AI Analysis
Final verdict: SAFE
The package presents a low risk profile with no detected network calls, obfuscation, or credential harvesting. The shell risk is moderately high due to potential command-line interactions, but this does not necessarily indicate malicious intent.
- moderate shell execution risk
- new maintainer account
Per-check LLM notes
- Network: No network calls detected.
- Shell: Shell executions may be legitimate for command-line tool interaction but could indicate potential execution of arbitrary commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.
Package Quality Overall: Medium (5.0/10)
○ Low
Test Suite
1.0
No test suite detected
No test files or test-runner configuration detected
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://agentsumo.readthedocs.ioDetailed PyPI description (3390 chars)
○ Low
Contributing Guide
4.0
No contributing guide or governance files found
Development Status classifier >= Beta
◈ Medium
Type Annotations
5.0
Partial type annotation coverage
96 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
4 unique contributor(s) across 42 commits in mw-jeong/AgentSUMOSmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
No obfuscation patterns detected
Shell / Subprocess Execution
score 10.0
Found 6 shell execution pattern(s)
ile).name}") result = subprocess.run(cmd, capture_output=True, text=True) if result.retuact (bbox: {bbox})") subprocess.run([ "osmium", "extract", f"--bbox={bboy take a while)...") subprocess.run([ "python3", osmget_path, f"--bbox={le).name}") result = subprocess.run([ duarouter_path, "-n", net_file_abs' ] result = subprocess.run(randomTrips_cmd, capture_output=True, text=True, check=Falsey_path("polyconvert") subprocess.run([ polyconvert_path, "-n", net_file,
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
No author email provided
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository mw-jeong/AgentSUMO appears legitimate
Maintainer History
score 2.0
1 maintainer concern(s) found
Author "Minwoo Jeong, Jeeyun Chang, Yoonjin Yoon" appears to have only 1 package on PyPI (new or inactive account)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with agentsumo-mcp
Create a real-time traffic management system using the 'agentsumo-mcp' package, which serves as a middleware between SUMO traffic simulation software and AI-driven decision-making systems. Your task is to develop a web-based application that allows users to design simple road networks, define vehicle routes, simulate traffic scenarios, and analyze the outcomes in real-time. This application will enable users to input parameters such as number of vehicles, road conditions, and time periods, then observe the effects on traffic flow and congestion levels. Key Features: 1. User Interface: Develop an intuitive web interface where users can draw road networks and add vehicles with specific routes. 2. Simulation Control: Users should be able to start, pause, and stop simulations directly from the UI. 3. Real-Time Visualization: Implement a visualization component that updates in real-time during the simulation, showing traffic density, vehicle speeds, and other metrics. 4. Data Analysis: Provide tools for analyzing post-simulation data, including average speed, travel times, and congestion points. 5. Integration with 'agentsumo-mcp': Utilize the 'agentsumo-mcp' package to manage the interaction between the SUMO simulation engine and your application. Use it to send commands for network configuration, trip definition, and simulation control, as well as to receive live data streams for visualization and analysis. Steps to Build the Application: 1. Set up the development environment with necessary Python packages, including 'agentsumo-mcp'. 2. Design the web interface using a frontend framework like React or Vue.js. 3. Implement backend services using Flask or Django to handle user inputs and interact with 'agentsumo-mcp'. 4. Configure SUMO and integrate 'agentsumo-mcp' to manage simulation workflows. 5. Develop real-time visualization components using libraries like D3.js or Plotly. 6. Implement data analysis functions to process and present simulation results. 7. Test the application thoroughly to ensure all features work as expected.