agent-coderag

v1.3.0 suspicious
5.0
Medium Risk

Lightweight semantic code search and distillation utility for AI coding agents. It solves the API knowledge gap via real-time local signature extraction and intent analysis without PyTorch. Optimized for token efficiency, it compresses codebase context into compact semantic summaries stored in a local DuckDB vector similarity index.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits moderate risk due to potential misuse of subprocess calls and undocumented network activities, which could suggest potential vulnerabilities or malicious intent.

  • High shell risk due to subprocess calls
  • Undocumented network GET requests
Per-check LLM notes
  • Network: GET requests with streaming might be legitimate if the package requires external resources, but could indicate data exfiltration if not properly documented.
  • Shell: Subprocess calls to Java tools like 'javap' and jar operations may serve valid purposes, such as code analysis or package inspection; however, they can also be used for executing arbitrary commands, suggesting potential for misuse.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting safe handling of secrets.
  • Metadata: The author details are sparse, suggesting a potentially new or less active maintainer.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • try: response = requests.get(url, stream=True, timeout=30) response.raise_for
✓ Code Obfuscation

No obfuscation patterns detected

⚠ Shell / Subprocess Execution score 4.0

Found 2 shell execution pattern(s)

  • target_jar) result = subprocess.run( [jar_bin, "-tf", str(target_jar)],
  • continue res = subprocess.run( [javap_bin, "-public", "-classpath", str(ta
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: gmail.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

Repository naranor/agent-coderag appears legitimate

⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author name is missing or very short
  • Author "" 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 agent-coderag 
Create a mini-application named 'CodeSearcher' that leverages the 'agent-coderag' Python package to enable users to search and retrieve relevant code snippets from a given codebase based on their queries. The application should follow these steps:

1. **Initialization**: Set up a clean virtual environment and install the 'agent-coderag' package alongside other necessary dependencies such as DuckDB.
2. **Codebase Indexing**: Develop a function that takes a directory path as input and indexes all Python files within it using 'agent-coderag'. This process involves extracting semantic signatures and storing them in a DuckDB database for fast lookup.
3. **Query Processing**: Implement a query interface where users can input natural language descriptions of what they're looking for in terms of code functionality. Utilize 'agent-coderag' to translate these descriptions into search queries that can be executed against the indexed codebase.
4. **Result Retrieval & Display**: Upon receiving a query, use 'agent-coderag' to perform a semantic search across the indexed codebase and return the most relevant code snippets along with their file locations. Present these results in a user-friendly manner.
5. **Enhancements**: Consider adding features like fuzzy matching for more robust search capabilities, support for multiple programming languages, and integration with version control systems to allow searching through historical code changes.

This project aims to showcase the power of 'agent-coderag' in bridging the gap between human intent and machine-executable code searches, making it easier for developers to find the exact code they need without manually browsing through repositories.