agentinsight-sdk

v0.1.4 safe
3.0
Low Risk

Python SDK for AgentInsight — LLM observability, tracing, evaluation, and prompt management

🤖 AI Analysis

Final verdict: SAFE

The package shows no signs of obfuscation or credential harvesting, which are critical indicators for malicious intent. While the metadata suggests some level of neglect in package maintenance, there is insufficient evidence to suggest a supply-chain attack.

  • Low obfuscation risk
  • No detected credential harvesting
  • Metadata suggests low maintenance effort
Per-check LLM notes
  • Obfuscation: No obfuscation patterns detected, indicating low risk of malicious activity.
  • Credentials: No credential harvesting patterns detected, suggesting the package is likely safe regarding secret management.
  • Metadata: The maintainer has a new or inactive account and lacks PyPI classifiers, indicating low effort in package management.

🔬 Heuristic Checks

⚠ Outbound Network Calls score 9.0

Found 6 network call pattern(s)

  • B response = requests.get("https://service-b.example.com/api") # user
  • self.httpx_client = httpx.Client(timeout=timeout, headers=client_headers) self.api
  • is not None else httpx.Client( timeout=_defaulted_timeout, follow_redirec
  • is not None else httpx.Client(timeout=_defaulted_timeout), timeout=_defaulted
  • is not None else httpx.AsyncClient( timeout=_defaulted_timeout, follow_redirec
  • is not None else httpx.AsyncClient(timeout=_defaulted_timeout), timeout=_defaulted
✓ 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

Email domain looks legitimate: agent.goldebridge.com>

✓ Suspicious Page Links

All external links appear legitimate

✓ Git Repository History

No GitHub repository linked

  • No GitHub repository link found
⚠ Maintainer History score 4.0

2 maintainer concern(s) found

  • Author "agentinsight" 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 agentinsight-sdk 
Develop a comprehensive Python-based mini-application named 'PromptEvaluator' that leverages the capabilities of the 'agentinsight-sdk' package to manage, trace, evaluate, and optimize prompts for language models. This application will serve as a tool for developers and data scientists to better understand and improve the performance of their interactions with large language models (LLMs).

**Core Functionality:**
1. **Prompt Management:** Users can input, store, and retrieve prompts from a database.
2. **Tracing and Observability:** Track the execution of prompts through LLMs, logging metadata such as response time, model used, and any errors encountered.
3. **Evaluation Metrics:** Automatically calculate various metrics based on the LLM's responses, including but not limited to response latency, token usage, and semantic similarity between the prompt and response.
4. **Optimization Suggestions:** Provide recommendations for improving prompt efficiency and effectiveness based on the collected data.
5. **User Interface:** Implement a simple command-line interface (CLI) for ease of use.

**Features and Implementation Steps:**
1. **Setup Environment:** Begin by setting up a Python environment with the necessary packages installed, including 'agentinsight-sdk'. Ensure that you have access to at least one LLM API endpoint for testing purposes.
2. **Database Integration:** Use SQLite or another lightweight database to store user prompts and associated metadata.
3. **Prompt Input and Storage:** Develop functions within 'PromptEvaluator' that allow users to input new prompts via the CLI and store them in the database.
4. **Prompt Execution and Tracing:** Utilize the 'agentinsight-sdk' to send prompts to an LLM, capturing and storing detailed information about each interaction, such as the timestamp, model name, and response content.
5. **Metrics Calculation:** Implement logic to automatically compute key metrics after receiving a response from the LLM. These metrics could include the time taken for the response, number of tokens used, and qualitative assessments like coherence and relevance.
6. **Optimization Recommendations:** Based on the collected metrics, generate suggestions for optimizing future prompts. For example, if a prompt results in high token usage, suggest ways to reduce complexity.
7. **User Interface Enhancements:** Refine the CLI to make it more intuitive, adding features like help commands and interactive sessions.
8. **Documentation and Testing:** Write clear documentation for 'PromptEvaluator', explaining how to install, configure, and use the application. Conduct thorough testing to ensure reliability and accuracy.

By following these steps, 'PromptEvaluator' will become a valuable tool for anyone working with LLMs, providing insights into prompt performance and aiding in the development of more effective conversational AI applications.