🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows low risks in direct security aspects like shell execution and obfuscation but has a notable metadata risk due to the unavailability of the repository and limited maintainer history, which raises suspicion.

Metadata risk score is 4 out of 10
Repository not found and maintainer has limited history

Per-check LLM notes

Network: The observed network calls are typical for packages that need to communicate with external services for configuration or health checks.
Shell: No shell execution patterns were detected, indicating no immediate risk from this aspect.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
Credentials: No credential harvesting patterns detected, suggesting secure handling of secrets.
Metadata: The repository is not found and the maintainer has limited history, suggesting potential risk.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

iguration" async with httpx.AsyncClient() as client: disco = await client.get(discovery_
try: async with httpx.AsyncClient() as client: resp = await client.get(check.u
y: async with httpx.AsyncClient() as client: await client.post(url, json

✓ 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 3.0

Repository not found (deleted or private)

Repository not found (deleted or private)

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

Only one version has ever been released — brand new package
Author "Sedge5 Technology Solutions" 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 agentflow-sdk

Create a mini-application named 'ChatFlow' that leverages the 'agentflow-sdk' package to facilitate intelligent communication within a multi-agent system. This application will enable users to set up conversations between multiple agents, each capable of performing specific tasks or roles. The goal is to demonstrate the core functionalities of 'agentflow-sdk', including but not limited to message routing, task delegation, and real-time communication.

Step-by-Step Instructions:
1. **Setup Environment**: Ensure Python 3.x is installed and create a virtual environment. Install the 'agentflow-sdk' package using pip.
2. **Define Agents**: Create at least three distinct agents within the system: a 'Dispatcher', a 'Responder', and a 'Logger'. Each agent should have unique capabilities defined by its role.
3. **Message Routing**: Utilize 'agentflow-sdk' to define rules for how messages are routed between agents based on their content or metadata.
4. **Task Delegation**: Implement a mechanism where the 'Dispatcher' agent can delegate tasks to other agents based on predefined criteria.
5. **Real-Time Communication**: Enable real-time communication between agents using 'agentflow-sdk' features, allowing for dynamic interaction and response handling.
6. **Logging Mechanism**: The 'Logger' agent should capture all communications and actions taken by other agents, storing them for later analysis or auditing purposes.
7. **User Interface**: Develop a simple command-line interface (CLI) that allows users to initiate conversations, view logs, and manage agents.
8. **Testing & Validation**: Test the application thoroughly to ensure that all agents communicate effectively and that the logging mechanism captures all interactions accurately.
9. **Documentation**: Provide comprehensive documentation detailing how to install, configure, and use 'ChatFlow', as well as an explanation of how 'agentflow-sdk' is integrated into the application.

Suggested Features:
- Support for adding custom agents with specific functionalities.
- A dashboard-like feature within the CLI for monitoring active conversations and agent statuses.
- Enhanced logging options, such as filtering logs by agent or timestamp.
- Integration with external systems for data input/output.

This project aims to showcase the versatility and power of 'agentflow-sdk' in managing complex communication flows within a multi-agent ecosystem.