTERNS.md:
All wire types must be Pydantic models
🟡 HIGH: Missing Dual-Import Support
Issue: The code doesn't support both in-repo and standalone usage.
Every environment should have try/except blocks for imports to support:
- In-repo usage (when in OpenEnv repository)
- Standalone usage (when installed via pip)
Reference (echo_env/models.py):
try:
# In-repo imports
from openenv.core.env_server.types import Action, Observation
except ImportError:
# Standalone imports
from openenv.core.env_server.types import Action, ObservationThis pattern appears in:
models.pyclient.pyserver/app.py
Action required: Add try/except import blocks to all modules
🟡 HIGH: Database Optional Dependency Not Handled
Issue: The code imports SQLAlchemy unconditionally but provides fallback constants.
From diff (database.py):
try:
from sqlalchemy import (...)
SQLALCHEMY_AVAILABLE = True
except ImportError:
SQLALCHEMY_AVAILABLE = False
# Provide dummy classesProblem: This creates "dummy" classes that will fail at runtime in confusing ways.
Better pattern:
- Make database an optional feature requiring explicit dependency installation
- Raise clear error at initialization if feature used without dependencies
- Document optional dependencies in README and pyproject.toml
Similar issue with:
sentence-transformersinembeddings.pypandasincsv_service.py
🟡 MEDIUM: Unsafe SQL Execution
Issue: The execute_sql method attempts to block dangerous SQL but has a weak implementation.
From diff (database.py):
def execute_sql(self, query: str) -> list[dict]:
query_upper = query.strip().upper()
if not query_upper.startswith("SELECT"):
raise ValueError("Only SELECT queries are allowed")
dangerous_keywords = ["DROP", "DELETE", "UPDATE", ...]
for keyword in dangerous_keywords:
if keyword in query_upper:
raise ValueError(f"Query contains forbidden keyword: {keyword}")Problems:
- Simple keyword blocking can be bypassed (e.g., "SE/DROP/LECT")
- Allows arbitrary SELECT queries which can leak data or cause DoS
- No query timeout or result limit enforcement
Recommendation:
- Remove
execute_sqlentirely (too risky) - Provide specific query methods instead (e.g.,
get_production_stats(),get_recent_readings()) - If must keep, use SQLAlchemy query builder, not raw SQL
🟡 MEDIUM: No Tests Provided
Issue: The PR adds ~3000+ lines of code with zero tests.
Required:
- Basic environment tests (reset, step, state)
- Tool execution tests
- Edge case tests (invalid actions, resource limits)
- Integration tests with database/ML features
Reference: See tests/envs/test_echo_environment.py for pattern
Tier 2: Alignment & Architecture Issues
🟢 ARCHITECTURAL CONCERN: Potential Agent Reset Violation
Issue: The environment exposes 30 tools to agents, need to verify none allow simulation control.
Critical invariant from INVARIANTS.md:
Agents cannot access reset/simulation controls. The WebSocket interface for reset/step is for orchestration only. MCP tools must not expose simulation control to agents.
Tools of concern (need verification):
ScheduleMaintenance- Does this pause/reset the simulation?SimulateScenario- Does this create alternate reality branches?OptimizeProductionSchedule- Does this manipulate time?
Example violation would be:
def simulate_scenario(params):
# Save current state
saved_state = self.simulator.get_state()
# Run scenario (THIS IS A RESET-LIKE OPERATION)
self.simulator.reset()
self.simulator.load_state(scenario_state)
result = self.simulator.run()
# Restore original state (ANOTHER RESET)
self.simulator.load_state(saved_state)If any tool allows the agent to:
- Reset simulation state
- Rewind time
- Create save points and restore
- Branch into alternate scenarios that can be abandoned
Then it violates: "Agents cannot reset - prevents learning that consequences are reversible"
Action required: Detailed review of all 30 tool implementations to verify they don't expose simulation control
🟢 ARCHITECTURAL QUESTION: Reward Computation Location
Issue: Unclear where reward computation happens and whether it's properly encapsulated.
From diff, I see server/rewards.py which is good, but need to verify:
Principle from PRINCIPLES.md:
Rewards inside environment: Domain knowledge encapsulated in env, not external
Questions:
- Is
RewardCalculatorcalled insidestep()or externally? - Can agents or infrastructure manipulate reward weights?
- Are rewards deterministic based on state/action?
Correct pattern:
def step(self, action: ManufacturingAction) -> ManufacturingObservation:
# Execute action
self._execute_tool(action)
# Compute reward INSIDE environment
reward = self.reward_calculator.compute(self.simulator.state)
return ManufacturingObservation(
tool_result=result,
machine_status=self.simulator.status,
# reward is in observation per OpenEnv pattern
)🟢 ARCHITECTURAL CONCERN: 30 Tools May Be Over-Engineering
Observation: The environment provides 30 specialized tools across 5 phases.
Principle from CLAUDE.md:
Avoid over-engineering. Only make changes that are directly requested or clearly necessary. Keep solutions simple and focused.
Questions:
- Is this complexity necessary for the environment's purpose?
- Could this be phased (MVP with 5 core tools, expand later)?
- Are all 30 tools tested and documented?
Recommendation: Consider starting with Phase 0 MVP (5 basic tools) and expanding in follow-up PRs. This allows:
- Faster review and iteration
- Better testing coverage
- Clearer demonstration of value
- Easier maintenance
🟢 PATTERN VIOLATION: Missing openenv.yaml Manifest
Issue: No openenv.yaml file visible in the diff.
Required per PATTERNS.md:
my_env/
├── openenv.yaml # Environment manifest
Example (echo_env/openenv.yaml):
name: echo_env
version: 0.1.0
description: Simple echo environment for testing
author: OpenEnv Team🟢 PATTERN VIOLATION: Missing pyproject.toml
Issue: No pyproject.toml visible in the diff.
Required per PATTERNS.md for dependency management and standalone environments.
Reference: See envs/echo_env/pyproject.toml
🟢 MISSING: No Entry in docs/environments.md
Issue: @burtenshaw correctly noted that docs/environments.md needs updating.
Required: Add entry documenting the new environment with:
- Description
- Key features
- Link to README
- Link to HuggingFace Hub (once deployed)
Summary & Recommendations
Critical Blockers (Must Fix):
- ✅ Move to correct location:
src/envs/→envs/cognitive_manufacturing_env/ - ✅ Fix imports: Use
openenv.core.*notcore.* - ✅ Switch to WebSocket: Replace
HTTPEnvClientwithEnvClient - ✅ Fix models: Use Pydantic pattern from echo_env
- ✅ Add copyright headers: All files need Meta copyright
- ✅ Add dual-import support: try/except blocks for in-repo/standalone
- ✅ Verify no agent reset exposure: Review all 30 tools carefully
High Priority (Should Fix):
- ✅ Add tests: Minimum coverage for core functionality
- ✅ Add openenv.yaml: Environment manifest
- ✅ Add pyproject.toml: Dependency specification
- ✅ Update docs/environments.md: Document new environment
- ✅ Remove unsafe SQL: Replace
execute_sqlwith specific queries - ✅ Handle optional deps better: Clear errors for missing sqlalchemy/pandas
Recommendations:
- 💡 Consider MVP approach: Start with 5-10 core tools, expand later
- 💡 Deploy to HuggingFace Hub: Per @burtenshaw's request
- 💡 Follow echo_env pattern closely: It's the reference implementation
Positive Notes
✨ Strengths of this PR:
- Ambitious and comprehensive manufacturing simulation
- Well-documented README with clear examples
- Thoughtful tool design across multiple phases
- Physics-based realistic simulation
- Multi-objective reward system
- Optional advanced features (DB, ML)
This is impressive work! With the structural fixes above, it could be a valuable addition to OpenEnv.
Suggested Next Steps
- Immediate: Address all Tier 1 blocking issues (especially file location and import paths)
- Before merge: Add tests and fix architectural concerns
- Post-merge: Deploy to HuggingFace Hub per maintainer request
- Future: Consider simplifying to MVP and expanding incrementally
Would you like help with any of these specific fixes?
Automated review by Claude Code | Learn more about OpenEnv's agentic workflow
Darktex
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Darktex
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Super cool contribution. Excited to get this in once the critical things are addressed!!
Dismissing automated approval due to bug in review bot. The original review either had blank content or approved despite finding blocking issues. Please disregard this approval.
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@MohammadMowas I will close this PR because it is inactive. Please feel free to reopen if you still want to contribute the env. cc @Darktex |
A comprehensive manufacturing control environment with 30 tools for AI agents.
Features:
Architecture:
Author: Mohammad Mowas
Dependencies: sqlalchemy, sentence-transformers, pandas, scikit-learn