agentsonar

v0.7.0 suspicious
4.0
Medium Risk

Coordination intelligence for AI. Detection, prevention, governance, and FinOps across single agents, agents calling tools, MCP servers, multi-agent orchestrators, RAG pipelines, and custom buses. CrewAI, LangGraph, custom-orchestrator, and Claude Code adapters.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows some potential risks, particularly concerning shell execution and obfuscation techniques, which warrant closer scrutiny before full trust can be established.

  • Shell risk due to potential benign but unverified shell executions
  • Obfuscation risk from the use of pickle.loads for deserialization
Per-check LLM notes
  • Network: No network calls detected, indicating low risk.
  • Shell: Shell execution seems to be used for version checking and possibly other benign purposes, but requires further investigation into the context and purpose of these commands.
  • Obfuscation: The use of pickle.loads on engine_dict_bytes could indicate an attempt to hide the structure and content of the data, but it may also be used for legitimate purposes such as deserializing data.
  • Credentials: No patterns indicative of credential harvesting were found in the provided code snippet.
  • Metadata: The maintainer has only one package, which may indicate a new or less active account, raising some suspicion but not enough to conclusively label it as malicious.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: conftest.py
  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. conftest.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (23500 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

  • 315 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 17 commits in agentsonar/agentsonar
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ry: saved_dict = pickle.loads(engine_dict_bytes) except Exception: l
⚠ Shell / Subprocess Execution score 6.0

Found 3 shell execution pattern(s)

  • mixups. """ result = subprocess.run( [ sys.executable, "-m",
  • ort __version__ result = subprocess.run( [sys.executable, "-m", "agentsonar", "--version"],
  • errors. """ result = subprocess.run( [sys.executable, "-m", "agentsonar"], captu
✓ 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

Repository agentsonar/agentsonar appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AgentSonar" 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 agentsonar 
Create a mini-application named 'AgentWatcher' that leverages the 'agentsonar' package to monitor and manage multiple AI agents within a distributed system. This application should serve as a comprehensive dashboard for monitoring AI agent activities, ensuring compliance with governance policies, and optimizing costs through intelligent resource allocation.

**Core Features:**
1. **Agent Monitoring:** Real-time tracking of all active AI agents within the system, including their status, last activity timestamp, and any ongoing tasks.
2. **Governance Compliance:** Automated checks against predefined governance rules to ensure each agent adheres to established operational guidelines.
3. **Cost Optimization:** Implementation of a FinOps module that dynamically allocates resources based on agent activity levels, aiming to minimize costs without compromising performance.
4. **Custom Adapters Support:** Integration capabilities for various AI toolkits such as CrewAI, LangGraph, and custom orchestrators, allowing users to extend functionality.
5. **Alert System:** Notification system for critical events like policy violations, unexpected shutdowns, or significant cost overruns.

**How 'agentsonar' is Utilized:**
- Use 'agentsonar' to detect and classify different types of agents in the system, enabling tailored monitoring and management strategies.
- Leverage 'agentsonar' for governance enforcement by defining and applying rules that govern agent behavior, ensuring compliance with organizational standards.
- Implement 'agentsonar' to analyze usage patterns and predict future demand, facilitating proactive cost optimization measures.
- Employ 'agentsonar' to integrate seamlessly with diverse AI frameworks, enhancing the application's flexibility and adaptability.
- Utilize 'agentsonar' for real-time alerting mechanisms, providing immediate notifications on critical system events.

Your task is to design and develop this mini-application from scratch, ensuring it is modular, scalable, and easy to maintain. Additionally, provide detailed documentation and a user guide to facilitate deployment and operation.

💬 Discussion Feed

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