deep-research-client 0.1.0

Wrapper for multiple deep research tools

deep-research-client

A simple Python wrapper for multiple deep research tools including OpenAI Deep Research, FutureHouse Falcon, and Perplexity AI.

Features

• 🔍 Multiple Providers: Support for OpenAI Deep Research, FutureHouse Falcon, and Perplexity AI
• 📚 Rich Output: Returns comprehensive markdown reports with citations
• 💾 Smart Caching: File-based caching to avoid expensive re-queries
• 🔧 Simple Configuration: Auto-detects providers from environment variables
• 📝 Library + CLI: Use as a Python library or command-line tool
• 📋 Advanced Templates: Support for both simple f-string and powerful Jinja2 templates
• 🏗️ Extensible: Easy to add new research providers

Installation

# Install with pip
pip install deep-research-client

# Or use uvx to run directly without installation
uvx deep-research-client research "What is CRISPR?"

# Or add to a uv project
uv add deep-research-client

# For development
git clone <repo-url>
cd deep-research-client
uv sync

Quick Start

Set up API Keys

# For OpenAI Deep Research
export OPENAI_API_KEY="your-openai-key"

# For FutureHouse Falcon
export FUTUREHOUSE_API_KEY="your-futurehouse-key"

# For Perplexity AI
export PERPLEXITY_API_KEY="your-perplexity-key"

# For Consensus AI (requires application approval)
export CONSENSUS_API_KEY="your-consensus-key"

Command Line Usage

# Basic research query
deep-research-client research "What is CRISPR gene editing?"

# Run without installation using uvx
uvx deep-research-client research "What is CRISPR gene editing?"

# Use specific provider and model
deep-research-client research "Explain quantum computing" --provider perplexity --model sonar-pro

# Save to file (citations included by default)
deep-research-client research "Machine learning trends 2024" --output report.md

# Save citations to separate file
deep-research-client research "AI trends 2024" --output report.md --separate-citations

# Use simple template with variables (f-string style)
deep-research-client research --template gene_research.md --var "gene=TP53" --var "organism=human"

# Use advanced Jinja2 template with conditionals
deep-research-client research --template gene_advanced.md.j2 \
  --var "gene=BRCA1" \
  --var "organism=human" \
  --var "detail_level=high"

# List available providers
deep-research-client providers

# List available models (with costs, speeds, and capabilities)
deep-research-client models
deep-research-client models --provider perplexity --detailed

# Cache management
deep-research-client list-cache   # Show cached files
deep-research-client clear-cache  # Remove all cache

Python Library Usage

from deep_research_client import DeepResearchClient

# Initialize client (auto-detects providers from env vars)
client = DeepResearchClient()

# Perform research with model selection
result = client.research("What are the latest developments in AI?", provider="perplexity", model="sonar-pro")

print(result.markdown)  # Full markdown report
print(f"Provider: {result.provider}")
print(f"Model: {result.model}")
print(f"Duration: {result.duration_seconds:.2f}s")
print(f"Citations: {len(result.citations)}")
print(f"Cached: {result.cached}")

Advanced Configuration

from deep_research_client import DeepResearchClient, ProviderConfig, CacheConfig

# Custom cache settings
cache_config = CacheConfig(
    enabled=True,
    directory="./my_cache"  # Custom cache location
)

# Custom provider settings
provider_configs = {
    "openai": ProviderConfig(
        name="openai",
        api_key="your-key",
        timeout=900,  # 15 minutes
        max_retries=5
    )
}

client = DeepResearchClient(
    cache_config=cache_config,
    provider_configs=provider_configs
)

Caching

The client uses intelligent file-based caching to avoid expensive re-queries:

• Location: Caches stored in ~/.deep_research_cache/ directory (user's home)
• Persistence: Cache files never expire (permanent until manually cleared)
• Human-Readable Names: Files named like openai-what-is-crispr-gene-a1b2c3d4.json
• Uniqueness: SHA256 hash suffix ensures no collisions
• Management:
  • deep-research-client clear-cache - Remove all cached files
  • deep-research-client list-cache - Show all cached files
  • --no-cache CLI flag to bypass cache for single query

Cache File Structure

~/.deep_research_cache/
├── openai-what-is-crispr-gene-a1b2c3d4.json
├── falcon-machine-learning-trends-f6e5d4c3.json
├── openai-quantum-computing-basics-b8f7e2a9.json
└── ...

Each cache file contains:

{
  "markdown": "# Research Report\n...",
  "citations": ["Citation 1", "Citation 2"],
  "provider": "openai",
  "query": "original query text"
}

Templates

Use template files with variable substitution for reusable research queries. The client supports both simple f-string templates and powerful Jinja2 templates with conditionals, loops, and filters.

Template Formats

F-String Templates (Simple)

Create a template file with {variable} placeholders:

# Gene Research Template

Please research the gene {gene} in {organism}, focusing on:

1. Function and molecular mechanisms
2. Disease associations
3. Expression patterns in {tissue} tissue
4. Recent discoveries since {year}

Gene: {gene}
Organism: {organism}
Tissue: {tissue}
Year: {year}

Jinja2 Templates (Advanced)

For more complex templates with conditionals, loops, and logic, use Jinja2 syntax with {{variable}} and save with .j2 extension:

# Gene Research: {{gene}}

Research the {{gene}} gene in {{organism}}.

{% if detail_level == "high" %}
Provide comprehensive information including:
- Detailed molecular mechanisms
- All known disease associations
- Complete expression atlas
- Evolutionary conservation
{% else %}
Provide a concise overview covering:
- Primary function
- Major disease links
- Key expression patterns
{% endif %}

Focus areas:
{% for topic in topics %}
- {{topic|capitalize}}
{% endfor %}

{% if year %}
Emphasize discoveries from {{year}} onwards.
{% endif %}

Format Detection

Templates are automatically detected as Jinja2 based on:

1. File extension: .j2, .jinja, .jinja2 (e.g., template.md.j2)
2. Frontmatter: YAML frontmatter with format: jinja in .md files
3. Default: F-string format for backward compatibility

Example with frontmatter:

---
format: jinja
description: Advanced gene research
---
Research {{gene}}{% if organism %} in {{organism}}{% endif %}.

Using Templates

# Simple f-string template
deep-research-client research \
  --template gene_research.md \
  --var "gene=TP53" \
  --var "organism=human" \
  --var "tissue=brain" \
  --var "year=2020"

# Jinja2 template with conditionals
deep-research-client research \
  --template gene_advanced.md.j2 \
  --var "gene=BRCA1" \
  --var "organism=human" \
  --var "detail_level=high"

# With uvx (no installation)
uvx deep-research-client research \
  --template gene_research.md \
  --var "gene=TP53" \
  --var "organism=human"

# Missing variables will show helpful error
deep-research-client research --template gene_research.md
# Error: Template requires variables: gene, organism, tissue, year
# Use --var key=value for each variable

Template Examples

The repository includes example templates:

• templates/gene_family.md - Simple f-string template for gene families
• templates/gene_jinja.md.j2 - Jinja2 template with conditionals
• templates/gene_frontmatter.md - Markdown with frontmatter-based format detection

Python API with Templates

from deep_research_client import DeepResearchClient
from deep_research_client.processing import TemplateProcessor
from pathlib import Path

# Initialize processor (auto-detects format)
template_processor = TemplateProcessor()

# Simple f-string template
query = template_processor.render_template(
    Path("gene_research.md"),
    {"gene": "TP53", "organism": "human", "tissue": "brain", "year": "2020"}
)

# Jinja2 template with advanced features
query = template_processor.render_template(
    Path("gene_advanced.md.j2"),
    {
        "gene": "BRCA1",
        "organism": "human",
        "detail_level": "high",
        "topics": ["function", "mutations", "expression"],
        "year": "2020"
    }
)

# Process template with metadata (includes format info)
rendered_query, metadata = template_processor.process_template(
    Path("gene_research.md.j2"),
    {"gene": "TP53", "organism": "human"}
)
print(f"Format used: {metadata['template_format']}")  # 'jinja' or 'fstring'

# Use with client
client = DeepResearchClient()
result = client.research(query)

Jinja2 Features

Jinja2 templates support powerful features:

• Conditionals: {% if condition %}...{% endif %}
• Loops: {% for item in list %}...{% endfor %}
• Filters: {{gene|upper}}, {{organism|capitalize}}
• Default values: {{year|default('2024')}}
• Comments: {# This is a comment #}

See docs/templates.md for comprehensive template documentation.

Output Format

The client outputs structured markdown with YAML frontmatter containing metadata:

---
provider: perplexity
model: sonar-pro
cached: false
start_time: '2025-10-18T17:43:49.437056'
end_time: '2025-10-18T17:44:08.922200'
duration_seconds: 19.49
template_file: examples/gene_research.md.j2  # If template used
template_format: jinja                        # Template format: 'jinja' or 'fstring'
template_variables:                           # If template used
  gene: TP53
  organism: human
  detail_level: high
provider_config:
  timeout: 600
  max_retries: 3
citation_count: 18
---

## Question

What is machine learning?

## Output

**Machine learning** is a branch of artificial intelligence...

## Citations

1. https://www.ibm.com/topics/machine-learning
2. https://www.coursera.org/articles/what-is-machine-learning
...

Citation Handling

• Default: Citations included at end of markdown output
• Separate file: Use --separate-citations to save citations to .citations.md file
• Library usage: Access citations via result.citations list

Model Discovery & Introspection

The client includes comprehensive model discovery capabilities to help you find the right model for your needs.

Listing Available Models

# List all available models across all providers
deep-research-client models

# Show models for a specific provider
deep-research-client models --provider openai
deep-research-client models --provider perplexity

# Filter by cost level
deep-research-client models --cost low      # Budget-friendly options
deep-research-client models --cost medium   # Balanced options
deep-research-client models --cost high     # Premium options
deep-research-client models --cost very_high  # Most expensive/comprehensive

# Filter by capability
deep-research-client models --capability web_search
deep-research-client models --capability academic_search
deep-research-client models --capability scientific_literature

# Show detailed information including pricing, use cases, and limitations
deep-research-client models --detailed

# Combine filters
deep-research-client models --provider perplexity --cost low --detailed

Model Aliases

Most models support convenient short aliases:

# These are equivalent
deep-research-client research "AI trends" --provider openai --model o3-deep-research-2025-06-26
deep-research-client research "AI trends" --provider openai --model o3
deep-research-client research "AI trends" --provider openai --model o3-deep

# Perplexity aliases
--model sonar-deep-research  # Can use: deep, deep-research, sdr
--model sonar-pro            # Can use: pro, sp
--model sonar                # Can use: basic, fast, s

# Using uvx with aliases
uvx deep-research-client research "AI" --provider openai --model o3

Model Selection

Use --model to override the default:

# Use faster Perplexity model
deep-research-client research "AI trends" --provider perplexity --model sonar-pro

# Use default deep research model (comprehensive but slower)
deep-research-client research "AI trends" --provider perplexity  # Uses sonar-deep-research

# Override OpenAI model using alias
deep-research-client research "AI trends" --provider openai --model o4-mini

Available Models by Provider

Provider	Model	Aliases	Cost	Speed	Context	Key Features
OpenAI	o3-deep-research-2025-06-26	o3, o3-deep, o3dr	💰💰💰💰	🐢	128K	Most comprehensive, web search, code analysis
OpenAI	o4-mini-deep-research-2025-06-26	o4m, o4-mini, mini	💰💰	⏳	128K	Balanced speed/quality
Perplexity	sonar-deep-research	deep, deep-research, sdr	💰💰💰	🐌	200K	Comprehensive with real-time data
Perplexity	sonar-pro	pro, sp	💰💰	⏳	200K	Fast with good quality
Perplexity	sonar	basic, fast, s	💰	⚡	100K	Fastest, budget-friendly
Falcon	FutureHouse Falcon API	falcon, fh, science	💰💰💰	🐌	-	Scientific literature focus
Consensus	Consensus Academic Search	consensus, academic, papers, c	💰	⚡	-	Peer-reviewed papers only

Cost Legend: 💰 = Low, 💰💰 = Medium, 💰💰💰 = High, 💰💰💰💰 = Very High Speed Legend: ⚡ = Fast, ⏳ = Medium, 🐌 = Slow, 🐢 = Very Slow

Model Capabilities

Each model has different capabilities:

Capability	Models
Web Search	OpenAI (o3, o4-mini), Perplexity (all)
Academic Search	Falcon, Consensus
Scientific Literature	Falcon
Real-time Data	OpenAI (all), Perplexity (all)
Citation Tracking	All providers
Code Interpretation	OpenAI (o3)

Python API for Model Discovery

from deep_research_client.model_cards import (
    get_provider_model_cards,
    list_all_models,
    find_models_by_cost,
    find_models_by_capability,
    resolve_model_alias,
    CostLevel,
    ModelCapability
)

# Get all models for a provider
openai_cards = get_provider_model_cards("openai")
models = openai_cards.list_models()
print(models)  # ['o3-deep-research-2025-06-26', 'o4-mini-deep-research-2025-06-26']

# Get detailed model information
card = openai_cards.get_model_card("o3-deep-research-2025-06-26")
print(f"Cost: {card.cost_level}")
print(f"Speed: {card.time_estimate}")
print(f"Capabilities: {card.capabilities}")
print(f"Context Window: {card.context_window}")
print(f"Use Cases: {card.use_cases}")
print(f"Limitations: {card.limitations}")

# Find models across all providers by criteria
cheap_models = find_models_by_cost(CostLevel.LOW)
# Returns: {'perplexity': ['sonar'], 'consensus': ['Consensus Academic Search']}

web_search_models = find_models_by_capability(ModelCapability.WEB_SEARCH)
# Returns: {'openai': ['o3-deep-research-2025-06-26', ...], 'perplexity': [...]}

# Resolve aliases to full model names
full_name = resolve_model_alias("openai", "o3")
print(full_name)  # 'o3-deep-research-2025-06-26'

# List all models across all providers
all_models = list_all_models()
# Returns: {'openai': [...], 'perplexity': [...], 'falcon': [...], ...}

# Get models by speed
fast_models = openai_cards.get_models_by_time(TimeEstimate.FAST)

Model Selection Guidelines

For comprehensive research:

• OpenAI o3 (most thorough, expensive, slow)
• Perplexity sonar-deep-research (thorough with real-time data)

For balanced performance:

• OpenAI o4-mini (good quality, reasonable cost)
• Perplexity sonar-pro (fast with good coverage)

For quick lookups:

• Perplexity sonar (fastest, cheapest)

For academic/scientific research:

• Falcon (scientific literature focused)
• Consensus (peer-reviewed papers only)

Budget considerations:

# Most expensive but most comprehensive
deep-research-client research "complex topic" --provider openai --model o3

# Good balance of cost and quality
deep-research-client research "complex topic" --provider perplexity --model pro

# Cheapest option for quick answers
deep-research-client research "simple question" --provider perplexity --model sonar

Supported Providers

Provider	Environment Variable	Model/Service	Strengths
OpenAI	OPENAI_API_KEY	o3-deep-research-2025-06-26	Deep research, comprehensive reports
Falcon	FUTUREHOUSE_API_KEY	FutureHouse Falcon API	Scientific literature focus
Perplexity	PERPLEXITY_API_KEY	sonar-deep-research	Real-time web search, recent sources
Consensus	CONSENSUS_API_KEY	Consensus Academic Search	Peer-reviewed academic papers, evidence-based research

Development

Essential Commands

# Run tests
just test

# Type checking
just mypy

# Format code
just format

# Install dependencies
uv sync

# Run CLI locally
uv run deep-research-client --help

Adding New Providers

1. Create a new provider class in src/deep_research_client/providers/
2. Inherit from ResearchProvider
3. Implement the research() method
4. Register in client.py

Example:

from . import ResearchProvider
from ..models import ResearchResult

class NewProvider(ResearchProvider):
    async def research(self, query: str) -> ResearchResult:
        # Your implementation
        return ResearchResult(...)

Repository Structure

• docs/ - mkdocs documentation
• src/deep_research_client/ - main package
  • client.py - main client class
  • models.py - Pydantic models
  • cache.py - caching implementation
  • providers/ - research providers
• tests/ - test suite

Potential Future Providers

The following providers were researched but not yet implemented:

Research-Focused Providers

• Semantic Scholar API - Free access to 200+ million academic papers with comprehensive metadata
• Elicit AI - AI research assistant focused on answering research questions
• Scite.ai - Citation-based research with smart citations and paper analysis

General AI Providers with Research Capabilities

• Anthropic Claude - Has web search API but lacks dedicated deep research mode
• Google Gemini - Deep Research feature exists but requires allowlist API access
• Exa (Metaphor) - AI-powered search API designed for research workflows

Specialized Tools

• Qatalog - Enterprise knowledge search and research
• Azure AI Search - Microsoft's cognitive search capabilities
• IBM Watson Discovery - Enterprise document analysis and research

These providers could be added based on user demand and API availability. Contributions welcome!

Claude Code Skill

This repository includes a Claude Code skill that makes it easy to use deep-research-client directly from Claude Code.

Installation

To use the skill:

# Copy to your local Claude skills directory
cp -r .claude/skills/run-deep-research ~/.claude/skills/

# Or use it project-wide (already available in this repo)
# Claude Code will auto-detect skills in .claude/skills/

Features

The skill provides:

• Guided research workflow: Automatically asks about speed vs depth preferences
• Provider selection: Helps choose the right provider and model for your needs
• Template support: Easy access to research templates for common patterns
• Smart defaults: Leverages caching and best practices automatically

Usage with Claude Code

Once installed, simply ask Claude to research topics:

"Research the latest developments in CRISPR gene editing"
"Do a quick overview of quantum computing (fast approach)"
"I need a comprehensive analysis of TP53 gene in humans"

Claude will automatically:

1. Ask whether you want a fast/light or comprehensive/slow approach
2. Select the appropriate provider and model
3. Execute the research with proper caching
4. Save results with citations and metadata

See .claude/skills/README.md for more details.

License

BSD-3-Clause