AI Analysis
Final verdict: SUSPICIOUS
The package shows low risk in terms of network, shell, and obfuscation activities. However, the metadata risk score is elevated due to incomplete author information and potential inactivity of the account, which raises suspicion.
- Incomplete author information
- Account appears new or inactive
Per-check LLM notes
- Network: No network calls detected, which is normal unless the package's functionality requires external communication.
- Shell: No shell execution patterns detected, indicating the package does not execute system commands without user interaction.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The author's information is incomplete and the account seems new or inactive, raising some suspicion but not conclusive evidence of malice.
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
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 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 agent-framework-orchestrations
Create a mini-application called 'TaskMaster' that leverages the 'agent-framework-orchestrations' package to manage and orchestrate various tasks in a smart home environment. TaskMaster should allow users to set up and manage different types of tasks such as turning lights on/off, adjusting thermostat settings, playing music, and more. The application should support both sequential and concurrent task execution based on user preferences. Step 1: Define a series of agents for each smart home device or action (e.g., LightAgent, ThermostatAgent, MusicPlayerAgent). Step 2: Use the SequentialBuilder from 'agent-framework-orchestrations' to create sequences of tasks that run one after another. For example, turn off all lights, then adjust the thermostat to a lower setting, and finally play soothing music. Step 3: Implement the ConcurrentBuilder to enable simultaneous actions. For instance, users could start multiple activities at once like opening the garage door while also starting a coffee machine. Step 4: Integrate the HandoffBuilder to handle scenarios where one task needs to wait for another to complete before proceeding. An example would be turning on the lights only after verifying if it's nighttime. Step 5: Utilize the GroupChatBuilder to simulate group discussions between agents for complex decision-making processes. This could involve deciding whether to activate security alarms based on motion detection and time of day. Step 6: Finally, implement the MagenticBuilder to provide a simple yet powerful way to define task flows using declarative statements, making it easier for users to configure their smart home routines without needing to understand the underlying orchestration logic. Features: - User-friendly interface for configuring task sequences and concurrent tasks. - Support for conditional task execution based on external inputs (e.g., time of day, sensor data). - Detailed logs and notifications for each task's status. - Save and load functionality for task configurations.