agentminds

v0.5.0 suspicious
6.0
Medium Risk

AgentMinds Python SDK — cross-site collective intelligence for production AI agents. Auto-capture errors + push/pull patterns from the network pool.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of obfuscation and has network and shell risks that require closer scrutiny. The maintainer's inactivity and lack of community engagement also raise concerns.

  • network calls to external APIs
  • potential obfuscation techniques
  • inactive maintainer
Per-check LLM notes
  • Network: Network calls to external APIs may be legitimate for telemetry or onboarding purposes but warrant further investigation into the destination and purpose.
  • Shell: Subprocess call to git is likely benign if used for version control purposes within the package's functionality, but could pose risks if misused.
  • Obfuscation: The observed pattern suggests potential obfuscation to hide the version or details of the agent, but without more context, it's unclear if this is malicious.
  • Credentials: No clear patterns indicating credential harvesting were detected.
  • Metadata: The maintainer seems new or inactive, and the repository lacks community engagement.

📦 Package Quality Overall: Medium (6.6/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • 3 test file(s) detected (e.g. test_cli.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://agentminds.dev/docs/python
  • Detailed PyPI description (8956 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

  • 122 type-annotated function signatures detected in source
✦ High Multiple Contributors 8.0

Active multi-contributor project

  • 3 unique contributor(s) across 9 commits in agentmindsdev/python-sdk
  • Small but multi-author team (3–4 contributors)

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • l}).encode("utf-8") req = urllib.request.Request( _ONBOARD_API + _ONBOARD_PATH, data=
  • , ) try: with urllib.request.urlopen(req, timeout=30) as resp: return json.lo
  • encode("utf-8") req = urllib.request.Request( self.dsn.ingest_url, data=b
  • try: with urllib.request.urlopen(req, timeout=HTTP_TIMEOUT_S) as resp:
  • "application/json" req = urllib.request.Request(url, data=data, headers=headers, method=method)
  • method) try: with urllib.request.urlopen(req, timeout=timeout) as r: payload = r.
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • -Agent": f"agentminds-python/{__import__('agentminds').__version__}", }, method="POST",
⚠ Shell / Subprocess Execution score 2.0

Found 1 shell execution pattern(s)

  • port subprocess out = subprocess.check_output( ["git", "rev-parse", "--short", "HEAD"],
✓ 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 score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AgentMinds" 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 agentminds 
Create a fully functional mini-application called 'ErrorGuardian' that leverages the capabilities of the 'agentminds' package to monitor and manage errors in a distributed system. This application should serve as a robust error management tool, enabling real-time capture, analysis, and resolution of errors across multiple sites and services. Here’s a detailed plan on how to build it:

1. **Setup Environment**: Begin by setting up your Python environment. Ensure you have Python 3.8 or higher installed. Install the necessary packages including 'agentminds'. Use pip to install 'agentminds'.

2. **Application Structure**: Design the application structure such that it includes modules for error capturing, analysis, and resolution. Each module will utilize specific functionalities provided by 'agentminds'.

3. **Error Capturing Module**: Implement a module that automatically captures errors occurring in any connected service or site. Utilize 'agentminds' auto-capture feature to ensure no error goes unnoticed. Configure settings within 'agentminds' to define which types of errors should be captured.

4. **Analysis Module**: Develop an analysis module that processes captured errors. This module should use 'agentminds' push/pull patterns to gather data from different nodes in the network pool. Analyze the collected data to identify common issues, trends, or anomalies that could indicate underlying problems in the system.

5. **Resolution Module**: Create a resolution module that suggests or implements fixes based on the analysis performed. This could involve restarting services, adjusting configurations, or alerting administrators. Use 'agentminds' to distribute these actions across the network efficiently.

6. **User Interface**: Design a simple web-based UI using Flask or Django that allows users to view error logs, perform searches, and manage alerts. Integrate 'agentminds' into this interface to provide real-time updates.

7. **Testing & Deployment**: Test the application thoroughly under various scenarios to ensure reliability. Deploy the application in a cloud environment like AWS or Azure, leveraging 'agentminds' to scale and manage resources effectively.

By following these steps, you will create a powerful tool that not only helps in identifying and resolving issues quickly but also enhances the overall resilience and performance of your distributed systems.