Deep Research w/ Swarm Agent

Autonomous deep research for LM Studio. A swarm of specialized workers searches your local libraries and the web, then synthesizes everything into a structured report - one tool call, no API keys.

Tools

# Deep Research

The main tool. Give it a topic, get back a full Markdown report with AI-written analysis, citations, contradiction detection, and a coverage breakdown across 12 research dimensions. When local document sources are enabled, workers search your RAG libraries progressively - proprietary first, web to fill gaps.

Parameters:

topic - what to research (be specific)
focusAreas - optional angles to emphasize, e.g. ["side effects", "FDA status"]
depthOverride - "shallow" / "standard" / "deep" / "deeper" / "exhaustive"
contentLimitOverride - chars per page (1K-20K, auto-scales with depth)

# Research Search

Scored DuckDuckGo results with domain authority tiers and snippet extraction.

# Research Read Page

Fetch and extract a single URL. Handles PDFs automatically.

# Research Multi-Read

Batch-fetch up to 10 URLs concurrently.

Local Library Tools

# Add Library

Index a local folder into a searchable library with full metadata.

Parameters:

name - descriptive name (e.g. "Company Policies", "Research Papers")
folderPath - absolute path to the document folder
priority - "proprietary" / "internal" / "reference" / "general" (default: general)
tags - array of routing tags: ["legal"], ["academic", "technical"], ["financial", "reports"], etc.
description - optional description

Example usage:

RAG Add Library(
  name: "Client Contracts",
  folderPath: "/home/user/documents/contracts",
  priority: "proprietary",
  tags: ["legal"],
  description: "All active client contracts and SLAs"
)

# List Libraries

Show all indexed libraries sorted by priority, with file counts, chunk counts, word totals, tags, and file type breakdown.

# Remove Library

Remove a library by its UUID (id).

# Search

Search across libraries with BM25 + fuzzy hybrid scoring mechanism.

Features:

Progressive mode (default): searches proprietary → internal → reference → general
Context windows: includes surrounding chunk text with each result
Library-specific search: filter to a single library by ID
Heading-aware: boosts results where the query matches section headings

# Update Library

Change a library's name, description, priority, or tags without re-indexing.

# Check Changes

Detect modified, deleted, and newly added files since last indexing.

# Save Index

Persist the entire RAG index to a JSON file on disk.

# Load Index

Restore a previously saved index - instant library access without re-scanning.

# Legacy

The previous Local Docs Add/List/Remove/Search Collection tools still work as aliases for backward compatibility.

Supported file types: .txt, .md, .html, .csv, .json, .xml, .log, and many more.

How It Works

Decomposes the topic into specialized workers (up to 10 roles: breadth, depth, recency, academic, critical, statistical, regulatory, technical, primary sources, comparative)
Searches local/RAG libraries progressively - proprietary → internal → reference → general, with auto-routing by tag. Each worker claims up to 30% of its page budget from local sources before touching the web
Searches the web across multiple engines in parallel - DuckDuckGo, Brave, Google Scholar, SearXNG, Mojeek (all scraped, no keys)
Fetches & extracts pages with aggressive boilerplate removal, relevance scoring, and duplicate detection
Follows links recursively (1-3 levels deep depending on depth preset)
Detects gaps across 12 research dimensions and spawns targeted follow-up workers
Stops intelligently - when coverage is complete, sources stagnate, or rounds run out
Synthesizes a narrative report with inline citations, contradiction detection, and source origin tags (web vs local)

Progressive Source Approach

This is the key innovation for organizations with large proprietary datalakes. Instead of treating all sources equally, the plugin searches in priority order:

┌─────────────────────┐
│  1. PROPRIETARY      │  < Your confidential data (contracts, internal memos, trade secrets)
│     Searched first   │
├─────────────────────┤
│  2. INTERNAL         │  < Shared team knowledge (wikis, documentation, reports)
│     Searched second  │
├─────────────────────┤
│  3. REFERENCE        │  < Curated reference materials (papers, standards, regulations)
│     Searched third   │
├─────────────────────┤
│  4. GENERAL          │  < Miscellaneous local documents
│     Searched fourth  │
├─────────────────────┤
│  5. WEB              │  < Public internet (fills remaining gaps)
│     Searched last    │
└─────────────────────┘

Workers also auto-route to the right library by tag:

Academic worker → searches academic and technical tagged libraries
Regulatory worker → searches legal and policy tagged libraries
Technical worker → searches technical and code tagged libraries
Statistical worker → searches financial and reports tagged libraries

Quick Start

1. Index your document libraries:

RAG Add Library(name: "Research Papers", folderPath: "/papers", priority: "reference", tags: ["academic"])
RAG Add Library(name: "Internal Docs", folderPath: "/company/docs", priority: "internal", tags: ["reports"])
RAG Add Library(name: "Legal", folderPath: "/legal", priority: "proprietary", tags: ["legal", "policy"])

2. Enable local sources in plugin settings (Local Document Sources → On)

3. Run Deep Research as usual - workers will search your libraries progressively

4. Save your index so you don't need to re-index next session:

RAG Save Index(filePath: "~/.lmstudio/rag-index.json")

5. Next session, load it back:

RAG Load Index(filePath: "~/.lmstudio/rag-index.json")

Depth Presets

	Shallow	Standard	Deep	Deeper	Exhaustive
Rounds	1	3	5	10	15
Worker roles	5	5	8	10	10
Pages/worker	5	8	12	18	25
Search engines	1	2	3	4	5
Link depth	1	1	2	2	3
Fan-out	x1	x1	x2	x2	x3
Content/page	5K	6K	8K	12K	16K
Sources (upto)	~25-50	~40-80	~80-150	~150-250+	~250-400+

No hard source cap - collection is fully adaptive. Local sources are additional - they don't eat into the web budget shown above.

Configuration

Setting	Description
Research Depth	Shallow → Exhaustive (scales everything)
Content Per Page	Chars extracted per page (auto-scales, up to 20K)
Link Following	Follow in-page citations and references
AI Query Planning	Use loaded model for query generation and synthesis
Safe Search	DuckDuckGo safe search level
Local Document Sources	Search indexed local/RAG libraries alongside the web

Recommended System Prompt

When the user asks for research or wants to understand a topic in depth, use the "Deep Research" tool. After receiving the report:
1. Lead with the AI Research Analysis - it's the main synthesis.
2. Check the Contradictions section for disagreements between sources.
3. Cite sources by index: [1], [2], etc.
4. Note any coverage gaps and offer to dig deeper.
5. Present both sides where sources conflict.
6. Distinguish between local and web sources when relevant.
7. Prioritise findings from proprietary/internal sources when only they're available.

MIT License

deep-swarm-research