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An enhanced implementation of the orchestrator-worker pattern from Anthropic’s paper, “How we built our multi-agent research system”, built on top of the bleeding-edge multi-agent framework swarms. Our implementation of this advanced research system leverages parallel execution, LLM-as-judge evaluation, and professional report generation with export capabilities. Repository: AdvancedResearch

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Installation

pip3 install -U advanced-research

# uv pip install -U advanced-research

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Environment Variables

# Exa Search API Key (Required for web search functionality)
EXA_API_KEY="your_exa_api_key_here"

# Anthropic API Key (For Claude models)
ANTHROPIC_API_KEY="your_anthropic_api_key_here"

# OpenAI API Key (For GPT models)  
OPENAI_API_KEY="your_openai_api_key_here"

# Worker Agent Configuration
WORKER_MODEL_NAME="gpt-4.1"
WORKER_MAX_TOKENS=8000

# Exa Search Configuration
EXA_SEARCH_NUM_RESULTS=2
EXA_SEARCH_MAX_CHARACTERS=100
Note: At minimum, you need EXA_API_KEY for web search functionality. For LLM functionality, you need either ANTHROPIC_API_KEY or OPENAI_API_KEY.

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Quick Start

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Basic Usage

from advanced_research import AdvancedResearch

# Initialize the research system
research_system = AdvancedResearch(
    name="AI Research Team",
    description="Specialized AI research system",
    max_loops=1,
)

# Run research and get results
result = research_system.run(
    "What are the latest developments in quantum computing?"
)
print(result)