Search Results for "reference search"

Deep Search Agent is an experimental demonstration project that showcases an autonomous AI agent designed to perform multi-step research and information gathering tasks. The repository illustrates how large language models can be orchestrated with tools and planning logic to execute complex search workflows rather than single-prompt responses.

gemini-fullstack-langgraph-quickstart is a fullstack reference application from Google DeepMind’s Gemini team that demonstrates how to build a research-augmented conversational AI system using LangGraph and Google Gemini models. The project features a React (Vite) frontend and a LangGraph/FastAPI backend designed to work together seamlessly for real-time research and reasoning tasks. The backend agent dynamically generates search queries based on user input, retrieves information via the Google Search API, and performs reflective reasoning to identify knowledge gaps. ...

...The primary chat logic lives in the Chat component at app/components/chat.tsx, which manages rendering, streaming, and forwarding function calls. Server handlers for threads are provided under api/assistants/threads/..., giving a reference for wiring the API into Next.js routes. The Chat component can be copied directly into other projects, along with its styles from app/components/chat.module.css. Example pages include a basic chat, a function calling demo, a file search demo, and a full-featured example, allowing developers to explore each feature in isolation or together.

...It includes a basic toolset for file-system operations and shell commands, plus integrations with MCP tools such as web search and knowledge graph access. Mini-Agent also comes with “Claude Skills”-style predefined skills for tasks like document processing, design work, and testing, packaged as reusable behaviors that can be invoked by the agent as needed.