> ## Documentation Index
> Fetch the complete documentation index at: https://docs.mindblix.tech/llms.txt
> Use this file to discover all available pages before exploring further.

# Research Agent

> The Research Agent excels at gathering, synthesizing, and fact-checking information from multiple sources.

## Overview

The **Research Agent** is designed for deep information gathering and synthesis. It can search the web, analyze documents, and produce comprehensive research reports with cited sources.

| Property | Value |
| - | - |
| **Template ID** | `research` |
| **Default Model** | Gemini 1.5 Flash |
| **Avg. Response Time** | \~8 seconds |
| **Accuracy Rate** | 94% |

***

## Capabilities

* 🔍 **Web Search** — Search the web for relevant information using multiple search engines
* 📄 **Document Analysis** — Extract and analyze content from PDFs, DOCX, and web pages
* ✅ **Fact-Checking** — Cross-reference claims against multiple sources
* 📊 **Competitive Intelligence** — Analyze competitors, market trends, and industry reports
* 📝 **Report Generation** — Produce structured research reports with citations

***

## Usage

**API**

```bash theme={null}
curl -X POST http://localhost:8000/api/v1/agents/research/execute \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "query": "What are the latest trends in AI agent orchestration?",
    "config": {
      "max_sources": 10,
      "output_format": "report"
    }
  }'
```

**Python**

```python theme={null}
from mindblix import MindBlix

client = MindBlix(api_key="YOUR_API_KEY")

result = client.agents.execute(
    template="research",
    query="What are the latest trends in AI agent orchestration?",
    config={"max_sources": 10, "output_format": "report"}
)

print(result.report)
```

***

## Output Example

```json theme={null}
{
  "summary": "AI agent orchestration is rapidly evolving with...",
  "key_findings": [
    {
      "finding": "Multi-agent systems are replacing single LLM calls",
      "confidence": 0.94,
      "sources": ["arxiv.org/...", "blog.google/..."]
    }
  ],
  "sources": [
    {
      "url": "https://arxiv.org/abs/2024.xxxxx",
      "title": "Survey of Multi-Agent AI Systems",
      "relevance_score": 0.95
    }
  ],
  "metadata": {
    "model": "gemini-1.5-flash",
    "tokens_used": 4521,
    "execution_time_ms": 7840
  }
}
```


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