airframe-agents

v0.8.0 suspicious
5.0
Medium Risk

Vendor-neutral agent runtime: one protocol, pluggable adapters for Claude Code, GitHub Copilot, Moonshot Kimi, AWS Bedrock, the OpenCode HTTP agent server, and OpenAI-compatible gateways.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package has a moderate risk score due to potential credential harvesting and limited author metadata. While the absence of network and shell risks is positive, the credential retrieval mechanism raises concerns.

  • High credential risk
  • Limited author metadata
Per-check LLM notes
  • Network: No network calls detected, which is not necessarily suspicious but should be assessed based on the package's intended functionality.
  • Shell: No shell execution detected, indicating low risk for direct system command execution.
  • Obfuscation: The observed byte sequences could be part of a binary data or encoded content, but without context, it's hard to determine if it's malicious obfuscation.
  • Credentials: The code patterns indicate an attempt to retrieve AWS credentials from environment variables, which could be legitimate, but also suggests potential risk for credential harvesting if not properly secured.
  • Metadata: The package shows some red flags due to low activity and an author with limited history, but there's no direct evidence of malicious intent.

📦 Package Quality Overall: Medium (6.2/10)

✦ High Test Suite 9.0

Test suite present — 15 test file(s) found

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

Some documentation present

  • Documentation URL: "Documentation" -> https://github.com/get2knowio/airframe/tree/main/docs
  • Detailed PyPI description (16864 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 7.0

Partial type annotation coverage

  • Type checker (mypy / pyright / pytype) referenced in project
  • 707 type-annotated function signatures detected in source
◈ Medium Multiple Contributors 6.0

Limited contributor diversity

  • 2 unique contributor(s) across 84 commits in get2knowio/airframe
  • Two distinct contributors found

🔬 Heuristic Checks

✓ Outbound Network Calls

No suspicious network call patterns found

⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • 00\x01\x00\x00\x00\x01" b"\x08\x06\x00\x00\x00\x1f\x15\xc4\x89\x00\x00\x00\rIDATx\x9cc\x00\x01" b"\x00\x00\x05\x00\x01\r\n-\xb4\x0
✓ Shell / Subprocess Execution

No shell execution patterns detected

⚠ Credential Harvesting score 10.0

Found 6 credential access pattern(s)

  • LT_BEDROCK_MODEL region = os.environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION") if not
  • .environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION") if not region: print("BedrockRu
  • override env_region = os.environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION") if
  • .environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION") if env_region: return e
  • ( github_token or os.environ.get("GITHUB_TOKEN") or os.environ.get("GH_TOKEN") ) self._cli
  • BedrockRuntime: region = os.environ.get("AWS_REGION") or os.environ.get("AWS_DEFAULT_REGION") if not
✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

No author email provided

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 2.5

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Airframe 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 airframe-agents 
Develop a versatile chatbot application named 'MultiAgentChat' that leverages the 'airframe-agents' Python package to integrate various AI agents seamlessly. This application will allow users to interact with different AI services such as Claude Code, GitHub Copilot, Moonshot Kimi, AWS Bedrock, and OpenAI-compatible models through a unified interface. The goal is to provide a platform where developers can test, compare, and utilize these AI services without needing to set up separate configurations for each service.

### Key Features:
1. **User Interface**: A simple, intuitive web-based UI that allows users to select the AI service they want to interact with.
2. **Agent Selection**: Users can choose from a list of supported AI agents available through 'airframe-agents'. Each selection should dynamically load the appropriate adapter.
3. **Real-time Interaction**: The application should support real-time text-based interaction between the user and the selected AI agent.
4. **History Management**: Maintain a history of interactions for each session, allowing users to review past conversations.
5. **Custom Prompting**: Allow users to input custom prompts or questions to the AI agents.
6. **Logging & Analytics**: Implement basic logging and analytics to track usage patterns and performance metrics.

### Utilizing 'airframe-agents':
- **Initialization**: Use 'airframe-agents' to initialize the connection to the chosen AI service based on the user's selection.
- **Adapter Configuration**: Dynamically configure the appropriate adapter for the selected AI service using 'airframe-agents'.
- **Communication**: Leverage 'airframe-agents' to send user inputs and receive responses from the AI service.
- **Error Handling**: Implement robust error handling mechanisms to manage any issues encountered during communication with the AI service.

### Steps to Build the Application:
1. **Setup Project Environment**: Install necessary dependencies including 'airframe-agents', Flask (for web development), and any additional libraries required for web UI.
2. **Define Routes & Views**: Create routes in Flask to handle requests for selecting AI agents and sending/receiving messages.
3. **Integrate 'airframe-agents'**: Write code to initialize and manage connections to different AI services using 'airframe-agents'. Ensure that the correct adapter is loaded based on the user's choice.
4. **Implement User Interface**: Develop a clean, responsive UI using HTML/CSS/JavaScript to facilitate interaction with the AI services.
5. **Testing & Debugging**: Test the application thoroughly to ensure smooth interaction with all supported AI services. Address any bugs or performance issues identified during testing.
6. **Deployment**: Deploy the application to a cloud platform like Heroku or AWS to make it accessible online.

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