AI Analysis
Final verdict: SAFE
The package shows minimal risks with no network calls, shell executions, or credential harvesting. The only notable concern is the metadata risk due to a new maintainer and missing classifiers.
- Low risk scores across all categories
- Metadata risk due to new maintainer and missing classifiers
Per-check LLM notes
- Network: No network calls detected, which is normal if the package does not require internet connectivity.
- Shell: No shell execution patterns detected, indicating no immediate signs of executing system commands.
- Obfuscation: No obfuscation patterns detected, indicating low risk.
- Credentials: No credential harvesting patterns detected, indicating low risk.
- Metadata: Low risk due to lack of red flags, but new maintainer and missing classifiers suggest potential low effort or inactive status.
Heuristic Checks
Outbound Network Calls
No suspicious network call patterns found
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 yaogdu/AgentLedger appears legitimate
Maintainer History
score 4.0
2 maintainer concern(s) found
Author "AgentLedger Contributors" 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 agentledger-langgraph
Create a fully-functional mini-application that leverages the 'agentledger-langgraph' Python package to manage a simple knowledge graph for a digital library system. This application will allow users to add books, authors, and their relationships into a LangGraph-compatible knowledge graph managed by AgentLedger. Here are the key steps and features for your application: 1. **Setup**: Begin by installing the 'agentledger-langgraph' package along with any other necessary dependencies. Ensure you have an environment set up where AgentLedger and LangGraph services are accessible. 2. **User Interface**: Develop a basic command-line interface (CLI) for user interaction. This CLI should allow users to perform CRUD (Create, Read, Update, Delete) operations on the knowledge graph nodes representing books and authors. 3. **Adding Books and Authors**: Implement functionality to add new books and authors into the knowledge graph. Each book node should include metadata like title, publication year, and ISBN. Author nodes should contain information such as name and birth date. Additionally, establish relationships between books and their respective authors. 4. **Querying Information**: Allow users to query the knowledge graph for specific books or authors based on provided criteria. For example, find all books published after a certain year, or retrieve all works by a particular author. 5. **Updating and Deleting Entries**: Provide options to update existing entries (such as correcting a publication year) and delete entries from the knowledge graph when they are no longer needed. 6. **Visualization**: Optionally, implement a feature that visualizes the knowledge graph, showing connections between books and authors. This could be done using a simple ASCII graph or a more advanced visualization tool if time permits. In each of these steps, utilize the 'agentledger-langgraph' package to interact with the knowledge graph. Specifically, use its methods to create nodes, establish relationships, query data, and modify the graph structure. By the end of this project, you should have a functional mini-application that demonstrates the power of integrating 'agentledger-langgraph' into real-world applications.