ai-token-tracker

v0.2.5 suspicious
6.0
Medium Risk

Python SDK for capturing LLM traffic and ingesting envelopes into Ai Token Tracker

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits a high network risk due to potential unauthorized data transmission, which raises concerns about its intended use. Additionally, the lack of a public repository and sole ownership of the package by the maintainer adds to the uncertainty.

  • High network risk (7/10) due to potential unauthorized data transmission.
  • No public git repository available, increasing suspicion.
Per-check LLM notes
  • Network: The observed network calls suggest potential unauthorized data transmission, possibly indicating an unexpected behavior for a package named 'ai-token-tracker'.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets and credentials.
  • Metadata: The maintainer has only one package and the git repository is not found, raising suspicion but not conclusive evidence of malice.

📦 Package Quality Overall: Medium (5.6/10)

✦ High Test Suite 9.0

Test suite present — 5 test file(s) found

  • Test runner config found: pyproject.toml
  • 5 test file(s) detected (e.g. test_ingestion_client_integration.py)
◈ Medium Documentation 7.0

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/ai-token-tracker/ai-token-tracker/tree/ma
  • Detailed PyPI description (7060 chars)
○ Low Contributing Guide 4.0

No contributing guide or governance files found

  • Development Status classifier >= Beta
◈ Medium Type Annotations 7.0

Partial type annotation coverage

  • Classifier: Typing :: Typed
  • 63 type-annotated function signatures detected in source
○ Low Multiple Contributors 1.0

Could not retrieve contributor data from GitHub

  • GitHub API error: 404

🔬 Heuristic Checks

⚠ Outbound Network Calls score 6.0

Found 4 network call pattern(s)

  • nterception() response = requests.post( f"{base_url}/v1/responses", headers={"Autho
  • ) try: response = requests.post( f"{base_url}/v1/responses", headers
  • http_interception() with httpx.Client(timeout=5.0) as httpx_client: response = httpx_clien
  • () -> int: async with httpx.AsyncClient(timeout=5.0) as httpx_client: response = await h
✓ 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 2.0

1 maintainer concern(s) found

  • Author "Ai Token Tracker" 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 ai-token-tracker 
Create a real-time chat application that integrates with large language models (LLMs) and utilizes the 'ai-token-tracker' Python package to monitor and analyze the interaction between users and the AI. This application should allow users to engage in conversations with an AI-powered chatbot while simultaneously tracking the usage of tokens consumed during these interactions.

### Features:
- **User Interface**: A simple web interface where users can type messages and receive responses from the AI.
- **Token Tracking**: Utilize the 'ai-token-tracker' package to capture all the communication traffic between the user and the AI model. Track the number of tokens used for each request and response.
- **Usage Analytics**: Display analytics on the web interface showing the total number of tokens used by the user in the session, the average tokens per message, and other relevant metrics.
- **Real-Time Updates**: Ensure that token usage data is updated in real-time as users interact with the AI.
- **Security**: Implement basic security measures such as user authentication to protect user data.
- **Customizable Model**: Allow users to choose different LLMs for their conversations.

### Steps to Create the Application:
1. **Setup Environment**: Install necessary packages including 'ai-token-tracker', Flask for the web server, and any required LLM APIs.
2. **Design UI**: Design a clean and intuitive user interface using HTML/CSS/JavaScript.
3. **Integrate LLM API**: Set up integration with an LLM API to handle the chatbot responses.
4. **Implement Token Tracking**: Use the 'ai-token-tracker' package to capture and process the token usage data.
5. **Develop Analytics Module**: Develop a module to calculate and display analytics about token usage.
6. **Ensure Real-Time Updates**: Implement mechanisms to ensure that the analytics dashboard updates in real-time as the conversation progresses.
7. **Add Security Measures**: Implement user authentication to secure user data.
8. **Testing**: Thoroughly test the application to ensure all features work as expected.
9. **Deployment**: Deploy the application to a cloud platform like Heroku or AWS.

💬 Discussion Feed

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