agentchaos-reliability

v0.1.0 suspicious
4.0
Medium Risk

Open-source reliability testing for tool-using AI agents: record, profile cost, detect regressions before production.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has some metadata risks and low activity, raising concerns about its authenticity and security practices. However, it does not exhibit clear signs of malicious intent.

  • Non-secure links in metadata
  • Low repository activity
Per-check LLM notes
  • Network: The network calls appear to be related to internal health checks and resetting server states, which is not inherently suspicious but should be reviewed against the package's documentation and intended use.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk.
  • Credentials: No credential harvesting patterns detected, indicating low risk.
  • Metadata: The package shows some red flags including a non-secure link and low activity in the repository, but lacks strong indicators of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • e: self._client = httpx.AsyncClient( timeout=target.timeout_s, h
  • ine: try: httpx.get(f"http://127.0.0.1:{port}/health", timeout=0.5)
  • baseline at rag_chunks=5 httpx.post(f"{server}/control/reset") sc_baseline = tmp_path / "bas
  • rio, different rag_chunks httpx.post(f"{server}/control/reset") sc_candidate = tmp_path / "ca
  • ine: try: httpx.get(url, timeout=0.5) return except Exceptio
  • ounter['n']}")) client = httpx.AsyncClient(transport=httpx.MockTransport(handler), timeout=5) sc =
✓ 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

No author email provided

⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8080/chat
⚠ 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 4.0

2 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • Author "AgentChaos 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 agentchaos-reliability 
Develop a small-scale application named 'ReliabilityGuard' that leverages the 'agentchaos-reliability' package to ensure the robustness of AI agents before they are deployed in production environments. This utility will serve as a critical tool for developers and DevOps teams to test and monitor AI agents under various conditions, ensuring they perform reliably and efficiently. Here’s a detailed breakdown of what your application should achieve:

1. **Setup and Configuration**: Begin by setting up a basic Python environment where 'agentchaos-reliability' is installed. Configure your application to accept inputs such as the AI agent's API endpoint, the types of tests to run (e.g., latency tests, failure injection), and any custom parameters for these tests.
2. **Recording Agent Behavior**: Implement functionality to record the behavior of the AI agent during different tests. This includes logging actions taken by the agent, responses received, and any errors encountered. Use the recording capabilities provided by 'agentchaos-reliability' to capture comprehensive data about the agent's performance.
3. **Cost Profiling**: Integrate cost profiling into your application using 'agentchaos-reliability'. This feature should allow you to monitor and report on the financial costs associated with running the AI agent under various conditions. Developers should be able to see how different scenarios affect the operational costs of their AI solutions.
4. **Regression Detection**: Utilize the regression detection tools within 'agentchaos-reliability' to identify any performance drops or changes in behavior that could indicate issues before they reach production. Your application should generate alerts when significant deviations from expected performance are detected.
5. **Reporting and Visualization**: Finally, implement a reporting module that summarizes the results of all tests conducted. This report should include visualizations of key metrics such as response times, error rates, and cost profiles. The goal is to provide actionable insights that help improve the reliability and efficiency of AI agents.

By following these steps, you'll create a powerful tool that not only tests but also optimizes AI agents, ensuring they are ready for real-world deployment.