🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low individual risks but raises concerns due to the newly established repository with limited activity.

New and inactive repository
Low individual risk scores across multiple categories

Per-check LLM notes

Network: No network calls detected, which is normal if the package does not require external communications.
Shell: No shell execution detected, indicating the package does not execute system commands.
Obfuscation: No obfuscation patterns detected, indicating low risk.
Credentials: No credential harvesting patterns detected, indicating low risk.
Metadata: The repository is new with no activity indicators, suggesting potential unreliability.

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ 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

All external links appear legitimate

⚠ Git Repository History score 5.0

Git history flags: Repository created very recently: 7 day(s) ago (2026-05-30T10:52:27Z)

Repository created very recently: 7 day(s) ago (2026-05-30T10:52:27Z)
Repository has zero stars and zero forks

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "agent-panorama 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 agent-panorama

Create a Python-based mini-application named 'AgentInsight' that leverages the 'agent-panorama' package to transform complex agent traces from Langfuse/LangSmith into user-friendly reports. This application will serve as a tool for developers and data analysts to easily understand the performance and behavior of their agents without needing deep technical knowledge.

**Application Requirements:**
1. **Integration with Langfuse/LangSmith**: Ensure that 'AgentInsight' can connect to Langfuse/LangSmith APIs to fetch agent traces.
2. **Report Generation**: Utilize 'agent-panorama' to convert these traces into both Markdown and HTML formats, making them accessible and easy to read.
3. **Customization Options**: Allow users to customize report templates and include specific metrics they are interested in.
4. **Visualization Tools**: Incorporate simple visualization tools (e.g., charts) to help users quickly grasp key insights from the data.
5. **User Interface**: Develop a basic web interface using Flask or Django where users can select which agent traces to analyze and view the generated reports.
6. **Security Measures**: Implement necessary security measures such as authentication to protect user data and privacy.

**Steps to Build 'AgentInsight':**
1. Set up your development environment with Python, Flask/Django, and the required packages including 'agent-panorama'.
2. Design a RESTful API that interacts with Langfuse/LangSmith to retrieve agent traces.
3. Use 'agent-panorama' to process these traces and generate human-readable reports in both Markdown and HTML formats.
4. Create customizable templates for the reports allowing users to highlight different aspects of the agent activity.
5. Integrate visualization libraries (such as Matplotlib or Plotly) to enhance the readability of the reports.
6. Develop a front-end using Flask/Django to provide an intuitive interface for users to interact with 'AgentInsight'.
7. Test the application thoroughly to ensure it meets all requirements and functions correctly.
8. Deploy the application on a server or cloud platform for public access.

This project aims to demonstrate the power of 'agent-panorama' in simplifying complex agent trace data and making it accessible to a wider audience.