Yagmi: Local-First Web Search Agent

🔑 Enhanced Key Takeaways

• •Yagami utilizes the Model Context Protocol (MCP) to standardize how the agent interacts with local LLMs and external search tools, facilitating interoperability across different AI development environments.
• •The architecture leverages a specialized search-to-context pipeline that processes raw web search results into a structured format optimized for local LLM token windows, minimizing context overflow.
• •By decoupling the search provider from the LLM inference engine, Yagami allows users to swap between different search APIs (such as Tavily or Brave Search) while maintaining a consistent local-first orchestration layer.

🛠️ Technical Deep Dive

• •Orchestration: Implemented as an MCP server, allowing it to act as a bridge between LLM clients (like Jan) and search tools.
• •Inference Backend: Designed to interface with vLLM, supporting high-throughput serving of models like Qwen2.5-9B.
• •Search Integration: Replaces traditional cloud-based search APIs in coding assistants by routing queries through a local proxy that handles request formatting and response parsing.
• •Dependency Management: Built to run within local Python environments, requiring minimal external dependencies beyond the MCP SDK and search API keys.

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline