agentmesh-ai

v0.3.0a2 suspicious
5.0
Medium Risk

Async multi-agent framework with traceability, observability, dashboarding, and extensibility.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risks due to potential shell execution and network interactions, though there is no clear evidence of malicious intent or credential harvesting.

  • moderate shell execution risk
  • network interaction risk
Per-check LLM notes
  • Network: Network calls appear to be part of normal API interactions but should be reviewed for the destination URL and data being sent.
  • Shell: Shell execution patterns may indicate automated testing or internal tooling, but could also suggest potential for executing arbitrary code which requires further investigation.
  • Obfuscation: The use of __import__ and json.dumps/loads might indicate an attempt to evade simple static analysis, but could also be used for legitimate purposes such as dynamic module loading and data serialization.
  • Credentials: No clear patterns indicating credential harvesting were detected.

📦 Package Quality Overall: Medium (5.0/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/raghuece455/AgentMesh/tree/main/docs
  • Detailed PyPI description (13495 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

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

Single-author or unverifiable project

  • 1 unique contributor(s) across 6 commits in raghuece455/AgentMesh
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • sonObject: http_request = urllib.request.Request(url, data=body, headers=headers, method="POST")
  • "POST") try: with urllib.request.urlopen(http_request, timeout=timeout_seconds) as response:
  • ) monkeypatch.setattr("urllib.request.urlopen", fake_urlopen) provider = OpenAICompatibleProvi
  • ne) monkeypatch.setattr("urllib.request.urlopen", rate_limited) with pytest.raises(AgentMeshErro
  • ne) monkeypatch.setattr("urllib.request.urlopen", auth_failed) with pytest.raises(AgentMeshError
  • w") monkeypatch.setattr("urllib.request.urlopen", timeout) with pytest.raises(AgentMeshError) as
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • s"]: try: __import__(module) checks["optional_dependencies"][module] = True
  • metadata_path.write_text(__import__("json").dumps(self._documents, indent=2), encoding="utf-8") de
  • xists(): loaded = __import__("json").loads(metadata_path.read_text(encoding="utf-8"))
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • path("src")} completed = subprocess.run( [sys.executable, "-m", "agentmesh.cli", "--db", str
  • abspath("src")} export = subprocess.run( [sys.executable, "-m", "agentmesh.cli", "--db", str
  • env=env, ) imported = subprocess.run( [sys.executable, "-m", "agentmesh.cli", "--db", str
  • =env, ) otel_export = subprocess.run( [ sys.executable, "-m",
  • env=env, ) doctor = subprocess.run( [sys.executable, "-m", "agentmesh.cli", "--db", str
  • env=env, ) version = subprocess.run( [sys.executable, "-m", "agentmesh.cli", "version"],
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

⚠ Suspicious Page Links score 4.0

Found 2 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8790
  • Non-HTTPS external link: http://127.0.0.1:8787
⚠ Git Repository History score 2.5

Git history flags: All 6 commits happened within 24 hours

  • All 6 commits happened within 24 hours
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "AgentMesh Contributors" 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 agentmesh-ai 
Develop a real-time monitoring and analytics tool for a simulated distributed system using the 'agentmesh-ai' Python package. This tool will help users understand the performance and health of their system components in real-time. The application should include the following features:

1. **Component Simulation**: Simulate a few key components of a distributed system, such as a database, web server, and cache.
2. **Performance Metrics Collection**: Collect performance metrics from each component, including latency, throughput, error rates, and resource usage.
3. **Real-Time Dashboard**: Display these metrics on a dynamic dashboard that updates in real-time, allowing users to monitor the health and performance of each component.
4. **Traceability**: Implement traceability to track requests as they flow through different components, helping identify bottlenecks and issues.
5. **Alert System**: Set up an alert system that notifies users when certain thresholds are breached, indicating potential problems in the system.
6. **Customizable Agents**: Allow users to define custom agents to monitor additional components or specific metrics not covered by default.
7. **Extensibility**: Ensure the application is easily extendable to support new components and metrics without major code changes.

**Utilizing 'agentmesh-ai'**:
- Use 'agentmesh-ai' to manage the asynchronous communication between different components and collect performance data efficiently.
- Leverage its traceability features to ensure that all request paths are logged and can be reviewed for performance analysis.
- Employ the observability features to create comprehensive logs and metrics that can be used for the real-time dashboard.
- Utilize the dashboarding capabilities to visualize collected data and provide actionable insights to users.
- Take advantage of 'agentmesh-ai's extensibility to allow for easy integration of new components and customization options.

This project will serve as a practical example of how 'agentmesh-ai' can be used to build complex, real-time monitoring tools for distributed systems.