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deepsearch-research-agent

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🔍 DeepSearch Autonomous Research Agent

Executive Overview

DeepSearch is a state-of-the-art autonomous deep research agent and consensus synthesis engine. Designed to tackle open-ended, complex scientific, technological, and market intelligence questions, DeepSearch iteratively decomposes research objectives, executes multi-angle boolean search queries, harvests and verifies primary literature, resolves contradictory empirical claims, and outputs publication-grade synthesized reports with mathematical grounding scores and interactive visualizations.

🌟 Key Capabilities

  • 🌳 Recursive Tree-of-Thought Query Decomposition: Breaks down broad questions into multi-stage Directed Acyclic Graphs (DAGs) with automated Boolean operator crafting (site:, filetype:pdf, intitle:).

  • ⚖️ Dialectical Consensus & Contradiction Matrix: Cross-references claims across dozens of sources, computing weighted agreement ratios and isolating contested assumptions.

  • 📐 Calibrated Confidence Scoring: Calculates grounded confidence metrics $C \in [0, 1]$ based on domain authority, recency decay, peer-review weight, and corroboration depth.

  • 🔌 Native Model Context Protocol (MCP) Server: Drop-in MCP integration for Claude Desktop, Cursor, Cline, Zed, and custom LLM runtimes.

  • 🖥️ Google Deep Research Studio UI: Built-in Google Material 3 web studio with live query branch visualizer, consensus matrix, interactive SVG metrics charts, and copy-ready client config hub.

  • 📦 Multi-Format Export: Generates peer-reviewed Markdown reports, interactive standalone HTML documents, and machine-readable JSON data bundles.

Related MCP server: genpark-scientific-consensus-ratio-mapper-skill

🏛️ System Architecture

+----------------------------------------------------------------------------------------------------+
|                                DEEPSEARCH SYSTEM ARCHITECTURE                                      |
+----------------------------------------------------------------------------------------------------+

                           [ Human Query / MCP Client / IDE / Web UI ]
                                                |
                                                v
               +----------------------------------------------------------------+
               |              Stage 1: Autonomous Query Planner                 |
               |   - Tree-of-Thought Decomposition (Technical, Market, Risk)    |
               |   - Search Operator Crafting (site:, filetype:pdf, boolean)    |
               +-------------------------------+--------------------------------+
                                               |
                                               v
               +----------------------------------------------------------------+
               |              Stage 2: Parallel Evidence Harvester              |
               |   - Multi-Engine Crawling (Serper, Tavily, ArXiv, PubMed)      |
               |   - Asynchronous Content Extraction & Boilerplate Scrubbing    |
               +-------------------------------+--------------------------------+
                                               |
                                               v
               +----------------------------------------------------------------+
               |           Stage 3: Domain Authority & Credibility Engine       |
               |   - Institutional Scoring (0-100), Bias Detection              |
               |   - Recency Decay Factor: R(dt) = exp(-lambda * dt)            |
               +-------------------------------+--------------------------------+
                                               |
                                               v
               +----------------------------------------------------------------+
               |            Stage 4: Consensus & Contradiction Matrix           |
               |   - Corroborating vs Refuting Claim Partitioning               |
               |   - Weighted Agreement Ratios & Conflict Flagging              |
               +-------------------------------+--------------------------------+
                                               |
                                               v
               +----------------------------------------------------------------+
               |             Stage 5: Grounded Multi-Perspective Synthesizer     |
               |   - Calibrated Confidence Math: C = sum(w_i * s_i * alpha)      |
               |   - Zero-Unreferenced-Claim Policy & Interactive Visuals       |
               +-------------------------------+--------------------------------+
                                               |
         +-------------------------------------+------------------------------------+
         |                                     |                                    |
         v                                     v                                    v
[ 📄 Markdown Research Report ]    [ 🌐 Material 3 HTML Artifact ]     [ 📊 JSON Data Bundle ]

🚀 Quick Start

1. Installation

# Install via pip
pip install deepsearch-research-agent

# Or run with uvx without installation
uvx deepsearch-research-agent --help

2. Environment Configuration

Set your preferred search and LLM API keys:

export SERPER_API_KEY="your_serper_api_key"
export TAVILY_API_KEY="your_tavily_api_key"
export OPENAI_API_KEY="your_openai_api_key"
export ANTHROPIC_API_KEY="your_anthropic_api_key"

3. Command Line Interface (CLI)

# Run quick research (1 stage, 5-10 sources)
deepsearch run "CRISPR-Cas12 vs Cas9 off-target editing rates" --depth quick

# Run exhaustive research with custom domain focus and exports
deepsearch run \
  "State of Fault-Tolerant Quantum Computing & Logical Qubit Scaling (2026)" \
  --depth exhaustive \
  --breadth 8 \
  --focus-domains "nature.com,science.org,aps.org,ieee.org" \
  --output-dir ./output-report \
  --format all

4. Python Programmatic API

from deepsearch_research_agent import DeepResearchEngine, ResearchConfig

# Configure research depth and constraints
config = ResearchConfig(
    depth="exhaustive",
    breadth=8,
    enable_consensus_matrix=True,
    min_source_authority=85,
)

engine = DeepResearchEngine(config=config)
result = engine.run(
    query="State of Fault-Tolerant Quantum Computing & Logical Qubit Scaling (2026)"
)

# Access structured data
print(f"Confidence Score: {result.confidence_score:.2%}")
print(f"Harvested Sources: {len(result.sources)}")
print(f"Verified Claims: {len(result.verified_claims)}")

# Export artifacts
result.save_markdown("report.md")
result.save_html("report.html")
result.save_json("data_bundle.json")

🤖 Model Context Protocol (MCP) Integration

DeepSearch can be attached as a live context engine in Claude Desktop, Cursor, Cline, and Zed.

# Launch MCP Server in stdio mode
deepsearch mcp

# Launch MCP Server over SSE (HTTP streaming)
deepsearch mcp --transport sse --port 8080

Drop-in Claude Desktop Configuration (claude_desktop_config.json):

{
  "mcpServers": {
    "deepsearch-research-agent": {
      "command": "uvx",
      "args": ["deepsearch-research-agent", "mcp"],
      "env": {
        "SERPER_API_KEY": "your_serper_api_key",
        "DEEPSEARCH_DEFAULT_DEPTH": "deep"
      }
    }
  }
}

For full integration guides for Cursor, Cline, and Zed, see docs/MCP_GUIDE.md and the examples/mcp-clients/ directory.

🖥️ Google Deep Research Studio Web UI

DeepSearch ships with an interactive, offline-ready Google Material 3 Light Mode studio located at public/index.html.

To launch the studio:

deepsearch studio --port 3000

Or open public/index.html directly in any modern browser.

Studio Features:

  1. 🔍 Deep Research Studio: Live multi-stage query decomposition visualizer, citation stream, and Markdown preview.

  2. 📑 Autonomous Query Planner: Interactive Tree-of-Thought graph, custom search operator generator, and boolean string preview.

  3. ⚖️ Source & Consensus Matrix: Interactive sources table with authority scoring, claim verification status, and corroboration gauges.

  4. 📊 Data Points & SVG Visuals: Native SVG multi-bar, time-series, radar, and donut charts with interactive tooltips.

  5. 🤖 AI Agent & MCP Hub: One-click configuration copier for Claude, Cursor, Cline, Zed, and live JSON-RPC tool simulator.

  6. 📖 Research Methodology Guide: Built-in interactive architectural documentation and confidence calibration math.

📚 Documentation Directory

📁 Reference Examples

🧪 Testing & CI/CD

Run the test suite locally:

PYTHONPATH=src pytest tests/ -v

The repository includes a comprehensive 15-job CI matrix in .github/workflows/ci.yml testing across Ubuntu, macOS, and Windows on Python 3.9, 3.10, 3.11, 3.12, and 3.13.

📄 License

Licensed under the Apache License, Version 2.0.

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