agentregistry

v0.1.0 suspicious
6.0
Medium Risk

The neutral cross-platform registry for AI agents

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows high metadata risk due to suspicious git repository activity and maintainer history, despite having low risks in network, shell, obfuscation, and credential aspects.

  • High metadata risk
  • No other significant risks detected
Per-check LLM notes
  • Network: The detected network call patterns are typical for packages that interact with external services or APIs, indicating legitimate functionality rather than malicious activity.
  • Shell: No shell execution patterns were detected, suggesting no risk related to executing system commands.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: High risk due to suspicious git repository activity and maintainer history.

📦 Package Quality Overall: Low (3.4/10)

○ 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://agentregistry.dev/docs
  • Detailed PyPI description (1975 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

  • 6 type-annotated function signatures (partial)
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 1 commits in aevonsystems/agentregistry-sdk
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • amework async with httpx.AsyncClient() as client: response = await client.post(
  • cing_per_call with httpx.Client() as client: response = client.post(
  • """ async with httpx.AsyncClient() as client: response = await client.get(
✓ Code Obfuscation

No obfuscation patterns detected

✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: agentregistry.dev>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 7.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • Very few commits: 1 total
  • Single contributor with only 1 commit(s) — possibly throwaway account
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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 agentregistry 
Your task is to develop a command-line tool using Python that leverages the 'agentregistry' package to manage AI agents across different platforms seamlessly. This tool will allow users to register new AI agents, query existing ones, update their information, and deregister them when they're no longer needed. The application should provide a robust interface for interacting with the 'agentregistry', ensuring that all operations are performed efficiently and securely.

Key Features:
1. Register New Agents: Users should be able to add new AI agents into the registry with essential details such as name, type, platform, and contact information.
2. Query Agents: Implement a feature that allows querying of agents based on various criteria like name, type, or platform.
3. Update Agent Information: Provide functionality to modify the details of registered agents if necessary.
4. Deregister Agents: Enable users to remove agents from the registry when they are no longer required.
5. Secure Operations: Ensure that all interactions with the 'agentregistry' are secure, possibly through encryption or other security measures.
6. User-Friendly CLI: Design a clean, intuitive command-line interface that guides users through the process of managing their AI agents easily.
7. Error Handling: Implement comprehensive error handling to provide meaningful feedback in case of issues during any operation.

The 'agentregistry' package is central to this application. It provides the core functionalities required for registering, querying, updating, and deregistering AI agents. Your job is to integrate these functionalities into a cohesive and user-friendly tool that showcases the capabilities of 'agentregistry'.