agentica

v1.4.6 suspicious
6.0
Medium Risk

Agentica: A lightweight Python SDK for building AI agents with persistent memory and self-evolution

🤖 AI Analysis

Final verdict: SUSPICIOUS

The package exhibits several concerning behaviors, including the execution of shell commands, potential obfuscation techniques, and insecure network practices. These factors elevate its risk profile, though there's no definitive proof of malicious intent.

  • High shell risk due to execution of shell commands
  • Signs of code obfuscation through base64 encoding
Per-check LLM notes
  • Network: The use of HTTP requests could indicate normal package updates or data fetching, but without more context, it might also suggest unauthorized external communication.
  • Shell: Executing shell commands and system calls can be legitimate for certain functionalities, yet they may pose a risk of executing arbitrary code or accessing sensitive information, suggesting potential misuse.
  • Obfuscation: The code shows signs of obfuscation through base64 encoding which could be used for malicious purposes.
  • Credentials: No clear evidence of credential harvesting is present.
  • Metadata: The package shows some suspicious signs, including a non-secure link and an author with limited history, but no clear evidence of typosquatting.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 1.5

Found 1 network call pattern(s)

  • import httpx resp = httpx.get( _CATALOG_URL, timeout=10,
⚠ Code Obfuscation score 2.0

Found 1 obfuscation pattern(s)

  • alse dest.write_bytes(base64.b64decode(b64_data)) return dest.exists() and dest.stat().st_s
⚠ Shell / Subprocess Execution score 10.0

Found 6 shell execution pattern(s)

  • : con = get_console() os.system('clear' if os.name != 'nt' else 'cls') current_agent = c
  • bool: try: info = subprocess.run( ["osascript", "-e", "clipboard info"],
  • -> bool: try: r = subprocess.run(["pngpaste", str(dest)], capture_output=True, timeout=3)
  • n' ) try: r = subprocess.run( ["osascript", "-e", script], captur
  • try: r = subprocess.run( [name, "-NoProfile", "-NonInteractive", "-C
  • rn False try: r = subprocess.run( [ps, "-NoProfile", "-NonInteractive", "-Command
✓ Credential Harvesting

No credential harvesting patterns detected

✓ Typosquatting

No typosquatting candidates detected

⚠ Registered Email Domain score 3.0

Suspicious email domain flags: Very short email domain: qq.com>

  • Very short email domain: qq.com>
⚠ Suspicious Page Links score 2.0

Found 1 suspicious link(s) on the package page

  • Non-HTTPS external link: http://127.0.0.1:8789/chat`。如需改监听地址，可设置
✓ Git Repository History

Repository shibing624/agentica 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 agentica 
Create a mini-application named 'MemoryBot' using the Python package 'agentica'. MemoryBot will serve as a personal assistant with persistent memory capabilities, allowing it to remember previous interactions and learn from them over time. This application will demonstrate the core features of the 'agentica' package, such as building an AI agent with memory and self-evolution.

Step 1: Define the purpose and functionality of MemoryBot. It should be able to answer questions, take notes, set reminders, and provide information based on past conversations.

Step 2: Set up the environment by installing 'agentica' and any other necessary Python packages. Use virtual environments for dependency management.

Step 3: Design the structure of your application. Create classes or modules for the main functionalities like conversation handling, note-taking, and reminder setting.

Step 4: Implement the core logic of MemoryBot using the 'agentica' package. Utilize its features to enable persistent memory and self-evolution within the AI agent.

Step 5: Integrate natural language processing (NLP) capabilities to allow users to interact with MemoryBot through text-based commands.

Step 6: Test MemoryBot with various scenarios to ensure it remembers past interactions and evolves its responses over time.

Suggested Features:
- Persistent Memory: Remember user inputs and outputs across sessions.
- Self-Evolution: Improve response quality and relevance based on user feedback and interaction history.
- Note-Taking: Allow users to add notes which the bot can recall during future conversations.
- Reminder Setting: Enable users to set reminders for specific times or dates.
- Information Retrieval: Provide relevant information based on the context of the conversation.