Deep Research Technology

Deep Research Agent, Seriously.

Deep Research is a specialized project research assistant designed for the crypto that generates comprehensive reports on any topic following a workflow similar to OpenAI and Gemini Deep Research. it allows you to customize the models, prompts, report structure, search API, and research depth.

I. Deep Research: The Intelligent Research Engine for Web3

Deep Research refers to AI tools' capability to perform complex, multi-step research tasks (e.g., OpenAI Deep Research, Google Gemini Deep Research), featuring:

• Web crawling & multi-source analysis: Cross-platform data scraping and structured report generation;

• Deep reasoning: Ideal for scenarios requiring long-term tracking and cross-validation (e.g., on-chain behavior pattern analysis).

Core Value in Web3:

• Blockchain data analysis: Real-time parsing of transaction volume, Gas fee fluctuations, and smart contract interactions to optimize trading strategies;

• Market trend prediction: Processing massive datasets to identify price signals and risk alerts (e.g., Perplexity's free tool generating crypto volatility reports);

• Compliance automation: Tracking global regulations (e.g., MiCA, FATF guidelines) and outputting audit-ready solutions.

Technical Breakthrough:

• Deep Research MCP servers (e.g., MCP.so instance) are AI agent-optimized, integrating Gemini multimodal models;

• Enabled by MCP protocol:

  Research task → MCP data calls → Deep Research analysis → Structured output → On-chain verification  

---

II. MultiAgent Architecture & Synergy

1. Technical Philosophy:

• MultiAgent as inevitability: DeepCore adopts a "virtual machine + MultiAgent collaboration" architecture, dynamically orchestrating GPT/Claude/DeepSeek model APIs;

• End-to-end: From natural language input to executable on-chain solutions (e.g., "Generate and auto-deploy a Uniswap V3 liquidity optimization strategy").

2. Interaction Revolution:

• “Less Structure, More Intelligence”:

  • Developers create agents via natural language commands (e.g., "Build a BAYC floor price monitoring bot");

  • No-code interface reduces 90% development barriers (testnet data).

3. MCP + Deep Research Synergy:

Dimension	Legacy Approach	DeepCore Solution
Task latency	Minutes (manual intervention)	Seconds (automated workflows)
Cross-model collaboration	Manual API bridging	Protocol-layer auto-routing (e.g., Claude→GPT-4→on-chain execution)
Security verification	Centralized audits	zk-SNARK on-chain verification + MultiAgent cross-checking