🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to the presence of shell execution patterns, despite having low risks in other areas. This suggests potential for risky behavior or a backdoor.

High shell risk
Low network and obfuscation risks

Per-check LLM notes

Network: No network calls detected, indicating low risk in this area.
Shell: Detection of shell execution patterns suggests potential for executing arbitrary commands, which may indicate risky behavior or a backdoor.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
Credentials: No credential harvesting patterns detected, suggesting safe handling of sensitive information.
Metadata: The maintainer has only one package, which may indicate a new or less active account, but no other red flags are present.

📦 Package Quality Overall: Medium (5.0/10)

◈ Medium Test Suite 6.0

Partial test coverage signals detected

Test runner config found: pyproject.toml

◈ Medium Documentation 7.0

Some documentation present

Documentation URL: "Documentation" -> https://github.com/agento-cc/agento/tree/main/docs
Detailed PyPI description (7609 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

266 type-annotated function signatures detected in source

◈ Medium Multiple Contributors 5.0

Limited contributor diversity

1 unique contributor(s) across 100 commits in agento-cc/agento
Single author but highly active (100 commits)

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 8.0

Found 4 shell execution pattern(s)

if none set.""" result = subprocess.run(["crontab", "-l"], capture_output=True, text=True) if re
_run: return True subprocess.run(["crontab", "-"], input=new_crontab, text=True, check=True)
try: result = subprocess.run( ["/opt/cron-agent/run.sh", "workspace:build
one: try: subprocess.run( ["/opt/cron-agent/run.sh", "replay", str(jo

✓ 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 agento-cc/agento appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "Marcin Klauza" 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 agento-core

Create a mini-application called 'AI Task Orchestrator' that leverages the 'agento-core' package to manage and automate complex workflows involving multiple AI services. This application will serve as a bridge between different AI models hosted in Docker containers, enabling users to define, schedule, and monitor task sequences that utilize these models. Here’s a detailed breakdown of the project scope and features:

1. **Project Scope**:
- The application will allow users to define tasks that involve invoking one or more AI services (e.g., image recognition, sentiment analysis).
- Each task will be executed within a Docker container, ensuring isolation and reproducibility.
- Users will have the ability to chain tasks together to form workflows, where the output of one task serves as input for another.
- A web-based UI will provide a visual representation of the workflow, including real-time status updates.

2. **Features**:
- **Task Definition**: Users can specify tasks by selecting from a predefined list of AI services or by adding custom services.
- **Workflow Creation**: Users can create workflows by arranging tasks in a sequence, with options to add conditional logic (e.g., if sentiment is positive, proceed to next task).
- **Scheduling**: Workflows can be scheduled to run at specific times or intervals.
- **Monitoring & Logging**: Real-time monitoring of task execution status and logs for troubleshooting.
- **Docker Container Management**: Utilizing 'agento-core', the application will handle the creation, management, and removal of Docker containers required for each task.

3. **Implementation Steps**:
- Step 1: Set up a Flask backend to handle API requests for task and workflow management.
- Step 2: Integrate 'agento-core' to manage Docker containers for executing AI tasks.
- Step 3: Develop a frontend using React or Vue.js to provide a user-friendly interface for defining and managing workflows.
- Step 4: Implement real-time monitoring using WebSockets to update the UI with task statuses.
- Step 5: Add support for scheduling workflows using a cron-like scheduler.
- Step 6: Test the application thoroughly with various workflows to ensure reliability and efficiency.

This project aims to demonstrate the power of 'agento-core' in orchestrating complex AI workflows and highlights its potential in simplifying the development of AI-driven applications.