AI Analysis
Final verdict: SUSPICIOUS
The package exhibits low risk in terms of network usage, shell execution, obfuscation, and credential harvesting. However, it is newly published and lacks detailed author information, raising suspicion.
- Brand new package
- Limited author information
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 the package does not execute system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: The package shows some red flags such as being brand new and having an author with limited information, but no clear signs of malicious intent.
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: ieee.org>
Suspicious Page Links
All external links appear legitimate
Git Repository History
Repository venkatapgummadi/agentflow appears legitimate
Maintainer History
score 6.0
3 maintainer concern(s) found
Only one version has ever been released — brand new packageAuthor 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 agentflow-orchestrator
Develop a mini-application named 'API Orchestrator' that leverages the 'agentflow-orchestrator' package to streamline the process of managing and orchestrating multiple enterprise APIs. This application will serve as a central hub for invoking different APIs based on user requests, handling complex workflows involving multiple API calls, and ensuring seamless data flow between these APIs. Here’s a detailed breakdown of the steps and features you need to implement: 1. **Setup Environment**: Begin by setting up your Python environment with all necessary packages including 'agentflow-orchestrator'. Ensure you have access to at least three different APIs from various domains such as weather, news, and finance. 2. **Define Agents**: Utilize 'agentflow-orchestrator' to define agents for each API. Each agent should be capable of initiating API calls, processing responses, and handling exceptions. For instance, create an agent named 'WeatherAgent' that fetches current weather data, another named 'NewsAgent' for fetching top headlines, and a 'FinanceAgent' for financial market updates. 3. **Workflow Definition**: Define workflows using 'agentflow-orchestrator’ that involve chaining these agents together. For example, create a workflow that first fetches the latest weather update, then retrieves top news stories relevant to that weather condition, and finally provides financial insights related to the weather and news. This workflow should demonstrate how 'agentflow-orchestrator' manages dependencies between agents and ensures that subsequent actions only proceed once previous ones are completed successfully. 4. **User Interface**: Develop a simple command-line interface (CLI) for interacting with your application. Users should be able to select from predefined workflows or input custom queries that trigger specific workflows or individual API calls. 5. **Error Handling & Logging**: Implement robust error handling mechanisms within your agents and workflows to manage failures gracefully. Additionally, log all interactions and errors to a file for future analysis and debugging. 6. **Configuration Management**: Allow users to configure API keys and other settings through a configuration file. This makes it easier to switch between development, testing, and production environments without hardcoding sensitive information. 7. **Documentation**: Provide clear documentation detailing how to set up and use the application, including examples of how to extend it with additional agents and workflows. By completing this project, you will gain hands-on experience with 'agentflow-orchestrator', understand its capabilities in managing complex API interactions, and learn best practices for developing multi-agent systems in Python.