AI Analysis
Final verdict: SAFE
The package axio v0.9.7 presents minimal risks based on the analysis. It does not engage in network calls or shell executions, and there are no indications of credential harvesting. The obfuscation through base64 encoding is noted, but it's commonly used for media data and doesn't strongly suggest malicious activity.
- No network or shell execution detected
- Base64 encoding used for media data
- Low credential risk
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require external communications.
- Shell: No shell execution patterns detected, indicating no direct system command execution observed.
- Obfuscation: The code appears to be using base64 encoding for media data, which is common but could also indicate an attempt to obscure code logic.
- Credentials: No clear patterns indicative of credential harvesting were found.
- Metadata: The package shows some low-effort signs but lacks clear indicators of malicious intent.
Package Quality Overall: Medium (6.2/10)
✦ High
Test Suite
9.0
Test suite present — 26 test file(s) found
Test runner config found: conftest.pyTest runner config found: pyproject.toml26 test file(s) detected (e.g. conftest.py)
◈ Medium
Documentation
7.0
Some documentation present
Documentation URL: "Documentation" -> https://docs.axio-agent.comDetailed PyPI description (7219 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
304 type-annotated function signatures detected in source
✦ High
Multiple Contributors
8.0
Active multi-contributor project
3 unique contributor(s) across 100 commits in mosquito/axio-agentSmall but multi-author team (3–4 contributors)
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
Code Obfuscation
score 6.0
Found 3 obfuscation pattern(s)
type=data["media_type"], data=base64.b64decode(data["data"])) case "audio": return Auditype=data["media_type"], data=base64.b64decode(data["data"])) case "video": return Videtype=data["media_type"], data=base64.b64decode(data["data"])) case "tool_use": return T
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
Repository mosquito/axio-agent appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Author name is missing or very shortAuthor "" appears to have only 1 package on PyPI (new or inactive account)Package has no PyPI classifiers (low effort / metadata quality)
Known CVE Vulnerabilities
No known vulnerabilities found in OSV database.
AI App Starter Prompt
Use this prompt to build a project with axio
Create a Python-based mini-application named 'StreamyBot' that leverages the 'axio' library to provide a streaming-first interface for interacting with large language models (LLMs). This application will serve as a conversational agent capable of handling user queries in real-time, offering a seamless experience through continuous data streams. Your task includes the following steps: 1. **Setup**: Begin by installing the 'axio' package and any other necessary dependencies. Ensure your development environment is properly configured for Python. 2. **Design**: Sketch out the basic architecture of StreamyBot. It should include components for user input handling, LLM interaction, and output rendering. 3. **Implementation**: - **User Input Module**: Develop a module that captures user inputs either via command line or a simple web interface. - **LLM Interaction Module**: Use 'axio' to establish a connection with an LLM service provider. Utilize its streaming capabilities to receive responses in real-time. - **Output Rendering Module**: Implement functionality to display responses back to the user in a readable format. Consider enhancing this by adding features like voice synthesis for auditory feedback. 4. **Enhancements**: Integrate additional features such as context-awareness (keeping track of previous interactions), sentiment analysis on user inputs, and personalized responses based on user profiles. 5. **Testing & Debugging**: Thoroughly test your application to ensure it handles various types of inputs gracefully and provides accurate outputs. Address any bugs or performance issues identified during testing. 6. **Documentation**: Provide comprehensive documentation detailing setup instructions, usage guidelines, and API references for developers who might want to extend or modify StreamyBot. Throughout the project, emphasize the use of 'axio' for its unique streaming capabilities and protocol-driven design, highlighting how these features enhance the interactivity and efficiency of StreamyBot.
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