A clean, modular implementation using Claude Agent SDK for deep research, data science workflows, and benchmark evaluations.

• Deep Research Pipeline: Multi-phase research on any topic with web search
• Data Science Workflows: Exploratory analysis, statistical analysis, and ML modeling
• GAIA Benchmark Evaluation: Evaluate Claude agents on the GAIA dataset
• Hydra Configuration: Clean configuration management with YAML files
• Rich Console Output: Beautiful progress tracking and logging
• Async Execution: Efficient concurrent task processing

cd agent1 uv venv uv pip install -e .

Research a topic:

python examples/dr.py research.topic="Impact of AI on healthcare"

Different research depths:

python examples/dr.py research.topic="Climate change" research=quick python examples/dr.py research.topic="Quantum computing" research=exhaustive

Save output to file:

python examples/dr.py research.topic="AI Ethics" research.output_file=report.md

Analyze data:

python examples/ds.py data_science.task="Analyze sales trends" data_science.data_path=sales.csv

Build a model:

python examples/ds.py data_science.modeling.task="Predict customer churn" data_science.data_path=customers.csv

Run GAIA evaluation on validation set:

python examples/run_gaia.py gaia.split=validation gaia.max_tasks=5

Full test set evaluation:

python examples/run_gaia.py gaia.split=test

agent1/ ├── examples/ │ ├── dr.py # Deep research CLI │ ├── ds.py # Data science CLI │ └── run_gaia.py # GAIA evaluation script ├── src/ │ ├── configs/ │ │ ├── deep_research.yaml # Research configuration │ │ ├── data_scientist.yaml # Data science configuration │ │ └── gaia.yaml # GAIA benchmark configuration │ ├── claude.py # Claude agent executor │ ├── pipelines.py # Pipeline implementations │ ├── logger.py # Rich console logger │ └── gaia_utils.py # GAIA dataset utilities └── data/ └── GAIA/ # GAIA dataset (add manually) 

All configurations use Hydra and are stored in src/configs/. Key options:

• model.name: Claude model to use (default: claude-sonnet-4-5-20250929)
• model.temperature: Sampling temperature
• model.max_tokens: Maximum tokens

• research.topic: Research topic (required)
• research.depth: quick, standard, comprehensive, exhaustive
• research.output_file: Optional output file path

• gaia.split: validation or test
• gaia.max_tasks: Maximum tasks to evaluate
• gaia.batch_size: Concurrent batch size
• gaia.results_path: Output JSONL path

1. Download GAIA dataset to data/GAIA/:

   • 2023_validation.json - Validation set with ground truth
   • 2023_test.json - Test set without ground truth

2. Run evaluation:

python examples/run_gaia.py gaia.split=validation

• Smart Resume: Automatically skips completed tasks
• Batch Processing: Concurrent execution with configurable batch size
• Comprehensive Metrics: Accuracy calculation and detailed reports
• Error Recovery: Graceful error handling with detailed logging
• Result Persistence: JSONL format with metadata and costs

Results saved in JSONL:

{ "task_id": "test_001", "question": "What is 2 + 2?", "prediction": "4", "true_answer": "4", "tools_used": ["WebSearch"], "num_turns": 3, "cost_usd": 0.002, "duration_ms": 5432 }

Test GAIA setup:

python test_gaia_setup.py

Executes single agents with specified tools and configurations:

from src.claude import create_agent_executor executor = create_agent_executor() result = await executor.execute_agent( prompt="Research quantum computing", agent_type="research", allowed_tools=["WebSearch", "WebFetch"] )

Orchestrates multi-phase pipelines:

from src.claude import create_pipeline_executor pipeline = create_pipeline_executor() result = await pipeline.execute_pipeline( phases=[...], initial_context="Topic: AI" )

High-level interfaces for specific workflows:

from src.pipelines import DeepResearchPipeline, DataSciencePipeline # Research research = DeepResearchPipeline() result = await research.research("AI ethics", depth="comprehensive") # Data science ds = DataSciencePipeline() result = await ds.analyze_data(data_path="data.csv", analysis_type="exploratory")

1. Create configuration in src/configs/
2. Extend BasePipeline in src/pipelines.py
3. Add CLI script in examples/

Modify allowed_tools in agent configurations:

• Research: WebSearch, WebFetch, Read, Write
• Analysis: Read, Write, Bash, Grep, Glob
• Coding: Read, Write, Edit, Bash

• Import Errors: Ensure dependencies installed with uv pip install -e .
• API Errors: Check Claude API key is set
• Dataset Not Found: Download GAIA dataset to data/GAIA/
• Out of Memory: Reduce batch_size in configuration

MIT