ak-agentbase

v0.1.0a1 suspicious
4.0
Medium Risk

Unified AI agent configuration, observability, and model management for multi-agent applications

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has minimal risks associated with network calls, shell execution, obfuscation, and credential harvesting. However, its recent creation and lack of maintainer history raise concerns about potential supply-chain attacks.

  • Low risk in network, shell execution, obfuscation, and credential aspects.
  • Suspiciously new package with no maintainer history.
Per-check LLM notes
  • Network: No network calls detected, which is typical and not indicative of malicious activity.
  • Shell: Shell execution appears to be for package validation purposes, which is common but should be reviewed for legitimacy and permissions.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows signs of being newly created and lacks maintainer history, which raises suspicion.

📦 Package Quality Overall: Low (4.8/10)

✦ High Test Suite 9.0

Test suite present — 10 test file(s) found

  • Test runner config found: pyproject.toml
  • 10 test file(s) detected (e.g. test_cli.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/Shivam1904/AgentBase/blob/main/README.md
  • Detailed PyPI description (7742 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

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

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

  • h code 0.""" result = subprocess.run( [ "python", "-m
  • h code 1.""" result = subprocess.run( [ "python", "-m
  • nts.yaml.""" result = subprocess.run( ["python", "-m", "agentbase", "validate"],
  • elp text.""" result = subprocess.run( ["python", "-m", "agentbase", "validate", "--he
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.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 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 ak-agentbase 
Create a mini-application called 'Multi-Agent Task Manager' using the Python package 'ak-agentbase'. This application will allow users to manage multiple AI agents performing different tasks in a unified environment. Here are the steps and features you need to implement:

1. **Setup Environment**: Begin by installing the necessary packages including 'ak-agentbase'. Ensure your application can handle multiple agents, each configured for specific tasks.

2. **Agent Configuration**: Utilize 'ak-agentbase' to configure various AI agents. Each agent should have its own unique set of parameters and capabilities. For example, one agent could be responsible for natural language processing tasks, while another handles image recognition.

3. **Task Assignment**: Implement a feature where users can assign tasks to these agents through a simple command-line interface. Users should be able to specify which task they want an agent to perform, and the application should ensure the correct agent is selected based on its capabilities.

4. **Observability**: Use 'ak-agentbase' to monitor the performance and status of each agent in real-time. Display metrics such as task completion rate, response time, and error rates for each agent.

5. **Model Management**: Allow users to update or switch models for any given agent dynamically without restarting the application. This feature should be managed through 'ak-agentbase', ensuring seamless integration and minimal downtime.

6. **Logging and Reporting**: Integrate logging and reporting functionalities to keep track of all operations performed by the agents. Logs should include details about the tasks executed, errors encountered, and any other relevant information.

7. **User Interface**: Develop a user-friendly command-line interface for interacting with the Multi-Agent Task Manager. This interface should allow users to view current statuses, assign new tasks, and manage agent configurations easily.

8. **Documentation**: Provide comprehensive documentation explaining how to install and use the Multi-Agent Task Manager, including examples of common use cases.

By following these steps and implementing these features, you'll create a robust, scalable, and easy-to-use tool for managing multiple AI agents in a single application, leveraging the power of 'ak-agentbase'.

💬 Discussion Feed

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