🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential unauthorized network activity and low maintainer activity, raising concerns about its integrity and security.

Moderate network risk
Low maintainer activity

Per-check LLM notes

Network: The presence of network calls is expected for an SDK that likely communicates with a service, but further investigation is needed to ensure it's not being used for unauthorized data transfer.
Shell: No shell execution patterns detected, which is normal and indicates no immediate risk of local system compromise.
Obfuscation: No obfuscation patterns detected, indicating low risk of malicious obfuscation.
Credentials: No credential harvesting patterns detected, indicating low risk of malicious credential theft.
Metadata: The package shows signs of low maintainer activity and poor metadata quality, which could indicate it's not actively maintained or monitored.

📦 Package Quality Overall: Low (3.6/10)

✦ High Test Suite 9.0

Test suite present — 8 test file(s) found

8 test file(s) detected (e.g. test_api.py)

○ Low Documentation 1.0

No documentation detected

No documentation URL, doc files, or meaningful description found

○ Low Contributing Guide 2.0

No contributing guide or governance files found

No CONTRIBUTING, CODE_OF_CONDUCT, or governance files found

◈ Medium Type Annotations 5.0

Partial type annotation coverage

71 type-annotated function signatures detected in source

○ Low Multiple Contributors 1.0

Unable to verify contributor count: no GitHub repository found

No GitHub repository linked — contributor count unavailable

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

ent_id self._client = httpx.Client(timeout=30) def save(self, content: str, tags: list[str
= False self._http = httpx.Client( base_url=self._base_url, headers={"

✓ 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

No GitHub repository linked

No GitHub repository link found

⚠ Maintainer History score 6.0

3 maintainer concern(s) found

Author name is missing or very short
Author "" 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 agentrecall-sdk

Your task is to develop a conversational memory management system for a chatbot using the 'agentrecall-sdk' Python package. This system will enhance the chatbot's ability to recall previous interactions and maintain context across multiple conversations. The application will be named 'ChatRecall' and will have the following core functionalities:

1. **User Interaction Logging**: When users interact with the chatbot, their messages and the chatbot's responses should be logged into the system.
2. **Contextual Recall**: If a user resumes a conversation after a break, the chatbot should be able to recall previous messages and provide relevant responses based on past interactions.
3. **User Identification**: Users should be able to log in, allowing the chatbot to maintain separate logs for each user.
4. **Query Interface**: Provide a simple query interface where users can ask the chatbot about previous conversations, such as 'What did I say last week?' or 'Remind me of our last discussion.'
5. **Data Privacy**: Implement measures to ensure that user data is not shared between different users and is securely stored.

To achieve these functionalities, you will utilize the 'agentrecall-sdk' package as follows:
- Use the 'MemoryManager' class from 'agentrecall-sdk' to handle logging and recalling of conversation data.
- Utilize the 'SessionHandler' class to manage user sessions and ensure that data is isolated per user.
- Leverage the 'PrivacyGuard' module to encrypt and protect user data stored within the system.

The goal is to create a fully functional prototype that demonstrates the power of 'agentrecall-sdk' in enhancing AI agents' memory capabilities.