ashira-memory

v0.1.0 suspicious
6.0
Medium Risk

Relational memory for AI characters and companions. Local-first.

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package shows moderate risk due to potential unauthorized network interactions and a suspiciously new repository with rapid commits and low maintainer activity.

  • Network risk (5/10)
  • Metadata risk (7/10)
Per-check LLM notes
  • Network: The presence of network calls suggests the package may interact with external services, which could be legitimate but warrants further investigation to ensure it is not engaging in unauthorized data transfer.
  • Shell: No shell execution patterns were detected.
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious intent.
  • Credentials: No credential harvesting patterns detected, indicating low risk of secret theft.
  • Metadata: Suspiciously new repository with rapid commits and low maintainer activity.

📦 Package Quality Overall: Low (4.6/10)

✦ High Test Suite 9.0

Test suite present — 3 test file(s) found

  • Test runner config found: pyproject.toml
  • 3 test file(s) detected (e.g. real_ollama_test.py)
◈ Medium Documentation 5.0

Some documentation present

  • Detailed PyPI description (4576 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 5.0

Partial type annotation coverage

  • 58 type-annotated function signatures detected in source
○ Low Multiple Contributors 2.0

Single-author or unverifiable project

  • 1 unique contributor(s) across 6 commits in Mint658/Ashira-memory
  • Single author with few commits — possibly a personal or throwaway project

🔬 Heuristic Checks

⚠ Outbound Network Calls score 3.0

Found 2 network call pattern(s)

  • oat]] = [] async with httpx.AsyncClient(timeout=self._timeout) as client: for t in texts
  • ] = "json" async with httpx.AsyncClient(timeout=self._timeout) as client: r = await clie
✓ 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 5.0

Git history flags: Repository has zero stars and zero forks

  • Repository has zero stars and zero forks
  • All 6 commits happened within 24 hours
⚠ Maintainer History score 2.0

1 maintainer concern(s) found

  • Author "Mint" 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 ashira-memory 
Develop a personal AI companion app named 'MemoFriend' using the Python package 'ashira-memory'. This app will serve as a digital friend that not only remembers interactions but also learns from them to provide more personalized responses over time. MemoFriend will be designed to run locally on a user's device, ensuring all data remains private and secure without needing internet connectivity.

Core Features:
1. **User Interaction Logging**: Users can converse with MemoFriend through text inputs. Each interaction should be logged in the relational memory system provided by 'ashira-memory', capturing context such as the date, time, and content of the conversation.
2. **Contextual Understanding**: Utilize 'ashira-memory' to analyze past conversations and understand the context of new queries. For example, if a user asks about their previous preferences, MemoFriend should be able to recall and reference these details accurately.
3. **Learning Over Time**: Implement a feature where MemoFriend improves its responses based on the frequency and nature of interactions. For instance, if a user repeatedly asks about similar topics, MemoFriend should become better at addressing those topics over time.
4. **Personalized Recommendations**: Based on the stored data, MemoFriend should offer personalized recommendations or suggestions related to the user's interests or recent activities.
5. **Data Privacy & Security**: Since 'ashira-memory' supports local-first operations, ensure that all user data is stored securely on the user's device, respecting privacy and security standards.

How to Use 'ashira-memory':
- Initialize the relational memory system to start storing and retrieving conversational data.
- Use 'ashira-memory' to create entities representing users and their interactions, linking them appropriately to capture the context of each conversation.
- Leverage 'ashira-memory' capabilities to query past interactions efficiently, enabling MemoFriend to respond with relevant information.
- Implement learning algorithms that utilize 'ashira-memory' to analyze patterns in user behavior and improve future interactions.

Your task is to design and implement a fully functional version of MemoFriend that demonstrates the above features, showcasing how 'ashira-memory' can enhance the functionality and personalization of AI companions.

💬 Discussion Feed

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