A multi-agent system built with DSPy that coordinates research and writing agents to produce comprehensive articles on any topic.

• Multi-Agent Architecture: Coordinated research and writing agents working together
• Web Search Integration: Automated research using Tavily search API
• DSPy Framework: Leveraging DSPy for structured AI workflows
• FastAPI Integration: REST API for production deployment
• Async Support: Concurrent processing for better performance
• Type Safety: Full type hints and mypy validation
• Comprehensive error handling and resource management

• Python 3.12+
• OpenAI API key
• Tavily API key (for web search functionality) - Get one at https://tavily.com

1. Clone the repository:

git clone <repository-url>
cd dspy-multi-agent

2. Install dependencies using uv:

uv sync --dev

3. Set up environment variables:

cp .env.example .env
# Edit .env with your API keys

uv run python src/main.py

Start the server:

python start_api.py
# or
uv run uvicorn src.api.main:app --reload --host 0.0.0.0 --port 8000

Run the demo via API (2 methods):

Method 1 - Python client (recommended):

uv run python test/demo_client.py

Method 2 - Direct curl command:

curl -X POST http://localhost:8000/agent/demo

FastAPI Server

Method 1 - Direct command:

uv run uvicorn src.api.main:app --reload --host 0.0.0.0 --port 8000

Method 2 - Startup script (recommended):

python start_api.py

The API will be available at:

• Main API: http://localhost:8000
• Interactive docs: http://localhost:8000/docs
• Alternative docs: http://localhost:8000/redoc

• MultiAgentCoordinator: Orchestrates the multi-agent workflow
• ResearchAgent: Conducts web research and information gathering
• WriterAgent: Creates and polishes articles based on research
• WebSearchTool: Handles web search API integration

1. Task Planning: Coordinator analyzes the request and creates execution plan
2. Research Phase: ResearchAgent searches for information and extracts key findings
3. Writing Phase: WriterAgent creates draft article and polishes it
4. Result Compilation: Final article with sources and summary

Environment variables in .env:

OPENAI_API_KEY=your_openai_api_key_here
TAVILY_API_KEY=your_tavily_api_key_here  # Required for web search
MODEL_NAME=gpt-4o-mini
TEMPERATURE=0.7
MAX_TOKENS=2000
ASYNC_WORKERS=4
API_HOST=0.0.0.0
API_PORT=8000
LOG_LEVEL=INFO

Run the built-in demo request (same as src/main.py).

No request body required.

Response:

{
  "status": "success",
  "topic": "Write a comprehensive article about the impact of artificial intelligence on modern healthcare",
  "article": "Full article content...",
  "summary": "Article summary...",
  "sources": ["source1", "source2", "..."],
  "key_points": ["point1", "point2", "..."],
  "processing_time": 47.2,
  "agent_id": "uuid"
}

Process a custom research and writing request.

Request Body:

{
  "query": "Write about artificial intelligence in healthcare",
  "target_audience": "healthcare professionals",
  "max_sources": 10
}

Response:

{
  "status": "success",
  "topic": "artificial intelligence in healthcare",
  "article": "Full article content...",
  "summary": "Article summary...",
  "sources": ["source1", "source2", "..."],
  "key_points": ["point1", "point2", "..."],
  "processing_time": 15.2,
  "agent_id": "uuid"
}

Health check endpoint.

Response:

{
  "status": "healthy",
  "timestamp": "2025-06-23T14:35:10.956221",
  "version": "1.0.0"
}

Get agent system status and configuration.

Response:

{
  "coordinator_ready": true,
  "model_configured": true,
  "async_workers": 4
}

Format code:

black .

Type checking:

mypy src/

Run tests:

uv run pytest

Install pre-commit hooks:

pre-commit install
pre-commit run --all-files

# Python client (recommended)
uv run python test/demo_client.py

# Or curl command
curl -X POST "http://localhost:8000/agent/demo"

curl -X GET "http://localhost:8000/health"

curl -X POST "http://localhost:8000/agent/process" \
     -H "Content-Type: application/json" \
     -d '{
       "query": "Write about AI in healthcare",
       "target_audience": "healthcare professionals",
       "max_sources": 5
     }'

curl -X GET "http://localhost:8000/agents/status"

1. Create agent class inheriting from dspy.Module
2. Define agent signature in src/signatures/agent_signatures.py
3. Implement forward method with your agent logic
4. Register agent in coordinator

Replace the placeholder search API in WebSearchTool.search() with your preferred search service:

• You.com Search API
• Serper.dev
• Bing Search API
• Custom search implementation

Agents can be extended with:

• Vector databases for long-term memory
• Session storage for conversation context
• Database integration for result persistence

FROM python:3.12-slim

WORKDIR /app
COPY . .

RUN pip install uv
RUN uv sync --no-dev

EXPOSE 8000
CMD ["uvicorn", "src.api.main:app", "--host", "0.0.0.0", "--port", "8000"]

Ensure all required environment variables are set in production:

export OPENAI_API_KEY="your-api-key"
export TAVILY_API_KEY="your-tavily-key"  # Required for web search
export MODEL_NAME="gpt-4o-mini"
export API_HOST="0.0.0.0"
export API_PORT="8000"

Compare multiple AI models with comprehensive reporting:

# Run with default configuration (models from model_comparison_config.json)
uv run python tests/models_batch_run/run_model_comparison_with_report.py

# Use custom configuration file
uv run python tests/models_batch_run/run_model_comparison_with_report.py --config custom_config.json

# Override specific parameters
uv run python tests/models_batch_run/run_model_comparison_with_report.py --models gpt-4o o3-mini --query "Explain quantum computing"

Models are configured in model_comparison_config.json:

{
  "models": ["gpt-4o-mini"],  // Models to compare
  "default_query": "Write a comprehensive article about the impact of artificial intelligence on modern healthcare",
  "default_audience": "general audience",
  "default_sources": 5
}

Model comparison tests generate comprehensive HTML reports in the reports/ directory including:

• Performance metrics and visualizations (performance charts, heatmaps, speedup matrices)
• Token usage and speed comparisons
• Side-by-side output comparisons with similarity metrics
• Full LLM outputs for each model (actual responses, not placeholders)
• Detailed phase timing breakdowns (planning, research, writing)
• Cross-model similarity analysis with key points extraction

Report structure:

reports/
└── model_comparison_YYYYMMDD_HHMMSS/
    ├── report_YYYYMMDD_HHMMSS.html     # Main comprehensive report
    ├── llm_outputs/                     # Raw model outputs
    ├── visualizations/                  # Generated charts and graphs
    └── data/                           # JSON data files

• ✅ Fixed all import errors and path resolution issues
• ✅ Added comprehensive type annotations (mypy compliant)
• ✅ Implemented proper async resource management
• ✅ Fixed FastAPI server startup issues
• ✅ Added startup script for easier development
• ✅ Added model comparison framework with comprehensive reporting
• ✅ External configuration file support for model testing
• ✅ Fixed LLM output capture to save actual model responses

GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 (see LICENSE)

[Add contribution guidelines here]

The project includes tools for visualizing test results:

# Generate visualizations from test results
uv run python tests/tools/visualize_results.py

# Compare specific model results
uv run python tests/tools/visualize_comparison_results.py

• API Testing Guide: docs/API Testing Examples.md - Comprehensive testing examples and curl commands
• Model Comparison Guide: docs/model_comparison.md - Detailed guide for model testing
• Test Organization: docs/test_organization.md - Test structure and usage
• Visualization Guide: docs/visualization_guide.md - Using visualization tools
• Report Generation Guide: docs/comprehensive_report_guide.md - Understanding test reports
• Interactive API Docs: http://localhost:8000/docs (when server is running)
• Alternative API Docs: http://localhost:8000/redoc (when server is running)