agent-framework

v1.8.0 suspicious
5.0
Medium Risk

Microsoft Agent Framework for building AI Agents with Python. This package contains all the core and optional packages.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows signs of potential obfuscation and lacks complete author metadata, which raises concerns about its authenticity and purpose. While there is no concrete evidence of malicious intent, these factors combined suggest a need for caution.

  • High obfuscation risk
  • Incomplete author metadata
Per-check LLM notes
  • Network: The detection of network calls suggests the package might be designed to communicate with external services, which is not inherently malicious but should be reviewed for unexpected behavior.
  • Shell: No shell execution patterns detected, indicating low risk of direct system command execution.
  • Obfuscation: The frequent use of base64 decoding suggests potential obfuscation practices, raising suspicion but not conclusive proof of malice.
  • Credentials: No clear patterns indicating credential harvesting were detected, however, further analysis may be required.
  • Metadata: The author's information is incomplete, which may indicate a lack of transparency.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • t() http_client = httpx.AsyncClient(timeout=self._timeout_config, headers=headers) s
  • .http_client = http_client or httpx.AsyncClient(timeout=timeout) async def post_run( self,
⚠ Code Obfuscation score 10.0

Found 5 obfuscation pattern(s)

  • parts.append(Part(raw=base64.b64decode(base64_str), media_type=content.media_type or ""))
  • raw=base64.b64decode(base64_data), media_type=content
  • decoded_bytes = base64.b64decode(encoded_data) state = json.loads(dec
  • try: decoded = base64.b64decode(data, validate=True) return Content.from_data(da
  • ry: decoded = base64.b64decode(data) return Content.from_data(data=decoded,
✓ 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: microsoft.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository microsoft/agent-framework appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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-framework 
Create a personalized task management AI assistant using the 'agent-framework' package in Python. This mini-application will serve as a digital personal assistant that helps users manage their daily tasks, set reminders, and provide motivational messages based on the user's activity level. The application will integrate several key features to enhance user experience and productivity.

1. **Task Management**: Users can input tasks, set deadlines, and categorize them into different types such as work, personal, or leisure. The AI assistant will remind users of upcoming tasks via notifications or emails.
2. **Reminders and Notifications**: Implement a system where the AI sends reminders about upcoming tasks or events at specific times. Additionally, allow users to customize these reminders based on their preferences.
3. **Motivational Messages**: Based on the user's engagement with tasks, the AI will provide motivational messages to encourage productivity and maintain a positive attitude towards completing tasks.
4. **User Feedback Loop**: Incorporate a feedback mechanism where users can rate the effectiveness of the AI's suggestions or reminders. This feedback will help the AI improve its performance over time.
5. **Integration Capabilities**: Enable the application to sync with popular calendar applications like Google Calendar or Outlook to automatically import tasks and events.

**Utilizing 'agent-framework':** 
- Use the 'agent-framework' package to define the behavior and interactions of your AI assistant. Each feature (task management, reminders, motivational messages) can be modeled as an agent with specific roles and responsibilities within the framework.
- For instance, create a 'TaskAgent' that handles task creation, categorization, and reminders; a 'ReminderAgent' that triggers notifications based on predefined schedules; and a 'MotivationAgent' that provides encouragement based on user interaction patterns.
- Leverage the 'agent-framework' capabilities to enable these agents to communicate and collaborate effectively, ensuring seamless integration of all functionalities.
- Finally, implement a learning loop where the AI adjusts its behavior based on user feedback, enhancing the overall user experience continuously.