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by danielecelsa

Overview

An advanced AI client for autonomous web research, utilizing a decoupled microservices architecture with the Model Context Protocol (MCP) for tool communication.

Installation

Run Command
streamlit run app.py

Environment Variables

  • GOOGLE_API_KEY
  • GENAI_MODEL
  • MCP_TAVILY_URL
  • MCP_SERVER_NAME
  • COST_PER_1K_TOKENS_USD_INPUT
  • COST_PER_1K_TOKENS_USD_OUTPUT
  • REDIS_URL
  • LOGTAIL_SOURCE_TOKEN
  • LOGTAIL_URL
  • CHECKPOINT_DB
  • RENDER

Security Notes

The project follows good security practices by externalizing API keys to environment variables and implements a decoupled security model where the client (agent) does not directly hold sensitive API keys like the Tavily key. However, it relies on an external MCP server URL (`MCP_TAVILY_URL`) which, if compromised or misconfigured, could lead to unexpected behavior or data exposure. The `get_user_info` helper function relies on undocumented Streamlit internal APIs, which could be a stability risk for future Streamlit versions, but not a direct security vulnerability.

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Stats

Interest Score0
Security Score9
Cost ClassMedium
Avg Tokens1000
Stars0
Forks0
Last Update2025-12-20

Tags

AI AgentWeb ResearchMicroservicesLangGraphMCPStreamlit