🤖 AI Analysis

Final verdict: SAFE

The package shows minimal risk indicators, with only network and metadata risks noted as moderately high. These do not strongly suggest a supply-chain attack.

moderate network risk
single package maintainer

Per-check LLM notes

Network: The presence of network calls is common in packages that require external communication, but unusual naming and patterns should be reviewed for legitimacy.
Shell: No shell execution patterns were detected, which is normal and indicates no direct system command execution.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The maintainer has only one package, which could indicate a new or less active account.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

) try: async with httpx.AsyncClient(timeout=120.0) as client: async def _do_post()
" try: async with httpx.AsyncClient(timeout=5.0) as client: resp = await client.get(
: try: async with httpx.AsyncClient(timeout=5.0) as client: resp = await client.get(
: try: async with httpx.AsyncClient(timeout=10.0) as client: resp = await client.get
: try: async with httpx.AsyncClient(timeout=15.0) as client: resp = await client.pos
: try: async with httpx.AsyncClient(timeout=15.0) as client: resp = await client.get

✓ 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

Repository agentbreeder/agentbreeder appears legitimate

⚠ Maintainer History score 2.0

1 maintainer concern(s) found

Author "AgentBreeder Contributors" 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 agentbreeder

Your task is to create a versatile mini-application that leverages the 'agentbreeder' Python package to demonstrate its capabilities in defining once and deploying anywhere while automatically governing the agents. This application will simulate a simple yet effective scenario where agents manage tasks across different environments, ensuring efficient workload distribution and automated governance based on predefined rules.

### Project Overview:
- **Name:** TaskMaster
- **Purpose:** To showcase 'agentbreeder's ability to define agents once and deploy them across multiple environments with automatic governance.
- **Key Features:**
- Define a set of tasks that need to be executed.
- Distribute these tasks among agents running in different simulated environments.
- Implement automatic governance mechanisms to ensure optimal task execution based on environmental conditions.
- Monitor and log the performance of each agent and the overall system.

### Steps to Build the Application:
1. **Setup Environment:** Ensure you have Python installed along with the 'agentbreeder' package. Use virtual environments to keep dependencies organized.
2. **Define Agents:** Utilize 'agentbreeder' to define your agents. These agents will represent task executors capable of running in various simulated environments (e.g., cloud, local machine).
3. **Task Definition:** Create a list of tasks that your agents will execute. Tasks can vary in complexity and resource requirements.
4. **Deployment Strategy:** With 'agentbreeder', deploy these agents across different simulated environments. Each environment should have its own set of constraints and capabilities.
5. **Governance Mechanism:** Implement a basic governance mechanism using 'agentbreeder'. This mechanism should dynamically allocate tasks to agents based on current load and environmental conditions.
6. **Monitoring & Logging:** Set up logging to monitor the performance of each agent and the overall system. This includes tracking task completion times, errors, and resource usage.
7. **Testing:** Thoroughly test your application under various scenarios to ensure it behaves as expected.
8. **Documentation:** Write comprehensive documentation explaining how to use your application, including setup instructions and examples.

### Additional Suggestions:
- Consider adding a user-friendly interface for easier interaction with the application.
- Explore integrating additional monitoring tools for real-time performance analysis.
- Experiment with different governance strategies to see how they impact overall system efficiency.