> For the complete documentation index, see [llms.txt](https://docs.deepcore.top/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.deepcore.top/core-product-suite/deep-research-technology.md).

# 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.

<figure><img src="/files/XiybemjV6MaFRgea0Pxi" alt=""><figcaption></figcaption></figure>

**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          |

<br>


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