AI Analysis
Final verdict: SUSPICIOUS
The package shows moderate network interaction risks and incomplete metadata, raising concerns about its reliability and potential misuse.
- moderate network risk
- incomplete author information
Per-check LLM notes
- Network: The presence of network calls suggests the package interacts with external services, which is not inherently malicious but requires scrutiny to ensure it's not used for unauthorized access or data exfiltration.
- Shell: No shell execution patterns were detected.
- Obfuscation: Base64 decoding is commonly used for data serialization and may not indicate malicious intent.
- Credentials: No patterns indicative of credential harvesting were detected.
- Metadata: The author information is incomplete, suggesting potential unreliability.
Package Quality Overall: Medium (6.4/10)
◈ Medium
Test Suite
6.0
Partial test coverage signals detected
Test runner config found: pyproject.toml
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://docs.agno.comDetailed PyPI description (4172 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
464 type-annotated function signatures detected in source
✦ High
Multiple Contributors
10.0
Active multi-contributor project
39 unique contributor(s) across 100 commits in agno-agi/agnoActive community — 5 or more distinct contributors
Heuristic Checks
Outbound Network Calls
score 6.0
Found 4 network call pattern(s)
olved_api_key()} with httpx.Client(timeout=self.timeout) as client: response = cliewritten = 0 with httpx.Client(timeout=self.timeout, follow_redirects=True) as client:) async with httpx.AsyncClient(timeout=self.timeout) as client: response = awai) async with httpx.AsyncClient(timeout=self.timeout) as client: async with clie
Code Obfuscation
score 2.0
Found 1 obfuscation pattern(s)
decoded_content = base64.b64decode(model_response_event.audio.content)
Shell / Subprocess Execution
No shell execution patterns detected
Credential Harvesting
No credential harvesting patterns detected
Typosquatting
No typosquatting candidates detected
Registered Email Domain
Email domain looks legitimate: agno.com>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository agno-agi/agno appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author name is missing or very shortAuthor "" 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 agno
Create a simple task management application using the 'agno' package. This application will allow users to create tasks, assign them to different categories, set due dates, mark tasks as completed, and view their progress. The application should also support adding notes and reminders to tasks. Steps: 1. Set up your development environment with Python and install the 'agno' package. 2. Design the user interface for adding, editing, and viewing tasks. 3. Implement functionality to add new tasks with fields for title, category, due date, and notes. 4. Add the ability to edit existing tasks and update their details. 5. Implement a feature to mark tasks as completed and show the completion status. 6. Include a calendar view to display upcoming tasks based on their due dates. 7. Allow users to add reminders for specific tasks at certain times. 8. Integrate a search function to find tasks by title or category. How 'agno' is Utilized: - Use 'agno' to define the behavior of each task object, including actions like setting a due date, marking it as completed, and adding notes. Each task should act as an agent capable of managing its state and reacting to user commands. - Leverage 'agno' to implement reminders and notifications, allowing tasks to automatically notify users when they are due or overdue. - Utilize 'agno' for the calendar view, enabling tasks to dynamically adjust their visibility based on their due dates and completion status.
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