agenthub-python

v0.3.3 safe
3.0
Low Risk

AgentHub is the LLM API Hub for the Agent era, built for high-precision autonomous agents.

🤖 AI Analysis

Final verdict: SAFE

The package appears to be legitimate with minor concerns about obfuscation and metadata. There is no strong evidence of malicious activity.

  • Moderate obfuscation risk due to base64 encoding
  • Lack of detailed metadata and PyPI classifiers
Per-check LLM notes
  • Network: The observed network call patterns are typical for fetching resources or making API calls, which could be legitimate depending on the package's functionality.
  • Shell: No shell execution patterns were detected, indicating no immediate risk associated with executing arbitrary commands.
  • Obfuscation: The usage of base64 encoding and decoding suggests some level of obfuscation, but it may also be used for legitimate purposes such as data encryption or transmission.
  • Credentials: No clear patterns indicating credential harvesting were detected.
  • Metadata: The package shows some red flags such as a single package from the author and lack of PyPI classifiers, but no direct evidence of malicious intent.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 4.5

Found 3 network call pattern(s)

  • drock: async with httpx.AsyncClient() as client: response = await client.get(url
  • else: async with httpx.AsyncClient() as client: response = await client.get(url
  • eturn url async with httpx.AsyncClient() as client: response = await client.get(url)
⚠ Code Obfuscation score 6.0

Found 3 obfuscation pattern(s)

  • image_bytes = base64.b64decode(base64_string) else: raise Value
  • ata, str): return base64.b64decode(data.encode("utf-8")) return data def _build_wa
  • byte_count = len(base64.b64decode(data.encode("utf-8"))) else: byte_count
✓ 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 score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:25751/tracer/`.
✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "PrismShadow" appears to have only 1 package on PyPI (new or inactive account)
  • Package has no PyPI classifiers (low effort / metadata quality)
✓ Known CVE Vulnerabilities

No known vulnerabilities found in OSV database.

💡 AI App Starter Prompt

Use this prompt to build a project with agenthub-python 
Create a Python-based chatbot application named 'AgentAssistant' using the 'agenthub-python' package. This application will serve as a personal assistant capable of understanding natural language commands and executing tasks accordingly. It will leverage the precision and autonomy provided by the AgentHub platform to handle a wide array of user requests efficiently.

**Features:**
1. **Task Execution:** Users can ask the chatbot to perform various tasks such as setting reminders, scheduling meetings, sending emails, and more.
2. **Contextual Understanding:** The chatbot should be able to understand the context of conversations, remembering previous interactions and using them to better assist the user.
3. **Customization Options:** Allow users to customize their experience by setting preferences for notifications, task priorities, and more.
4. **Integration Capabilities:** The chatbot should integrate with external services like calendar apps, email clients, and other APIs.
5. **Learning and Improvement:** Implement a feature where the chatbot learns from user interactions to improve its responses and task execution over time.

**Steps to Build the Application:**
1. **Setup Environment:** Install Python and necessary libraries including 'agenthub-python'.
2. **Design User Interface:** Create a simple and intuitive UI where users can interact with the chatbot.
3. **Integrate 'agenthub-python':** Use the 'agenthub-python' package to connect to the AgentHub platform and enable the chatbot's autonomous capabilities.
4. **Develop Core Functions:** Write functions that allow the chatbot to execute common tasks based on user commands.
5. **Implement Contextual Memory:** Develop a system to store and recall contextual information from previous conversations.
6. **Add Customization Features:** Provide options for users to tailor their chatbot experience.
7. **Connect External Services:** Integrate the chatbot with third-party services to expand its functionality.
8. **Incorporate Learning Mechanism:** Include a feedback loop where the chatbot's performance improves with each interaction.
9. **Testing and Debugging:** Thoroughly test the application to ensure it works as expected and fix any bugs.
10. **Deployment:** Once tested, deploy the application so users can access it via web or mobile interfaces.

💬 Discussion Feed

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