agentpulse-cli

v1.2.1 suspicious
6.0
Medium Risk

Real-time AI Agent activity dashboard — sessions, tokens, tools, costs at a glance

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to its network and shell execution behaviors, suggesting potential for unintended or harmful actions. Further investigation is recommended.

  • High shell risk indicating potential for system modification
  • Moderate network risk requiring verification of legitimate usage
Per-check LLM notes
  • Network: Network calls suggest external interactions which may be legitimate depending on the package's functionality, but require further investigation to confirm.
  • Shell: Shell execution patterns indicate the package performs actions that could modify or interact with the system, raising concerns about potential misuse or unintended behavior.
  • Obfuscation: The use of __import__ to dynamically import modules may indicate an attempt to hide or delay the import process, but it's not conclusive evidence of malicious intent.
  • Credentials: No patterns indicative of credential harvesting were detected.

📦 Package Quality Overall: Low (3.8/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

  • 1 test file(s) detected (e.g. test_agent_log_sources.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (12564 chars)
○ Low Contributing Guide 2.0

No contributing guide or governance files found

  • No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found
◈ Medium Type Annotations 5.0

Partial type annotation coverage

  • 364 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

  • No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 7.5

Found 5 network call pattern(s)

  • mbed]}).encode() req = urllib.request.Request( url, data=payload, heade
  • ) try: resp = urllib.request.urlopen(req, timeout=10) return resp.status in (200
  • locks}).encode() req = urllib.request.Request( url, data=payload, heade
  • ) try: resp = urllib.request.urlopen(req, timeout=10) return resp.status == 200
  • }).encode() req = urllib.request.Request( url, data=payload, heade
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • ): try: __import__(module) results.append(CheckResult(f"Dependency: {labe
  • ): try: __import__(module) results.append(CheckResult(f"Optional: {label}
  • eturn { "timestamp": __import__("datetime").datetime.now(__import__("datetime").timezone.utc).isoformat
  • rt__("datetime").datetime.now(__import__("datetime").timezone.utc).isoformat(), "hours": hours,
  • datetime.now(timezone.utc) - __import__("datetime").timedelta(hours=since_hours) for log_dir in self
⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • try: r = subprocess.run( f"git {cmd}".split(), cwd=path, capture_ou
  • try: r = subprocess.run( ["find", str(path), "-name", "*.py", "-o",
  • return 0 r2 = subprocess.run(["wc", "-l"] + files[:100], capture_output=True, text=True,
  • try: r = subprocess.run( ["find", str(path), "-name", "test_*.py",
✓ 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

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agentpulse-cli 
Your task is to develop a real-time dashboard application using the Python package 'agentpulse-cli'. This application will provide insights into the activities of AI agents, such as session logs, token usage, tools employed, and associated costs. The goal is to create a user-friendly interface where users can monitor these metrics in real-time, allowing them to optimize their AI operations effectively.

### Application Overview:
- **Name:** AgentPulse Dashboard
- **Purpose:** To visualize and manage AI agent activities in real-time.

### Key Features:
1. **Real-Time Monitoring:** Display current AI agent activities, including ongoing sessions, active tools, and token usage.
2. **Historical Data Analysis:** Provide graphs and charts to analyze past agent activities, showing trends over time.
3. **Cost Management:** Track and display the cost associated with each agent's operation, helping users understand financial implications.
4. **Custom Alerts:** Allow users to set up alerts based on specific conditions, such as high token usage or unusual tool activity.
5. **User Interface:** Design an intuitive UI with clear visuals and easy navigation.

### Utilizing 'agentpulse-cli':
- Use 'agentpulse-cli' to fetch real-time data about AI agent sessions, token usage, tools, and costs.
- Implement 'agentpulse-cli' commands to integrate historical data retrieval for analysis purposes.
- Leverage 'agentpulse-cli' functionalities to trigger custom alerts based on predefined criteria.

### Development Steps:
1. **Setup Environment:** Ensure you have Python installed, then install 'agentpulse-cli' via pip.
2. **Data Fetching:** Write scripts to periodically fetch real-time data from 'agentpulse-cli'.
3. **Data Storage:** Decide on a method to store fetched data temporarily for real-time and historical analysis.
4. **UI Design:** Choose a suitable framework for your UI, such as Flask or Django for backend, and React or Vue.js for frontend.
5. **Integration:** Integrate 'agentpulse-cli' functionalities into your application, ensuring seamless data flow and updates.
6. **Testing:** Conduct thorough testing to ensure all features work as expected and data is accurately displayed.
7. **Deployment:** Prepare your application for deployment, considering hosting options like AWS, Heroku, or Google Cloud Platform.

### Deliverables:
- A fully functional real-time dashboard application.
- Documentation explaining setup, configuration, and use of the application.
- Sample screenshots and a demo video showcasing key features.

By completing this project, you'll gain valuable experience in integrating third-party packages, developing real-time applications, and creating user-friendly interfaces.