Scoras¶
Intelligent Agent Framework with Complexity Scoring
Scoras is a powerful, intuitive framework for building intelligent agents with built-in complexity scoring. Inspired by PydanticAI and Langgraph but designed to be more accessible and comprehensive, Scoras provides everything you need to create sophisticated AI agents, RAG systems, and multi-agent workflows.
Key Features¶
- Integrated Complexity Scoring: Automatically measure and understand the complexity of your agent workflows
- Multi-Model Support: Work with OpenAI, Anthropic, Google Gemini, and other LLM providers
- Protocol Support: Native integration with MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols
- Intuitive API: Simple, expressive interface for creating agents and tools
- Advanced Graph-Based Workflows: Create sophisticated agent workflows with conditional branching
- Enhanced RAG Capabilities: Build powerful retrieval-augmented generation systems
- Structured Data Validation: Leverages Pydantic-style validation for robust data handling
- Comprehensive Tooling: Extensive tool framework for agent capabilities
Quick Example¶
import scoras as sc
# Create a simple agent
agent = sc.Agent(
model="openai:gpt-4o",
system_prompt="You are a helpful assistant."
)
# Run the agent
response = agent.run_sync("What is the capital of France?")
print(response)
# Check the complexity score
score = agent.get_complexity_score()
print(f"Complexity: {score['complexity_rating']} (Score: {score['total_score']})")
Understanding Complexity Scores¶
Scoras provides a unique complexity scoring system that helps you understand and manage the complexity of your agent workflows:
- Nodes: Basic processing units (1-1.5 points each)
- Edges: Connections between nodes (1.5-4 points each)
- Tools: Agent capabilities (1.4-3 points each)
- Conditions: Decision points (2.5 points each)
Complexity ratings: - Simple: Score < 10 - Moderate: Score 10-25 - Complex: Score 25-50 - Very Complex: Score 50-100 - Extremely Complex: Score > 100
License¶
Scoras is created by Anderson L. Amaral and is available under the MIT License.