local-deep-research/docs/features.md

Features Documentation

This comprehensive guide covers all features available in Local Deep Research (LDR).

Note

: This documentation is maintained by the community and may contain inaccuracies. While we strive to keep it up-to-date, please verify critical information and report any errors via GitHub Issues.

Table of Contents

1. Research Modes
2. Search Capabilities
3. LLM Integration
4. User Interface Features
5. Advanced Features
6. Developer Features
7. Performance Features

Research Modes

Quick Summary Mode

Fast research mode that provides concise answers with citations.

Features:

• Automatic query decomposition
• Parallel search execution
• Smart result synthesis
• Citation tracking
• Structured output with tables when relevant

Usage:

from local_deep_research.api import quick_summary

result = quick_summary(
    query="Your research question",
    iterations=2,  # Number of research iterations
    questions_per_iteration=3  # Sub-questions per iteration
)

Detailed Research Mode

Comprehensive analysis mode for in-depth exploration of topics.

Features:

• Section-based research organization
• Multiple research cycles
• Cross-reference validation
• Extended context windows
• Detailed citation management

Report Generation Mode

Creates professional research reports with proper structure.

Features:

• Automatic table of contents
• Section headers and organization
• Executive summary generation
• Bibliography management
• Export to PDF/Markdown

Document Analysis Mode

Searches and analyzes your private document collections.

Features:

• Multiple document formats supported
• Vector-based semantic search
• Collection management
• Incremental indexing
• Privacy-preserved processing

Chat Mode

Experimental — interface and behavior may change before GA.

Interactive multi-turn research conversations. Each session accumulates context across turns and supports streaming progress and follow-up refinement via the sidebar Chat link or /chat/. Designed for exploring a topic progressively rather than one-off lookups; for single queries, use a research mode directly from the home page.

Features:

• Multi-turn conversation with accumulated context (entities, topics, source count)
• Live streaming of research steps and citations as the answer is built
• Persistent sessions in your per-user database (encrypted by default; survive logout)
• Session lifecycle: archive, reactivate, permanently delete
• Optional LLM-generated session titles (toggle via chat.llm_title_generation)
• Export a session as Markdown
• Always uses "quick" research mode (v1); one in-flight research per session

Search Capabilities

Multi-Engine Search

Simultaneously query multiple search engines for comprehensive results.

Supported Engines:

• Academic: arXiv, PubMed, Semantic Scholar
• General: Wikipedia, SearXNG, DuckDuckGo
• Technical: GitHub, Elasticsearch
• Custom: Local documents, LangChain retrievers

Intelligent Query Routing

The default langgraph-agent strategy selects appropriate search engines dynamically based on query type:

• Scientific queries → Academic engines
• Code questions → GitHub + technical sources
• General knowledge → Wikipedia + web search

Adaptive Rate Limiting

Features:

• Learns optimal wait times per engine
• Automatic retry with exponential backoff
• Fallback engine selection
• Rate limit status monitoring

Search Strategies

Search strategies:

• source-based: Comprehensive research with detailed source tracking
• focused-iteration: Iterative refinement, quick Q&A (highest factual accuracy)
• focused-iteration-standard: Comprehensive variant with broader exploration
• topic-organization: Clusters sources into topics for structured output
• mcp: Agentic ReAct-pattern research using MCP tools
• langgraph-agent: Autonomous agentic research

See Architecture Overview for details.

LLM Integration

Local Models (via Ollama)

Supported Models:

• Llama 3 (8B, 70B)
• Mistral (7B, 8x7B)
• Gemma (7B, 12B)
• DeepSeek Coder
• Custom GGUF models

Features:

• Complete privacy
• No API costs
• Model hot-swapping
• GPU acceleration support

Cloud Models

Providers:

• OpenAI (GPT-3.5, GPT-4)
• Anthropic (Claude 3 family)
• Google (Gemini models)
• OpenRouter (100+ models)

Features:

• Automatic fallback
• Cost tracking per model
• Token usage monitoring
• Model comparison tools

User Interface Features

Web Interface

Core Features:

• Real-time research progress
• Interactive result exploration
• Settings management
• Research history
• Export capabilities

Keyboard Shortcuts

• ESC: Cancel current operation
• Ctrl+Shift+1: Quick Summary mode
• Ctrl+Shift+2: Detailed Research mode
• Ctrl+Shift+3: Report Generation
• Ctrl+Shift+4: Settings
• Ctrl+Shift+5: Analytics

Real-time Updates

WebSocket Features:

• Live research progress
• Streaming results
• Status notifications
• Error handling
• Connection management

Export Options

Formats:

• PDF with formatting
• Markdown with citations
• JSON for programmatic use
• Plain text
• HTML with styling

Advanced Features

LangChain Integration

Connect any LangChain-compatible retriever:

from local_deep_research.api import quick_summary

result = quick_summary(
    query="Internal documentation query",
    retrievers={"company_docs": your_retriever},
    search_tool="company_docs"
)

Supported Vector Stores:

• FAISS
• Chroma
• Pinecone
• Weaviate
• Elasticsearch
• Custom implementations

MCP Server (Claude Integration)

Use LDR as a research tool directly from Claude Desktop or other MCP-compatible AI assistants.

Features:

• 8 tools (5 research, 3 discovery) accessible via Model Context Protocol
• STDIO transport for secure local operation
• Per-call settings overrides
• ReAct (agentic) strategy with dynamic tool selection
• Document analysis with RAG pipeline

See MCP Server Guide for setup and usage.

REST API

Full HTTP API for language-agnostic access:

# Quick summary
POST /api/v1/quick_summary

# Detailed research
POST /api/v1/detailed_research

# Report generation
POST /api/v1/generate_report

Features:

• OpenAPI specification
• Authentication support
• Rate limiting
• Webhook callbacks
• Batch processing

Analytics Dashboard

Metrics Tracked:

• Cost per research/model
• Token usage patterns
• Response times
• Success rates
• Search engine health
• User ratings

Time Ranges:

• Last 7 days
• Last 30 days
• Last 90 days
• All time

Research History

Features:

• Full research archive
• Search within results
• Tagging system
• Sharing capabilities
• Version tracking

Developer Features

Python SDK

from local_deep_research import ResearchClient

client = ResearchClient(
    llm_provider="ollama",
    llm_model="llama3:8b",
    search_engines=["searxng", "arxiv"]
)

result = client.research(
    query="Your question",
    strategy="focused_iteration"
)

Benchmarking System

Features:

• SimpleQA dataset support
• Custom dataset creation
• Performance metrics
• A/B testing framework
• Configuration optimization

Usage:

python -m local_deep_research.benchmarks \
    --dataset simpleqa \
    --examples 100 \
    --config your_config.json

Command Line Tools

# Run benchmarks from CLI
python -m local_deep_research.benchmarks --dataset simpleqa --examples 50

# Manage rate limiting
python -m local_deep_research.web_search_engines.rate_limiting status
python -m local_deep_research.web_search_engines.rate_limiting reset

Performance Features

Caching System

Document Embedding Cache:

• Caches document embeddings for faster subsequent searches

Parallel Processing

Optimization:

• Concurrent search queries
• Parallel LLM calls
• Async result processing
• Thread pool management

Resource Management

Features:

• Token budget enforcement
• Request queuing
• Graceful degradation

Security Features

Privacy Protection

• Local processing options
• No telemetry by default
• Secure credential storage

Related Documentation

• Search Engines Guide
• API Quickstart
• Configuration Guide
• Full Configuration Reference
• Troubleshooting
• Analytics Dashboard