agent-memory-toolkit

v0.1.0 suspicious
7.0
High Risk

Hybrid retrieval memory for AI agents - BM25 + vectors + knowledge graph with RRF fusion

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits high obfuscation and metadata risks, indicating potential attempts to hide malicious activities or intentions. The combination of these factors raises concerns about its safety.

  • High obfuscation risk
  • Unverified and potentially untraceable maintainer
Per-check LLM notes
  • Network: The network calls to various endpoints may be part of the package's functionality, but require further investigation into their purpose.
  • Shell: No shell execution patterns detected.
  • Obfuscation: The code uses unusual patterns that could be used to obfuscate code execution, potentially hiding malicious activities.
  • Credentials: No direct evidence of credential harvesting was found in the provided snippet.
  • Metadata: The package shows several red flags including an untraceable repository, a new and potentially unverified maintainer, and no maintainer history.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • i/stats" with urllib.request.urlopen(url, timeout=2) as response: dat
  • pi/memories" with urllib.request.urlopen(url, timeout=2) as response: data =
  • api/domains" with urllib.request.urlopen(url, timeout=2) as response: data =
  • pi/branches" with urllib.request.urlopen(url, timeout=2) as response: data =
  • api/storage" with urllib.request.urlopen(url, timeout=2) as response: data =
  • pi/searches" with urllib.request.urlopen(url, timeout=2) as response: data =
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • ith patch.object( __import__('agent_memory.dashboard.server', fromlist=['DashboardServer']).DashboardServer, 'start' ) as mock_star
✓ Shell / Subprocess Execution

No shell execution patterns detected

✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

✓ Registered Email Domain

Email domain looks legitimate: autosre.ai>

✓ Suspicious Page Links

All external links appear legitimate

⚠ Git Repository History score 3.0

Repository not found (deleted or private)

  • Repository not found (deleted or private)
⚠ Maintainer History score 6.0

3 maintainer concern(s) found

  • Only one version has ever been released — brand new package
  • 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-memory-toolkit 
Create a Personal Knowledge Assistant (PKA) that leverages the 'agent-memory-toolkit' package to enhance user interaction and information retrieval from a personalized dataset. PKA should allow users to input text documents, which will then be indexed using the toolkit's hybrid memory system (BM25, vectors, knowledge graph with RRF fusion). Users can ask questions about the content of their documents, and PKA will provide accurate and contextually relevant answers. Additionally, PKA should support incremental learning, meaning it continuously updates its knowledge base as new documents are added.

Step 1: Set up the environment
- Install Python and necessary packages including 'agent-memory-toolkit'.

Step 2: Develop the document indexing feature
- Implement functions to process and index user-uploaded text documents into the hybrid memory system provided by 'agent-memory-toolkit'.

Step 3: Create the query processing module
- Design a component that takes user queries, processes them through the hybrid memory system, and retrieves relevant information.

Step 4: Integrate feedback mechanisms
- Allow users to rate the relevance of responses, which will help in refining the search results over time.

Step 5: Add user interface
- Build a simple web-based UI for uploading documents and asking questions.

Suggested Features:
- Incremental Learning: Update the knowledge base with each new document added.
- Contextual Understanding: Ensure responses are not only accurate but also maintain context across multiple queries.
- User Feedback Loop: Incorporate a rating system for query responses to improve future searches.
- Document Management: Provide options for users to organize and manage their uploaded documents.

How 'agent-memory-toolkit' is utilized:
- For indexing documents using BM25, vector embeddings, and knowledge graphs.
- For retrieving information through the hybrid retrieval model that combines the strengths of different memory types.
- For continuous learning and updating the knowledge base with new data.