agentbay

v1.8.1 suspicious
5.0
Medium Risk

Persistent memory for AI agents. Store, recall, and share knowledge across sessions.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows potential risks related to shell command execution and network calls, raising concerns about its legitimacy and safety. Further investigation is required to confirm its intentions.

  • Shell risk due to potential execution of arbitrary commands.
  • Network risk due to external URL calls requiring further investigation.
Per-check LLM notes
  • Network: The network calls to external URLs may be used for legitimate purposes like checking for updates or telemetry, but require further investigation into their intent and the data being transmitted.
  • Shell: Executing shell commands can be risky if not properly sanitized or intended to perform unauthorized actions. This pattern suggests potential execution of arbitrary commands which could indicate a security risk.
  • Obfuscation: No obfuscation patterns detected.
  • Credentials: The code snippet suggests potential credential harvesting, but could also be legitimate usage of environment variables for AWS configuration.
  • Metadata: The missing repository and the new/inactive maintainer raise concerns about potential malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • ib.request resp = urllib.request.urlopen("https://pypi.org/pypi/agentbay/json", timeout=3)
  • ][0]}" ) with urllib.request.urlopen(callback_url, timeout=5) as response: as
  • -> str: http = session or requests.Session() response = http.post(f"{base_url.rstrip('/')}/api/v1/a
  • Result: http = session or requests.Session() if prompt_for_telemetry: maybe_prompt_for_ins
  • eout self._session = requests.Session() self._session.headers.update( {
  • nt: http = session or requests.Session() try: http.post( f"{bas
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • () try: result = subprocess.run(cmd) if result.returncode == 0: print("\
  • .") try: result = subprocess.run(cmd) return result.returncode except FileNotFoun
⚠ Credential Harvesting score 2.5

Found 1 credential access pattern(s)

  • drock( aws_region=os.environ.get("AWS_REGION", "us-east-1"), ) call_kwargs: Dict[
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: aiagentsbay.com>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 agentbay 
Create a personalized daily briefing app using Python and the 'agentbay' package. This app will aggregate news, weather, and personal reminders into a single, concise summary tailored to the user's preferences. The app should have the following core functionalities:

1. User Profile Management: Allow users to create profiles where they specify their interests, preferred topics for news, and locations for weather updates.
2. Daily Briefing Generation: Each day, the app should generate a personalized briefing based on the user's profile. This includes relevant news articles, weather forecasts, and personal reminders.
3. Persistent Memory: Use 'agentbay' to store user preferences, past briefings, and any other relevant information persistently. This allows the app to remember user choices over multiple sessions and improve the relevance of future briefings.
4. Sharing and Collaboration: Users should be able to share their briefings with others via email or social media platforms. Additionally, users can collaborate by sharing their favorite sources or topics with friends.
5. Customization Options: Provide options for users to customize the content of their briefings further, such as adding or removing categories, adjusting the frequency of reminders, etc.
6. Notifications: Implement push notifications to alert users when their daily briefing is ready.

The 'agentbay' package will play a crucial role in storing and recalling user data across sessions, ensuring that the app can provide increasingly personalized briefings over time. By leveraging 'agentbay', the app aims to offer a seamless, efficient way for users to stay informed and organized.